Top 10 ML Books

The Why, What, and Which of learning ML from books.

Why read books on machine learning?

It is allegedly easy to get started with training neural nets. . ., giving the (false) impression that this stuff is plug and play but, Backprop + SGD does not magically make your network work. Batch norm does not magically make it converge faster. RNNs don’t magically let you “plug in” text, Just because you can formulate your problem as RL doesn’t mean you should. If you insist on using the technology without understanding how it works you are likely to fail. -Andrej Karapathy Former Director of AI at Tesla.

Understanding the importance of knowing the theory behind Machine Learning

Theory might not sound as the most intuitive way of learning ML or coding for that matter, but what seperates ML from other concepts of Computer Science and traditional analytics is its extensive use of rigorous thoery.Solving any ML problem whether it be optimizing for time-sapce consumption of algorithms to representations of data in a machine comprehensible format for classification or regression is essentially a complex math problem solved using code.

Even if one could get away with not knowing much about the behemoth inside the black box algorithms, they have greater chances of getting stuck on a bottleneck, or the algorithms performing poorly or having unintended consequences like bias, which can be detrimental for the applications, hence its suffice to say that being aware of theory and underlying concepts of ML can not only give someone an extra edge but help them understand the possibilities and limitations of algorithms, saving them from mishaps.

Benefits of reading books for learning Machine Learning

To summarize the above discussed reasons along with some other benifits are mentioned in brief:

• Offers Foundational Understanding: ML books give readers a methodical way to comprehend theories and concepts.
• Structured Learning: Books are more successful than haphazard online learning since they include topics ranging from fundamental to sophisticated.
• Extensive Coverage: Books on machine learning cover a wide range of subjects, such as methodologies, theoretical underpinnings, algorithms, and real-world applications.
• Depth of Knowledge: ML books include comprehensive justifications, mathematical deductions, and illustrations that improve comprehension and problem-solving abilities.
• Credibility and Quality: To ensure accuracy and dependability, ML books are typically subjected to peer review and editing.
• Reference Material: Books on machine learning are a great way to review ideas, formulas, and procedures.
• Enhancement of Skills: Knowledge of ML algorithms can help with problem-solving and critical thinking.
• Authoritative Insights: Learning gains context and richness from the insights of authors.
• ML Books for Advanced Themes: Essential for staying updated with latest advancements and research, providing a cutting edge advantage to the readers.
• Long-term Investment: ML books offer pertinent and practical knowledge for job advancement, making them a long-term investment in education.

Books better than Blogs/Repos/Courses? Aren't books outdated?

Blogs and repositories are a great place to learn and practice the concepts of ML, there is no doubt to that,infact in the breakneck speed of changes happening in ML, one could conclude that these resources are rather the better option to stay up to date with recent advancements.

But fact of the matter is, like it or not, Books tend to have a sense of sanctity attatched to them, hence writers ake sure that information in it credible and reliable, they also undergo rounds of editing and publishers who make sure that the content is useful, and well structured, they give author the incentive to put out quality content

Books often focus on (though not limited to) mathematical rigor behind the theory of ML while they are often supplemented by Repositories for practicing the theory in a hands on manner, Courses on the other hand can fall victim to being watched like a Netflix binge session where the viewers skim through the videos for hours without any practice, leaving the viewers deprived of both theory and practical applications,not to mention the loss of expensive fees.

The deliberately slow process of publishing a Book makes sure that their topic is actually concrete,or has peer-reviewed quality check of the arguments made by the author for their point of view, and makes sure that the topic is not just another hyped-up fad, this combined with the fact that we have to pay a certain amount of money to get these books and they remain a physical reminder or a proof for our past commitments and can serve as motivation us to learn.

Hence choosing the right method to learn and learning from various mediums of ML based content is neccesary for a comprehensive understanding.

What is the correct way of learning ML from the books?

“I learned very early the difference between knowing the name of something and knowing something.” - Richard Feynman

There is no “one size fits all” way of learning ML, it very much depends on what objectives we aim to achieve at the end of it, how much of the prerequisistes are we aware about and how much time and effort we are willing to dedicate for learning, but irrespective to that, there are some basic guidelines which can be used to devise a stratergy for learning ML :

Comprehend the Core: Learning the foundations and cores of topics

• Pick the right books: Choose books that Match your current knowledge level and learning goals, are written by esteemed authors from academia and industry experts and publications.
• Define your objectives Clearly : Put an emphasis on practical skills and conceptual understanding along with the intuition of theory.
• Adopt a methodical approach: Engage in active reading, take notes, practice applying algorithms, and working through examples and exercises.
• Use interactive technologies, practical projects, and online courses to augment learning.
• Get involved in the community: Participate in forums, create study groups, and go to conferences, workshops, and meetings for machine learning in your community.
• Regularly review and consider: Review notes frequently, go back over important chapters, and create blog entries.
• Stay updated: Read recent research papers and articles, and follow ML experts on social media and blogs.

Learning the Ad-hoc way:

• Once we get atleat intuition of the foundation of a topic, one can choose to (and more often should) mix it up with implementations ,practical applications and projects.
• Learning everything is impossible, Waiting for understanding an algorithm to execute is a futile bet, we truly understand something only when we execute it and realize its practical applications.
• During Execution or project we are likely to hit roadblocks, which could be in a range between not knowing a topic at all, to having confusions regarding its functioning,the Ad-hoc method of learning can be a practical approach to learn ML, where we choose topics as their need arives in project and implementations and stick to reading parts relevant to that topic and skipping the rigorous theory or irrelevant parts and if neccessary revise and skim important pre-requisistes of the topic.
• If we hit a major roadblock or find a particular topic or are confused regarding a topic, we can go back to learn about it using the comprehensive learning method .

Which Books to read for ML ? Criteria for selecting top books as per topics.

Criterias used for selecting top books for learning ML

1. Author skills: Ensuring that books are authored by recognized writers possessing pertinent skills.
2. Reputation and Reviews : Examining reviews for relevancy and quality from reliable sources.
3. Content Coverage: Selecting books that thoroughly cover subjects while striking a solid balance between theory and real-world examples.
4. Clarity and Accessibility : Choosing well-written and reader-friendly literature.
5. Accuracy and Relevance of value: Checking the validity and accuracy of information in light of recent developments, to see if its still relevant.
6. Practicality: Preferring texts that include activities, real-world applications, and practical insights.
7. Pedagogical Approach: Evaluating the learning tools and instructional methods in the book to improve comprehension.
8. Supporting Resources: Looking for additional materials such as code samples, databases, or web resources.

Top Books recommended for every stage of ML:

Priming the Primers : Mathematics for Machine Learning

"People think machine learning is magic. It's not. It's just a bunch of linear algebra and statistics with some marketing behind it.” -Josh Stammer (Creator of StatQuest ML explainer videos)

Learning mathematics is an important step to understand the working of ML algorithms, as many of them use linear algebra,optimization,probability and statistics to model the data and perform tasks on unseen data. Although one can get lost in the ocean of mathematics based content which are either too rigorous or too shallow for our needs

Here are top 5 books to learn Mathematics for Machine Learning :

1.Mathematics for Machine Learning - by Deisenroth,Faisal,Ong (Cambridge University Press) (free pdf )

This book strikes the delicate balanace of comprehensive explaination of the intuitions of maths behind ML without getting into too much rigourous details, doesnt require much prerequisites except high school level maths and is good foundation for learning ML or revising concepts of maths.

Advantages:

• Comprehensive coverage of mathematical foundations for machine learning.
• Structured learning path from basic to advanced concepts,covers a wide range of topics including linear algebra, probability theory, calculus, and optimization
• Practical applications of mathematical concepts in machine learning algorithms.
• Accessible writing style with intuitive explanations and visual aids.
• Supplementary materials like online resources and exercises for self-study.

Disadvantages:

• Depth vs. Breadth: The book may not cover all advanced topics.
• Mathematical Prerequisites: Requires basic understanding of high school mathematics.
• Application Focus: Emphasizes mathematical theory over practical implementation.
• Exercises Complexity: Can be challenging and require significant effort to solve.

2. Foundations of Machine Learning -by Mohri et.al. ,2nd edition (MIT Press)

This book is written by professors of of NYU and researchers at Google, this book is more covers more depth than the MML book mentioned above, it explains mathematics with more rigour and goes on to explore advanced topics like Support Vector Machines , kernel based models and Entropy based models, it can serve as good transition for learning ML theory for those who have the descent knowledge and fluency in high school mathematics, it explains the concepts in a detailed manner by giving a peek into the derivations of algorithms mentioned in it.

Advantages:

• A thorough theoretical basis in machine learning.

• A coherent and well-organized presentation.
• Discussion of complex subjects such as large-margin classifiers, kernel techniques, and online learning.
• Places a strong emphasis on mathematical rigor in order to comprehend machine learning algorithms deeply.
• A range of tasks to improve problem-solving abilities and reinforce topics.
• Content that has been updated to reflect current developments in the field.

Drawbacks:

• Learning curve might be challenging for individuals with little or no mathematical background.

• There aren't many real-world examples or code implementations.

• Presentation style and language are dense and technical.
• Not suitable for beginners as it requires a foundation in machine learning and maths.

3.The Cartoon Guide to Statistics (pdf)

The Cartoon Guide to Statistics covers concepts of Statistics and Probability in a illustrative manner these concepts include ,The Central Limit Theorem, probability in gambling and medicine, random variables, Bernoulli trails, hypothesis testing, confidence interval calculation, and many other key concepts, its a good book to get clear understanding of basic statistical concepts and can be a supplimentary or prequel to other rigorous books on statistics and ml.

Advantages:

• Engaging and Fun: Uses cartoons and humor to demystify statistical concepts.
• Simplified Explanations: Breaks down complex statistical ideas into simple explanations.
• Visual Learning: Uses extensive use of cartoons and visual aids for clearer understanding.
• Broad Coverage of Basics: Covers probability, distributions, hypothesis testing, and regression.
• Accessible to All Ages: Suitable for high school students, college undergraduates, and adults.
• Supplementary Resource: Serves as a supplementary resource for traditional statistics textbooks.

Disadvantages:

• Lack of Depth: Does not delve deeply into any particular area.
• Not Comprehensive: Some key statistical concepts and advanced topics may be oversimplified or omitted.
• Limited Practical Applications: Few real-world examples and practical applications.
• Basic Mathematical Explanations: Provides basic mathematical explanations, not sufficient for detailed mathematical understanding.

4.Introduction to Statistical Learning with application in R - by Trevor Hastie (Springer Texts) (pdf)

Intorduction to Statistical Learning with application in R is an condensed version of a previous book by Trevor Hastie called “Elements of Statitical Learning” published in 2001 which is considered as one of the pioneering books on foundations of Machine Learning. ISLR is a simplified version of ESL with less mathematical rigor, to make it convinient to understand and get an intuition for people who do not have a thorough mathematics background. It has practical excersises and examples given, which involve the use of R. In 2023 an official new edition of this book published which had the applications explained using Python .

Benefits:

• Beginner-friendly writing style that is easy to understand.

• Several illustrations and examples to aid with understanding.
• A practical emphasis on using statistical learning techniques in real-world situations.
• Provides R and Python code to put the suggested techniques into practice.
• Thorough discussion of all the important subjects in statistics education.
• A healthy mix of theory and application.
• Extra materials accessible on the book's webpage.

Drawbacks:

• Inadequate knowledge of mathematical theory, not enough for thorough knowledge of maths.

• Slight emphasis on cutting-edge subjects and current advancements in machine learning.
• Presupposes a fundamental understanding of probability, statistics, and linear algebra.
• R is mostly used for exercises and examples, the latest edition in Python isnt discussed in forums as much as R because of it being a recent release,because of which readers can get stuck on solutions and left without legitimate communities to discuss about them.
• A scant discussion of deep learning methods.

5.Probabilistic Machine Learning - a book series by Kevin Murphy (MIT Press)

Book written by Kevin Murphy, a professor in University of British Columbia, the book is written in 2 parts 1st being and introductions to ML with a particular focus on mathematics behind probabilistic approaches in ML and also a thorough discussion on concepts of Linear Algebra that are used in the algorithms.While the second version came out in 2023 which covered topics like RL topics like Monte Carlo Methods , deep neural networks and Causal ML and others. The math pre-requisistes for these are also covered in the early chapters of the book , making it an wholesome book for with both depth and width of topics.

Advantages and Disadvantages of the book are listed below:

Advantages:

• Comprehensive treatment of machine learning from a probabilistic perspective.
• Includes Bayesian networks, graphical models, variational inference, and Markov chain Monte Carlo methods.
• Includes both supervised and unsupervised learning, and advanced topics like non-parametric methods, sequential data, and approximate inference.
• Deep understanding of mathematical foundations of machine learning.
• Balances theoretical underpinnings and practical implementations.
• Central theme: Probabilistic framework for understanding connections between methods and developing new algorithms.
• Well-structured and detailed with clear explanations and numerous examples.
• Code examples in MATLAB and translations in Python.

Disadvantages:

• Complexity and mathematical rigor may be challenging for beginners or those without strong background.
• Long length and detail may be overwhelming for some readers.
• Primary code examples are in MATLAB, which may not be as popular as Python, the recent edition contains Python snippets translated from MATLAB snippets,functioning of the solutions cannot be reliable and lack of forum for discussions on the solutions.
• Limited coverage of recent advances in machine learning, particularly in deep learning.

Coding the Connundrum of algorithms:

Coding is an essential part of integrating the statistical and other mathematical operations with computers, after all they are the “machines” which need to learn to perform tasks.

One might say that the very idea of ML lies on an attempt to teach tasks to machines without explicitly defined code, and that would be right, but to get a computer that level of competency requires it go through large datasets, and perform mathematical operations , coding is needed to represent the data in 1s and 0s for processors to comprehend them and coding is how the complex calculations are optimized to be performed on the data and return an output which is comprehensible, accurate and useful for humans.

Python is the most widely used programming language, in the ML and DS communities , mainly because of its easy to learn syntax, its handling of lower level abstractions, obejct oriented features, and a vast community of forums and repos which has helps by creating vast numbers of ML libraries and fixing bugs which has aided the growth of ML beyond programmers.

Apart from Python languages like R and Julia are used in ML (mainly by science researchers and statisticians) , CUDA a language based on used for performing and optimizing operations on GPUs for ML tasks. CUDA along with C++ are used to create backend libraries and tools for python to handle and process data at a faster rate with optimum number of resources. JSX a java script based library for mainly deploying algorithms and taking inference are used. The coding in iteself is a study of its own which one has to be well wersed with for gaining expertise in ML, below are some books mentioned for the same:

1. Python Crash Course: A Hands-On, Project-Based Introduction to Programming" by Eric Matthes (amazon link)

This book is for absolute beginners who want a gentle introduction to Python, and yet want to get an acquainted to all the significant libraries of Python used in ML and data Visualization. Its advantages and drawbacks are mentioned below:

Benefits:

• Beginner-Friendly: Covers fundamentals of Python before moving on to more advanced subjects.
• Simple and unambiguous language: Employs language that is understandable to novices.
• Hands-on Projects: Contains a web application built with Django, a data visualization project, and a game akin to Space Invaders.
• Extensive Coverage: includes topics such as classes, functions, control flow, data structures, and basic Python syntax.
• Promotes Best Practices: Stresses the need of early habit development and clear, understandable code writing.
• Visuals and Exercises: Contains exercises, snippets of code, and illustrations.
• Supportive Community: Consists of an active online community as well as a website that is related and offers further resources.

Cons:

• Some people may find the pace to be fast: Projects require a firm grasp of the fundamentals, which can be difficult for novices.
• Pay attention to Python 3: Particular to Python 3, which could result in Python compatibility problems
• Offers a thorough introduction to Python, but falls short in discussing more complex subjects like web development, machine learning, and data science.
• Not every Python use case or application may be covered by hands-on projects. More varied examples would be preferred by some readers

2.Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter -Wes Mckinney (O’Reilly) (amazon)

Benefits:

• Written by Wes McKinney, the person who created the pandas library
• A thorough examination of all pertinent subjects for data analysis.
• Real-world datasets and applicable applications.
• Easy-to-follow tutorials for seamless implementation.
• Practical approach with many of examples and activities.
• An approachable and lucid writing style.
• Content that has been updated to reflect the most recent changes in the Python data analysis community.

Drawbacks:

• Requires a basic understanding of Python.
• Provides less coverage of other data analysis libraries in favor of pandas and NumPy.
• Limited discussion of sophisticated machine learning.
• Information that is dense and needs to be read more than once to fully comprehend.

3.Scientific Computing with Python - : High-performance scientific computing with NumPy, SciPy, and pandas, 2nd ed. Edition - by Solem and Verdier (Packt Publications) (Amazon)

"Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas, 2nd Edition" is a valuable resource for researchers, students, and professionals, but requires basic Python knowledge and refer to other books to get some idea of ML concepts for advanced topics. The Advantages and Disadvantages of the book are given below:

Advnatages include:

• Thorough coverage of the major NumPy, SciPy, pandas, and Matplotlib scientific computing libraries in Python.

• Practical approach: Provides exercises and real-world examples for experiential learning.• Performance-focused approach: Prioritizes optimization, effective resource management, and high-performance computing methods.

• Simple and easy to read writing that organizes and makes sense of complex ideas.Content has been updated to reflect the most recent developments in scientific computing libraries.

• Wide readership: Relevant to professionals, scholars, and students in disciplines such as data science, engineering, physics, and finance.

Cons:

• Requires some prior knowledge. Prerequisites include a working grasp of Python.

• Dense material: Newcomers may find it overwhelming.

• Inadequate discussion of machine learning focuses on numerical techniques and scientific computers, ML topics discussed require prior knowledge as discussion is quite shallow and abrupt.

• Examples of Python 3 Code: All examples and exercises use Python 3, which may cause compatibility issues.

4.SQL for Data Analysis -by Cathy Tanimura (O’Reilly Publications) (amazon)

While Python is great for coding algorithms, it certainly lacks the ability to deal with huge databases,or isnt specifically designed for data analysis which is the reason why Julia and R can be given preference for less latency,or more functionalities, but python has the option of SQL and SQLlite and other NO SQL database connectors along with distributed systems which makes it easy to integrate with ML Libraries of Python which are more mature than that of R and Julia, hence learning SQL is an important skill for any data scientist and analyst. The advantages and disadvantages of this book are mentioned below:

Benefits:

• Emphasizes the use of SQL in data analysis, making it pertinent to data scientists, business analysts, and analysts of data.

• Offers useful illustrations and actual situations to show how to apply SQL in day-to-day tasks.
• Writes in an approachable and transparent manner, making difficult SQL topics simple to comprehend.
• Addresses a broad range of subjects that are crucial for data analysis, such as common table expressions, window functions, aggregation, data cleansing, and subqueries.
• Places a focus on a data-driven approach to analysis, showing how to utilize SQL to extract insightful information and think critically about data.
• Offers helpful hints and techniques for enhancing SQL proficiency.

Drawbacks:

• Requires a rudimentary understanding of SQL, which may pose difficulties for novice users.

• Strict concentration on standard SQL, with little attention paid to the unique features or syntactic variants of various SQL database systems.
• Is limited to data analysis; it does not address more general database design, management, or administration problems.
• Offers examples that are exclusive to a single application, which narrows the scope of applications; • May be daunting for beginners owing to the complexity and breadth of topics covered.

5.Professional CUDA C Programming -**by John Cheng (For Advanced Learners) (pdf)

A thorough tutorial on programming with CUDA (Compute Unified Device Architecture), an application programming interface model and parallel computing platform developed by Nvidia, may be found in "Professional CUDA C Programming" by John Cheng, Max Grossman, and Ty McKercher. The benefits and drawbacks of this book are as follows:

Benefits:

• Detailed Overview of CUDA: Provides an in-depth comprehension of the fundamental principles and architecture of parallel computing.

• Useful Examples: Offers a plethora of exercises and examples that highlight the versatility and strength of CUDA.

• Performance Optimization with a Focus: This section covers kernel performance optimization, memory hierarchy, and data transfer while focusing on developing effective CUDA code.

• Clearly Explained: Offers detailed directions to make complicated ideas simple to understand.

• Covers Advanced Topics: including debugging methods, CUDA libraries, and multi-GPU programming.

• Well-structured: For methodical knowledge building, each chapter builds on the one before it.

Drawbacks:

• Prerequisite Knowledge: This assumes that readers are familiar with C programming.

• Hardware Specific: Since CUDA is exclusive to Nvidia GPUs, users of Nvidia hardware are the main audience for this content

• Dense Material: A wealth of material that might need to be read more than once.

• Emphasis on Low-Level Programming: Extremely technical, might not support discussions of high-level concepts.
• Limited Coverage of Newer Developments: The most recent CUDA developments might not be included.
• Its suggested for using current materials or official Nvidia documents to augment learning.

Books for Explorative Data Analysis using Visualization, Data Analytics, and Statistics

1.Think Stats: Exploratory Data Analysis 2nd Edition - by Allen Downey ( O’reilly Publications) (amazon)

For practitioners interested in studying statistics and exploratory data analysis using Python programming, Allen B. Downey's "Think Stats: Exploratory Data Analysis, 2nd Edition" scan serve as a good resource. Beginners and those wishing to apply statistical ideas in their data analysis job will find this guide to be very helpful due to its approachable writing style, practical examples, and emphasis on real-world data. Readers should, however, be familiar with the fundamentals of Python; individuals who are interested in more complex subjects or in-depth data science procedures may need to augment their education with other materials.

Advantages and Disadvantages of "Think Stats"

Advantages:

• Practical Approach: Uses hands-on teaching methods, including real datasets.
• Accessible Writing Style: Clear and approachable, making complex statistical concepts understandable for beginners.
• Python Focus: Extensively uses Python for examples and exercises, covering libraries like NumPy, pandas, and SciPy.
• Comprehensive Coverage of EDA: Covers topics like data exploration, visualization, probability, hypothesis testing, regression, etc.
• Real-World Examples: Uses real-world datasets for engaging learning.
• Interactive Learning: Includes exercises and end-of-chapter problems for interactive learning.
• Free Online Access: First edition available for free online, so readers can check it out before purchasing second edition.

Disadvantages:

• Assumes Some Python Knowledge: Assumes basic understanding of Python, may not cover advanced statistical techniques.
• Emphasizes Simulation: Emphasizes simulations for explaining statistical concepts, may not appeal to all readers.
• Data Science Context: Does not cover data science practices, including machine learning and big data technologies.
• Python 2 vs. Python 3: Some examples may be based on outdated versions like Python 2 based libraries and code.

2.Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python" by Peter Bruce (O’Reilly ) (amazon)

The book "Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python" written by Peter Bruce, Andrew Bruce, and Peter Gedeck aims to close the gap between statistical theory and real-world data science applications. The benefits and drawbacks of this book are as follows:

Benefits:

• Dual Language Approach: Offers data scientists examples in both R and Python. • Extensive Coverage: Includes more than 50 key statistical concepts. • Practical Examples: Offers real-world examples of statistical approaches being applied to data. • Accessible and straightforward Explanations: Provides accessible and straightforward explanations of statistical principles. • Data Science Applications are the main focus: this approach differs from standard statistics textbooks. • Harmony between Theory and Practice: Establishes a harmonious equilibrium between elucidating theoretical ideas and showcasing their pragmatic application. • Helpful for Review: Provides a resource for going over key statistical ideas.

Drawbacks

1. Lacks technical depth :- Although the book covers a lot of ground, some readers might not find it to be as in-depth as they would be for more complex or specialist statistical techniques. Readers who want to go deeply into a certain topic may require more resources.
2. Assumes Basic Knowledge: - This book is predicated on the reader's familiarity with R or Python programming. It could be difficult for total beginners to program, therefore they might need to look over more introductory materials first.
3. Limited Coverage of Advanced issues:- Modern machine learning techniques and advanced statistical issues are not thoroughly covered. It may be necessary for readers with an interest in these topics to seek out more specialized publications or resources.
4. Practical Implementation Focus: - Because of this practical emphasis, the book might not go in-depth into the statistical methods' mathematical underpinnings. This may not satisfy readers who are seeking rigorous mathematical answers.
5. Book Format:- Because each concept is covered as a stand-alone topic, the format of covering more than fifty concepts can occasionally appear disjointed. Another structure might be more appealing to readers who want a story that is more cohesive and continuous.

3.Python for Data Science Handbook - by Jake VanderPlas (O’Reiliy) (amazon)

For anyone wishing to learn data science using Python, Jake VanderPlas' "Python Data Science Handbook" is a great reference. It is an excellent resource for novice and seasoned data scientists alike because of its thorough coverage, real-world examples, and emphasis on the Python ecosystem. It may not cover complex topics in detail and require some basic understanding of Python, but it offers a strong foundation in data science and is ideal for practical application and hands-on learning. Benefits:

• Extensive discussion of data science subjects, such as machine learning, data processing, and visualization specific to EDA.

• Useful Python code snippets and examples for practical applications.• Comprehensive coverage of the NumPy, pandas, Matplotlib, and Scikit-learn packages, as well as the Python ecosystem for data science.

• Jake VanderPlas's approachable writing style makes difficult subjects simple to comprehend.• Open Access: Content that is freely available online to a larger audience.

• Jupyter Notebooks: Learn and explore with interactive examples.

• A well-organized structure that progresses progressively from simpler to more complex subjects.

Drawbacks:

• Requires a basic understanding of Python; newbies may need to refer to a more elementary resource.

• Implementation-focused approaches might not offer in-depth theoretical justifications.
• Inadequate coverage of deep learning and sophisticated machine learning methods.
• Long-term commitment: Some readers may find the book challenging due to its length.
• Quickly changing field: Some information can become antiquated and need to be supplemented with newer sources.

4.Practical Time Series Analysis: Prediction with Statistics and Machine Learning- by Aileen Nielsen (O’Reilly) (amazon)**

Time Series datasets are complex, and analysis of such data requires knowlwdge of advanced statistics and ML methods and ability to implement them with libraries built for time series data.The practical application of this is extracting valuable insights from time series data in financial and commodity trading markets, along with predicting future demands and prices for a company to help it prepare a stratergy. The book Practical Time Series Analysis:Prediction with Statistics and Machine Learning helps in getting equipped with time series analysis specifically EDA, processing and elementary techniques of forecasting using statistics and ML methods.

The advantages and disadvantages of the book are given below:

Benefits:

• Pragmatic Approach: Offers case studies and real-world examples for using statistical and machine learning models on time series data.

• Broad Coverage: Examines a variety of time series analysis subjects, such as data pretreatment, exploratory data analysis (EDA), modeling strategies, machine learning models, and metrics for evaluating forecasting accuracy.
• Simple explanations: Aileen Nielsen breaks down difficult ideas into easily understood chunks that are suitable for readers of all skill levels.
• Code Examples: Offers Python code samples for real-world use.
• Integration of Machine Learning and Statistics: Fills up the gaps between contemporary machine learning approaches and conventional statistical methodologies.
• Real-World Case Studies: These showcase the applicability of time series analysis techniques by providing examples from various domains.
• Emphasis on Forecasting: primarily concentrates on time series data forecasting and prediction of future values.

Disadvantages:

• Assumes Basic Understanding: For advanced topics, extra resources are required. The book presupposes a basic understanding of machine learning and statistics.

• Limited Theoretical Depth: It might not go too far into theoretical underpinnings while still offering helpful advice.
• Quickly Changing Field: Time series analysis and machine learning are fields that are always changing, rendering some knowledge out of date.
• Emphasis on Python: The majority of the concepts in the book are implemented in Python, which may be restrictive for readers who would rather use another programming language.

5.The Art of Statistics - by David Spiegelhalter (pdf) (Penguin Publications)

Benefits :

• A fundamental component of EDA, the book highlights the value of visually and analytically examining data to find patterns, anomalies, and linkages.
• Real-World Examples: Spiegelhalter illustrates statistical ideas using real-world data settings, which can aid readers in understanding the practical applications of EDA.
• Visual Representation: The book's use of visual aids can highlight the value of visual exploration in statistical analysis. EDA frequently uses graphical approaches to summarize and analyze data.
• Contextual Understanding: The book's historical and contemporary examples aid readers in appreciating the development of statistical techniques and their use in EDA.
• Practical Applications: Although "The Art of Statistics" concentrates on more general statistical ideas, it gives readers the fundamental understanding required to

Book’s drawbacks include:

• Complexity: Readers with little experience in math or statistics may find some sections difficult.

• Depth vs. Breadth: Some subjects might not be thoroughly covered, necessitating the need of extra resources.
• Emphasis on Concepts: This approach prioritizes conceptual comprehension above detailed instructions for statistical tools or data processing.
• Not a Practical Guide: This is more of a conceptual and instructional tool than a how-to manual for statistical modeling or data analysis.

6.Storytelling With Data: A Data Visualization Guide For Business Professionals -by Nussbaumer Knaflic Cole (Wiley) (amazon)

This book is a practical guide aimed at helping business professionals effectively communicate insights through data visualization. Here are the advantages and disadvantages of this book:

Advantages:

• Provides practical techniques for creating effective data visualizations.
• Uses an accessible writing style, making complex data visualization principles accessible to all levels of expertise.
• Offers a step-by-step approach to designing and presenting data visualizations.
• Includes real-world examples and case studies from different industries.
• Emphasizes on using data visualization as a storytelling tool to convey insights, trends, and patterns.
• Provides practical tips and tricks for improving the clarity, impact, and effectiveness of data visualizations.
• Uses visuals to illustrate key concepts and techniques.

Disadvantages:

• Focuses on business professionals, limiting for academic or technical backgrounds.
• Does not delve deeply into the technical aspects of data visualization tools or programming languages.
• Recommends subjective design choices based on author's preferences and experiences.
• May become outdated over time due to rapid evolution of tools and techniques.

Classical ML-Supervised, Unsupervised,Semi-Supervised

Unlocking the Magical power of Machine Learning using  Data,Math, and Code

Supervised learning uses regression and classification to train models on labeled data, while unsupervised learning discovers hidden patterns in unlabeled data through clustering and dimensionality reduction. Semi-supervised learning combines labeled and unlabeled data to improve model performance when labeled data is scarce. Understanding these paradigms is crucial for selecting the right techniques and preparing for advanced AI applications.

Here are some books to learn the theory and code of different types of ML:

1.The Hundred-Page Machine Learning Book - Andriy Burkov (pdf)

This roughly 100-page book provides an overview of machine learning, including the math behind techniques. It includes QR code links for further reading and follows a'read first, buy later' policy. The book is well-organized, discussing common algorithms, best practices, supervised and unsupervised learning, and related topics.

Here are the advantages and disadvantages of the book:

Benefits:

• Focused and Concise: Provides a 100-page overview of machine learning that is focused and concise.
• Practical Approach: Emphasizes real-world examples and practical applications of machine learning methods.
• Comprehensive Coverage: Includes a broad range of subjects related to machine learning, such as feature engineering, supervised and unsupervised learning, model evaluation, overfitting, regularization, and cross-validation.
• Suitable for Novices: written without a lot of mathematical jargon and in an approachable tone.
• Extra Resources: enhanced by extra resources and a vibrant online community.

Drawbacks:

• Limited Depth: Doesn't go into great detail on any one subject.
• Inadequate: Doesn't address cutting edge subjects or current advancements in machine learning.
• Practicality First: May neglect theoretical underpinnings that are essential for a more thorough comprehension of machine learning concepts.
• No Hands-On Code Examples: Offers no exercises or examples involving hands-on coding.

2.Pattern Recognition and Machine Learning -by Christopher Bishop (pdf)

Benefits:

• Extensive focus on machine learning and pattern identification.

• Places a focus on a probabilistic framework for treating uncertainty in a systematic manner.
• Well-written, readable, and visually appealing content.
• Useful applications with exercises and examples from the actual world.
• Expert advice from a foremost specialist.

Cons:

• Density: Might be overwhelming for certain readers.

• Mathematical Rigor: Readers who lack a solid foundation in statistics, probability, and linear algebra may find this difficult.
• Outdated Content: Certain content, particularly that which discusses developments in deep learning, might be out of date.
• Absence of Code Examples: Reduces the applicability for practical instruction.
• Narrow Focus: Might not go in-depth with niche subjects or the most recent advances in research methodology.

3.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Third Edition , - by Aurelion Geron (amazon)

One of the most popular books on learning ML, it uses Tensorflow in its implementation, the author maintains a fine balance of theoretical concepts required to begin ML along with code explanation and examples of implementing various tasks like classififcation to regression to various tasks of unsupervised,semi supervised and deep learning,hence giving a broad overview.This book is ideal for beginners and intermediate readers.Its advantages and disadvantages are listed below:

Advantages:

• Practical Approach: Hands-on, example-driven teaching of machine learning concepts.
• Comprehensive Coverage: Covers supervised, unsupervised, deep learning, using Scikit-Learn, Keras, and TensorFlow.
• Beginner-Friendly: Assumes minimal prior knowledge, suitable for beginners.
• Production-Ready Frameworks: Uses industry-standard tools for real-world machine learning applications.
• Regularly Updated: Second edition updated to cover latest advancements.

Disadvantages:

• Depth vs. Breadth: Lacks mathematical and theoretical depth.
• Code-Centric Approach: Focuses on code examples and implementation, not suitable for theoretical understanding.
• Specific to Tensorflow Frameworks: May limit usefulness for other programming languages or tools,tensorflow is begginer friendly but is reduntant in modern libraries, Pytorch is the trending library for ML yet this book does not cover Pytorch.
• Potential Outdated Content: Some content may become outdated over time.

4.Introduction to Machine Learning, Fourth Edition -Ethem Alpaydin

One of the most popular textbooks in the topic of machine learning is "Introduction to Machine Learning, Fourth Edition" by Ethem Alpaydin. A thorough introduction to the concepts and methods of machine learning is given in this book. The advantages and disadvantages of this book are as follows:

Benefits:

• Extensive coverage: encompasses deep learning, reinforcement learning, neural networks, supervised and unsupervised learning.

• Clear explanations: Offers succinct justifications for difficult subjects.
• Structured learning: Progresses from basic ideas to more complex subjects step-by-step.
• Mathematical rigor: Consists of machine learning algorithms' mathematical underpinnings.
• Useful examples: Offers a plethora of case studies and examples.
• Updated material: Shows the most recent advancements and patterns in machine learning.
• Practice problems and exercises: There are exercises and problems in every chapter.

Drawbacks:

• Complexity: Readers lacking a solid foundation in mathematics or statistics may find some portions difficult to understand.

• Depth vs. Breadth: Some topics may not be covered in great detail.
• Limited Programming Focus: Gives theory and principles more weight than real-world application.
• Resource-Intensive: May be difficult and time- and effort-consuming.
• Recommended resource for additional materials: Online tutorials, code repositories, or interactive exercises.

5. Elements of Statistical Learning -by Trevor Hastie et. al. (Springer ) (pdf)

"The Elements of Statistical Learning" is regarded as a foundational and authoritative work that offers a strong conceptual framework for a variety of methods. in statistical learning and machine learning.However, some readers may find it difficult due to its thoroughness and mathematical depth, especially those who are looking for the most recent developments in the subject or a more hands-on, practical approach.

Advantages of Disadvantages of the book include:

Advantages:

• Extensive discussion of statistical learning methods and supervised and unsupervised machine learning approaches. • Conceptual approach, emphasizing intuitions and concepts. • Real-world examples and data sets that demonstrate how to use the techniques. • Expert opinions from renowned specialists. • Updated often to reflect the most recent developments.

Cons:

• Mathematical Depth: Needs a strong foundation in statistics, programming, and mathematics. • Density: Extensive and thick, sometimes too much for inexperienced readers. • Absence of Code Examples: Restricts application and hands-on learning. • Possible Outdated Content: Over time, content may become antiquated. • Uneven Pacing: The authors' priorities may cast a shadow over certain topics.

6.Machine Learning with PyTorch and Scikit-Learn - by Sebastian Raschka (Packt) (amazon)

Benefits:

• Extensive coverage of machine learning subjects, ranging from fundamental ideas to sophisticated methods.

• A hands-on approach using real-world exercises and examples.
• Twofold emphasis on Scikit-Learn and PyTorch.
• Sebastian Raschka's concise explanations.
• Content that has been updated with the most recent advances in deep learning and machine learning.
• Useful illustrations based on actual datasets.
• A code repository containing all of the code samples for quick access, usage, and editing.

Drawbacks:

• May be difficult for novices, requiring a basic understanding of Python.

● Difficulty for novices, particularly when using PyTorch for deep learning.

• Because this is a rapidly changing field, certain material and code samples may not be current.
• Not covering every topic; depth preferred over breadth.

7.Semi Supervised Learning -by Chapelle et.al. (MIT Press) (pdf)

For individuals who are particularly interested in semi-supervised learning, the book "Semi-Supervised Learning," is an extremely useful resource. It is a must-read for scholars and experienced practitioners in the subject because of its thorough coverage, expert contributions, and emphasis on theoretical underpinnings. Though because of its depth of technical knowledge and narrow emphasis, readers with a strong background in machine learning and a particular interest in SSL would benefit most from it. The advantages and diadvantages of the book are listed below:

Advantages:

• Extensive Overview: Addresses a variety of SSL approaches.

• Expert Contributions: Renowned researchers edited the book.

• Theoretical Foundations: Offers a strong basis in mathematics and SSL concepts.

• Diverse Methods: Includes transductive learning, co-training, self-training, and graph-based techniques.

• Real-World Applications: Covers the useful uses of SSL across a range of industries.

• Current Research: Consists of current research and advancements within the subject.

Cons:

• Complexity: Requires a solid foundation in statistics and machine learning.

• Niche Focus: It might not be helpful to introduce machine learning more broadly

.• Few Real-World Examples: There might not be as many real-world, interactive examples or code implementations.

• Potentially Outdated: Newer techniques may have supplanted certain older methods and approaches.

• Dense Reading: The material is complex and demands close reading and comprehension.

Books for Deep Learning and other methods of ML:

1.Dive into Deep Learning - Zhang et.al. (Open source) (web version)

Dive into Deep Learning" (D2L) is an interactive deep learning book written by Zhang et.al. It combines theory, real-world examples, and interactive coding activities, it seeks to make deep learning approachable to a broad audience. With executable code and interactive Jupyter notebooks, the book's interactive approach sets it apart from the competition. The Benifits and cons of this book are listed below:

Benefits include:

• Interactive Learning: Readers can run and edit code right within the book

.• Extensive Coverage: Includes everything from fundamentals to sophisticated methods related to deep learning.• Practical Focus: Uses exercises and examples of coding to highlight practical use.

• Open Source: The book encourages collaborative learning because it is freely accessible online and open-source.

• Current Content: Consistently updated to showcase the most recent developments in deep learning.

• Extensive Examples: Using well-known deep learning frameworks, this section offers extensive examples.

• Simple explanations: Provides an approachable way to understand complicated ideas.

Cons:

• Assumes Basic Knowledge: Requires a fundamental comprehension of machine learning and Python programming.

• Broad Scope: May not go into great detail on every topic.

• Technical Setup: Needs a proper setting with Jupyter notebooks and required deep learning

• Dense Material: Some sections can be dense and complex.
• Focus on Code: May not provide as much theoretical depth as some readers prefer.

2.Python Machine Learning:ML and DL with Python, Scikit-learn, and TensorFlow 2 - Sebastian Raschka (Packt) (amazon)

Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow 2" by Sebastian Raschka and Vahid Mirjalili is a popular book that provides a comprehensive introduction to machine learning and deep learning using Python. It covers a wide range of topics and emphasizes practical implementation using popular Python libraries. Here are the benifits and drawbacks of this book:

Benefits:

• Extensive covering of machine learning subjects, ranging from fundamentals to sophisticated methods.

• Hands-on exercises and multiple code examples for a practical implementation.
• Sebastian Raschka's concise explanations, appropriate for readers of different skill levels.
• Content that has been updated to match the most recent advances in deep learning and machine learning.
• Case studies and real-world examples that illustrate how machine learning and deep learning approaches are applied.
• A wealth of resources, such as a GitHub repository, are readily available for community sharing, problem-solving, customization, and hands-on learning.

Drawbacks:

• May be difficult for novices, requiring a basic understanding of Python.

• Because this is a rapidly changing field, certain material and code samples may not be current.● Difficulty for novices, particularly with TensorFlow 2 deep learning.
• Comparing breadth and depth, not covering every subject.
• Practical emphasis rather than deep theoretical analysis

3.Deep Learning by -Ian Goodfellow et.al. (MIT Press) (pdf)

Three influential publications in the field of deep learning has been "Deep Learning" by Goodfellow,Bengio,and Courville. It was published by MIT Press and is recognized by many as one of the most thorough and reliable books on the topic. The book offers a thorough exploration of the conceptual underpinnings and real-world uses of deep learning, making it an invaluable tool for scholars, practitioners, and students alike.

The advantages and disadvanatges of the book are as follows:

Benefits:

• Authoritative: Prominent experts in deep learning, such as Yoshua Bengio, Aaron Courville, and   Ian Goodfellow.

• Comprehensive: Offering a strong theoretical and practical foundation, the book covers a wide spectrum of deep learning issues.
• Theoretical Depth: Providing rigorous mathematical derivations and explanations, the book explores the theoretical components of deep learning.
• Well-Ordered: The book is arranged in a logical manner, beginning with the fundamentals and progressively covering more complex subjects.
• Ample References: There are a lot of scholarly articles and additional readings cited throughout the book.
• Wide Readership: Scholars, practitioners, and students alike can benefit from this work.
• Standard Textbook: Frequently used as a textbook in deep learning and artificial intelligence courses at universities.

Disadvantages:

• The book's complexity makes it difficult for readers without a good background in mathematics, which is one of its drawbacks.
• Theory-focused: There might not be enough real-world examples or code implementations in the book.
• Dense Material: Careful reading and comprehension are necessary due to potentially dense information.
• Quickly Changing Field: Unlike other deep learning books, TensorFlow and PyTorch are not covered in this one.

4.Grokking Deep Learning -by Andrew Trask (Manning Publications)(amazon)

"Growing Deep Learning" by Andrew W. Trask is a good introductory book for anyone wishing to learn the foundations of deep learning in an easy and practical manner. It is appropriate for novices with some basic programming skills because of its hands-on approach, understandable explanations, and captivating writing style. It may not go into great detail on complex subjects or particularly emphasize well-known deep learning frameworks, but it nevertheless offers a solid foundation and encourages readers to delve deeper into the subject. Readers should think about adding additional sophisticated materials and current resources to this book in order to stay up to date and expand their knowledge. Benifits and Drawbacks of the book are listed:

Benefits :

• An approachable introduction that starts with fundamental ideas and works its way up to more difficult ones. This makes it ideal for beginners.

• Practical Approach: Focuses on learning through practice, including a ton of coding activities and examples.

• Step-by-Step Explanations: Builds on earlier chapters to offer a seamless learning curve by clearly explaining deep learning concepts.

• Visual Illustrations: Offers illustrations to help clarify concepts that are abstract.• From Scratch Implementations: Enhances comprehension of foundational models by focusing on developing deep learning models from scratch.

• Real-World Applications: Provides useful illustrations and actual uses for deep learning.

• Engaging Writing tone: Andrew Trask writes in an engaging and conversational tone.

Drawbacks:

• Limited Depth: More resources may be needed to fully address complex topics.

• Basic Implementation Focus: Uses NumPy and Python to create deep learning models from scratch.
• Assumes Some Programming Knowledge: Presumes a working familiarity with Python.
• Less Focus on Mathematical Detail: May overlook intricate mathematical details.
• Quickly Changing Domain: Over time, content may become old and need to be updated on a frequent basis.

5.Deep Learning for Coders with fastai and Pytorch -by Jeremy Howard et.al. (web-version)

Jeremy Howard and Sylvain Gugger's book "Deep Learning for Coders with Fastai and PyTorch" offers a useful and interactive introduction to deep learning with the fastai library and the PyTorch framework. A broad readership, including those without a strong foundation in mathematics or deep learning theory, is intended for this book, which aims to make deep learning approachable. The benefits and drawbacks of this book are as follows:

Benefits:

• Practical Focus: Offers practical instruction through projects and examples of coding.• Beginner-friendly: explains difficult ideas in straightforward language.

• Fastai Library: based on PyTorch, this library offers advanced deep learning functionality.

• Real-World Applications: Shows how to use deep learning to solve practical issues.

• Intense Community: Provides discussion boards, extra resources, and assistance.

• Integration with PyTorch: Offers a comprehensive overview of PyTorch.• Project-Based Learning: Interesting, pertinent projects are included in every chapter.

Cons:

• Requires Some Programming Knowledge: This requires some familiarity with Python in particular.• Less Theoretical Depth: Emphasizes actual application over theoretical underpinnings.

• Fastai-Specific: May restrict access to additional deep learning frameworks and tools

.• Quickly Changing Field: Over time, certain information may become dated.

• Bottom-up method preferred; top-down approach may not be suitable for everyone.

Books for NLP, CV and other application-task specifc purposes:

Books for Natural Language Proccessing:

1.Speech and Language Processing - by Jurafsky and Martin (Stanford.edu) (web version)

 Written by Daniel Jurafsky and James H. Martin's "Speech and Language Processing (3rd Draft)" is an extensive textbook that covers a wide range of topics in speech recognition and natural language processing (NLP). It is regarded as a foundational literature in various subjects and is widely utilized in academia. The benefits and drawbacks of this book are listed below:

Benefits:

• Extensive coverage: covers advanced themes, statistical procedures, machine learning approaches, language fundamentals, and basic principles.

• Prominent experts in the subject Daniel Jurafsky and James H. Martin are examples of authoritative writers.
• Detailed explanations: Offers thorough justifications for ideas, formulas, and methods.
• Updated Content: Takes into account the most recent developments in speech recognition and NLP.
• Balanced Approach: Maintains equilibrium between computational techniques and linguistic theory.
• Rich Resources: Contains a plethora of exercises, examples, and references.
• Educational Focus: Well-organized for learning, with activities at the end of each chapter, vivid examples, and clear definitions.
• Real-World Applications: Talks about how NLP and voice processing are used in real-world situations.

Drawbacks:

• Assumptions: Some prior expertise in mathematics, linguistics, and computer science is assumed.
• Dense Material: Information-rich content is possible.
• Broad Scope: May not go into great detail on every subject.
• Rapidly Evolving Field: Content may become outdated over time.
• Focus on Theory: May not suit readers seeking a more hands-on, code-centric approach.

2.Foundations of Statistical Natural Language Processing" by Christopher D. Manning and Hinrich Schützer

Foundations of Statistical Natural Language Processing is a highly regarded textbook that offers a comprehensive introduction to statistical approaches in NLP.Its clear explanations, theoretical depth, and academic rigor make it a valuable yet challenging for those who intenf to master statistical methods for NLP,TThe book is based on traditional statistical methods of NLP and doesnt cover modern deep learning techniques, yet it remains a foundational text that continues to influence the study and development of NLP algorithms which can be deployed in scale with low latency.The benifits and cons of the book are given below:

Benefits include:

• Broad coverage, including syntactic parsing, machine translation, part-of-speech tagging, language models, and semantic analysis.

• Theoretical Foundation: Highlights the statistical and mathematical foundations of NLP algorithms.

• Clear Explanations: Provides accessible and understandable explanations of difficult ideas.Statistical natural language processing algorithms are explained in length in the Algorithmic length section.

• Academic Standard: Often used as a textbook in computational linguistics and natural language processing courses at universities.

• Pertinence to Contemporary NLP: Fundamental ideas and methods are still applicable to contemporary NLP.• Supplementary Resources: consists of exercises, lecture slides, and datasets.

Cons:

• Complexity: Demands a solid grasp of algorithms, linear algebra, and probability theory

.• Emphasis on Conventional Approaches: insufficient discussion of deep learning methods, which can be unnecessary in a field that is developing quickly.

• Less Focus on Useful Applications: Does not include useful, , hands-on applications.

3.Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP System -by Vajjala et. al. (O’Reilly) (Amazon):

Practical Natural Language Processing is a comprehensive Guide to Building Real-World NLP Systems" is likely a valuable resource for practitioners and enthusiasts looking to delve into practical NLP implementations. Its hands-on approach, comprehensive coverage of NLP tasks, and focus on real-world applications make it suitable for developers, data scientists, and researchers interested in leveraging NLP techniques effectively.The following are benifits and cons:

Benifits:

• It provides a comprehensive overview of the entire field of natural language processing (NLP), making it a great foundation for learning the subject.
• The book is well-organized, with a clear structure divided into four parts: foundations, essentials, applied, and bringing it all together.
• Each chapter includes a wrapping up section to summarize the key points and provide references, which helps the reader retain the information.
• The authors guide readers through the process of building real-world NLP solutions and adapting them for different industry verticals like healthcare, social media, and retail.
• The book is endorsed by experts in the field like Zachary Lipton, Sebastian Ruder, and Marc Najork.
• It is used as a reference book and textbook at various universities

Pitfalls:

• The book may be too high-level and not cover in-depth theoretical and technical details related to NLP concepts, which could be a drawback for those pursuing cutting-edge NLP research.
• Readers are expected to follow the respective documentations for various frameworks used in the code examples, which requires additional effort.
• The book is printed in black and white ,some of the color figures in the paperback version may not be legible in black and white.

4.Neural Network Methods for Natural Language Processing- by Yoav Goldberg (Morgan & Claypool Publications) (Amazon)

The book "Neural Network Methods in Natural Language Processing" provides a thorough examination of neural network approaches used in the NLP domain. This book explores the most recent developments and offers insightful information about the potent combination of neural networks and natural language processing. The benifits anc cons of the books are;

Benefits include:

• Discusss the most recent developments in neural network techniques for Natural Language Processing (NLP).

• Offers a thorough examination of the integration of neural networks with NLP.
• Improves text production, sentiment analysis, and machine translation, among other language processing tasks.
• Provides real-world examples and applications for practical experience.
• Draws attention to how neural network approaches outperform conventional NLP techniques in terms of performance.

Cons:

• Quickly changing field; may not include the most recent advancements.

• Complexity; may be too advanced for novices.
• Limited broad context; might not offer an extensive overview of the whole NLP domain.
• A thorough discussion may not be given to ethical issues like bias, privacy, and possible misuse.

5.Text Mining and Visualization using Open Source Tools -by Chapman&Hall (CRC Press) (Amazon)

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning" serves as a comprehensive guide to harnessing the power of text analysis and machine learning. This practical book equips readers with the skills to develop language-aware data products using Python, it demonstrates how to extract valuable insights from text data, covering essential topics such as sentiment analysis, topic modeling, and text classification.

Benefits and Drawbacks of "Applied Text Analysis with Python"Benefits:

Benifits:

• Empirical, practical approach: Offers code snippets and detailed instructions for using Python for applied text analysis.

• Python and Machine Learning Focus: Makes use of the well-liked programming language Python for machine learning and text analysis.

• Machine Learning Integration: Describes how to create data products that are cognizant of language.

• Real-world Data Sources: Text analysis approaches are demonstrated using customer reviews, social media data, and news articles

.• Discussion on Complex Subjects: comprises natural language processing, text categorization, topic modeling, and sentiment analysis.

• Bias and Ethical Issues: Talks about possible bias in text analysis.

Drawbacks:

• Knowledge of Python Is Necessary: Prerequisite understanding of Python programming is assumed.
• Limited Theoretical Explanation: Offers nothing in the way of theoretical underpinnings.
• Updates and New Developments: Current developments might not be covered.
• Specific Library Versions: Makes use of particular Python packages and libraries.
• Complexity for Novices: Including machine learning ideas could make it more intricate.
• Less Attention on Basic Text Processing: Advanced subjects and machine learning integration are prioritized.

Books for Computer Vision:

1.Computer Vision: Algorithms and Applications by Richard Szelisk (Springer) (Amazon)

Often called the "bible of computer vision", this comprehensive book covers fundamental techniques and real-world application of Computer Vision, the advantages and disadvnatages of the book are:

Benefits include:

• A thorough review of computer vision, including features identification, image processing, camera imaging models, and scene understanding.

• Strikes a balance between applied theory and real-world applications.

• Offers thorough justifications for the different algorithms utilized in computer vision problems.

• Discusses methods for processing images, including feature extraction, segmentation, enhancement, and filtering.

• Emphasizes 3D modeling, stereo vision, and structure from motion as methods for 3D vision and reconstruction.

• Highlights practical uses of computer vision algorithms across a range of sectors.

Cons:

• Mathematical complexity: Readers with little or no foundation in mathematics may find it difficult

.• Quickly changing field: May not incorporate the newest developments and styles

.• Trade-off between length and depth: Some readers may find it too much to handle.

• Implementation details: Because certain algorithms are complex, they can need more explanations or simpler variants.

• Absence of recent updates: The 2010 update may not have included the most recent advancements in machine learning and deep learning applications.

2.Computer Vision: A Modern Approach (2nd Edition) - David Forsynth (Pearson Publications) (Amazon)

"Computer Vision: A Modern Approach" by David A. Forsyth is considered an excellent foundational textbook for learning the core principles and techniques of computer vision. However, it may be better suited for readers with some prior background in the subject matter.

Advantages:

• It is a widely used textbook that provides a comprehensive introduction to the fundamental ideas and methods in computer vision.
• The book covers a broad range of topics, including image formation, object recognition, and complex areas like stereo and motion analysis.
• It is classroom-focused, with tested exercises and a clear structure to help readers learn the essential concepts.
• The author, David A. Forsyth, is an expert in the field of computer vision, ensuring the content is authoritative and up-to-date.

Disadvantages:

• The book may require some prior knowledge in areas like linear algebra, optimization, and machine learning, making it potentially challenging for complete beginners.
• The depth of coverage on advanced topics may be too technical for readers looking for a more introductory-level understanding of computer vision.
• As the field is rapidly evolving, some of the content may become outdated over time, requiring readers to supplement the information with more recent resources.

3.Learning OpenCV 4 Computer Vision with Python 3 -by Howse and Minchino (Packt) (Amazon)

Learning OpenCV 4 Computer Vision with Python 3" is a valuable resource for beginners and intermediate learners interested in applying computer vision techniques using OpenCV and Python. It offers a practical, accessible introduction, covering a wide range of OpenCV functions and real-world applications.

Benefits:

• Using Python and OpenCV, it offers a practical introduction to computer vision.

• Designed with OpenCV and Python integration in mind, making it appropriate for novices.
• Covers a large number of OpenCV modules and functions.
• Demonstrates the use of computer vision concepts to practical issues.
• Easy to read and brief writing style that is appropriate for beginning and intermediate readers.
• Provides community support via the open-source OpenCV project.

Limitations:

• Requires a foundational understanding of OpenCV and Python programming.

• Strict theoretical justifications.
• Might not include the most recent iterations or library updates.
• A reduced emphasis on complicated computer vision methods and deep learning with OpenCV, among other advanced topics.
• Readers should modify the code according to their particular version of OpenCV.

4.Computer Vision: Models, Learning, and Inference by Simon J.D. Prince (Cambridge University Press) (Amazon)

Simon J.D. Prince's "Computer Vision: Models, Learning, and Inference" is a thorough book that explores the field of computer vision and provides insights into its models, inference techniques, and learning procedures.Focuses on the relationships between image data and other aspects like object class and 3D structure to enable new inferences. Below is a summary of its benefits and drawbacks:

Benefits:

• Places a strong emphasis on computer vision models and the process of inference.

• Offers a solid theoretical and mathematical basis.
• Covers statistics, probability theory, and linear algebra fundamentals.
• Spends a good deal of time on temporal models, which are necessary to comprehend and evaluate sequential data.
• Describes graphical models and how computer vision uses them.
• Discusses different learning and inference strategies.
• Illustrates the practical applications of inference methods and computer vision models.

Cons:

• Mathematical Complexity: Readers who lack a solid foundation in mathematics may find this challenging.

• Less Emphasis on Practical Implementation: There might not be many examples of coding or comprehensive practical implementations.
• Updates and New Developments: The most recent developments and trends might not be included.

5.Modern Computer Vision with PyTorch -by Ayyadevra and Reddy (Packt)(Amazon)

Modern Computer Vision with PyTorch" is a valuable resource for beginners and intermediate learners interested in applying deep learning to computer vision tasks. It offers clear instructions, practical examples, and exposure to advanced topics. However, readers should be versed certain prerequisites like basic understanding of CV algorithms,intermediate Python coding etc

The benifits and drawbacks of the book are listed below:

Benefits:

• Using PyTorch, it offers a contemporary and useful introduction to computer vision.

•It is centered around PyTorch, a popular deep learning framework.

• Provides beginners with succinct, easy-to-follow step-by-step directions.

• Provides a ton of Jupyter Notebooks and code examples for practical practice.

• Addresses complex subjects such as generative models, object detection, picture segmentation, and convolutional neural networks.

• Exhibits how to combine PyTorch with additional libraries, such as PyTorch Lightning, PyTorch Ignite, and TorchVision.

Cons:

• Prerequisites include familiarity with Python programming and fundamental machine learning ideas.

• Offers only a few theoretical justifications.

• Consistently adds new features and enhancements to PyTorch.• A reduced emphasis on conventional CV writing methods.

• Beginners may find advanced topics challenging.Readers may find the example's intricacy and variants challenging.

Books for Reinforcement Learning, Generative model based Machine Learning tasks and niches:

1.Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play - by David Foster , (O’Reilly) (Amazon):

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play" is a comprehensive guide to the exciting world of generative models in deep learning, like GANs, VAEs and other semi-supervised methods. Here's an overview of its advantages and disadvantages:

Advantages:

• Focus on Generative Models: Provides in-depth understanding of how machines can generate novel content.
• Explores Creative Applications: Showcases potential for machines to exhibit creativity and innovate in human-like ways.
• Clear and Accessible Writing: Balances technical detail and narrative style for a smooth learning journey.
• Coverage of GANs and VAEs: Provides comprehensive treatment of these models.
• Practical Implementation: Includes practical implementation details, code examples, and step-by-step instructions.
• Exploration of Ethics: Discusses potential misuse, copyright concerns, and impact on creative industries.

Disadvantages:

• Basic Knowledge Required: Assumes basic understanding of machine learning and deep learning concepts.
• Limited Mathematical Detail: May not go into extensive depth.
• Updates and New Developments: May not cover latest advancements.
• Less Focus on Traditional ML: Focuses on deep learning approaches to generative models.
• Complexity of Advanced Topics: Some advanced topics may be complex for beginners.
• Example Variations: Examples may not cover all possible variations or applications of generative models.

2.Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto: (KD Publications) (Amazon)

This book is considered the seminal and foundational textbook on reinforcement learning. It covers the core concepts, algorithms, and mathematical foundations of RL in a comprehensive manner, and helps bringing clarity in theoretical concepts of RL .

Benefits include:

• Thorough and scholarly treatment of foundational ideas, algorithms, and mathematics.

• Foundational text: Considered by many to be the best reinforcement learning textbook available.

• Theoretical depth: explores the fundamental ideas behind reinforcement learning.

• Easy-to-understand explanation: Provides difficult subjects in an understandable way.

• Exercises and informative examples for practical use are included.

Drawbacks:

• Mathematical Rigor: Needs a strong foundation in mathematics.

• Absence of Code Samples: Doesn't include comprehensive code examples of algorithms for reinforcement learning.

• Potential Outdatedness: Over time, content may become antiquated.

• Breadth vs. Depth: This approach covers the fundamentals of reinforcement learning but might not go into great detail on recent developments.

3.Deep Reinforcement Learning -Maxim Lapan (Packt) (Amazon)

This books is an excellent practical guide for developers and researchers looking to apply RL techniques in their projects, with a good balance of theory and hands-on implementation.

Benefits:

       • Practical Approach: Reinforcement learning (RL) is taught through hands-on, code-focused instruction.

• Extensive Coverage: Includes discussion of RL algorithms and concepts such as policy gradients, deep Q-networks, TRPO, PPO, and DDPG.
• Cutting-Edge Topics: Discrete optimization, multi-agent systems, and exploration approaches are covered in new chapters.
• Easy to Use for Novices: geared toward novices with some experience in deep learning.
• Skilled Writer: Deep learning practitioner and researcher Maxim Lapan has extensive experience.

Drawbacks:

• Prerequisite Knowledge: A presumption of Python and deep learning expertise.
• Insufficient Theoretical Depth: May not offer the same level of theoretical precision.
• Possible Outdating: Over time, content may become dated.
• Limited Hardware Focus: Concentrates on RL applications that are software-based.

4.Natural Language Processing with Transformers: Building Language Applications with Hugging Face -by Wolf et.al.(amazon)

The book focuses on using the Hugging Face Transformers library, a Python-based deep learning tool, for training and scaling large Transformer models. Essential advances in NLP are covered, such as text categorization, named entity identification, and question answering. Model optimization strategies including distillation, pruning, and quantization are also covered. Even in situations with a dearth of labeled data, the practical methodology and thorough coverage make it an invaluable tool for applying Transformers to real-world NLP tasks.

Advantages

• Hands-On Approach: The book uses a practical, hands-on approach to teach readers how to train and scale large models using the Hugging Face Transformers library, making it highly applicable for real-world projects.
• Comprehensive Coverage: It covers a wide range of topics, including the basics of Transformer models, fine-tuning, and applying these models to various NLP tasks. It also extends to tasks in speech processing and computer vision.
• Access to Resources: The book is supported by an organization on Hugging Face that contains all the models and datasets covered, providing readers with easy access to practical resources.
• Revised Edition: The revised edition is in full color, which can enhance readability and understanding of complex diagrams and code snippets.
• Expert Authors: The authors are experienced machine learning engineers at Hugging Face, ensuring that the content is both authoritative and up-to-date with the latest advancements in NLP.

Disadvantages

• Prerequisite Knowledge: The book requires a good knowledge of Python and is better suited for those who have completed an introductory deep learning course. This might be a barrier for beginners without this background.
• Focus on Hugging Face Ecosystem: While the focus on Hugging Face tools is a strength, it may limit the book's applicability for those looking to learn about other NLP frameworks or libraries.
• Technical Depth: The book may be too technical for readers who are not familiar with machine learning concepts, potentially making it challenging for those without a strong technical background.

5.Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox-(O’Reilly Publications) (Amazon)

Probabilistic Robotics" focuses on the application of probabilistic methods to address the challenges of perception, localization, and mapping in robotics. The book covers a wide range of topics, including Bayes filters, Kalman filters, particle filters, and Markov localization. It also explores advanced topics such as simultaneous localization and mapping (SLAM) and decision-making under uncertainty.

Content:

1. Introduction to Probabilistic Robotics: Understanding the role of uncertainty in robotics and the need for probabilistic methods.
2. Bayes Filters: Fundamental concepts and implementations, including Kalman filters, extended Kalman filters, and particle filters.
3. Localization: Techniques for robot localization, including Markov localization and Monte Carlo localization.
4. Mapping: Approaches for building maps of the environment, such as occupancy grids and feature-based mapping.
5. SLAM: Detailed exploration of simultaneous localization and mapping, combining localization and mapping into a unified framework.
6. Decision Making: Methods for making decisions under uncertainty, including path planning and control.

Benefits and Drawbacks of Probabilistic Methods in Robotics

Benefits include:

• Thorough investigation of robotics using probabilistic techniques.

• Detailed explanations of difficult ideas

.• Case studies and real-world examples.The use of pseudo-code and algorithmic detail.

• Multidisciplinary method connecting statistics, computer science, and robotics.

Cons:

• Difficulty in math; readers lacking a solid foundation in statistics and probability theory may find it difficult.

• Specializing in probabilistic techniques, possibly omitting other important details.

• Not as appropriate for novices due to assumed competence in robotics and related subjects.

• Outdated examples that require updating with newer research.

Books for Research in AI:

Amongst the books mentioned above, the books focusing on rigorous theoretical concepts are the ones which are “must-read” books for reseach, as every step and every decision and choice of parameters while conducting must be justified,books help in this aspect as they contain rigorous mathematical and theoretical proofs explaining the choice of one configuration over any other for the specific task and dataset of the research, hence they can be cited as sources which can give advantage in establishing credibility in Peer Reviewed research journals.

Theoretical Books useful in this aspect are:

Amongst the ones discussed above:

1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (Pearson)
2. An Introduction to Statistical Learning by Gareth James et al. (Springer)
3. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer)
4. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (MIT Press)
5. Pattern Recognition and Machine Learning by Christopher Bishop (Springer)
6. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy (MIT Press)
7. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (MIT Press)

Some additional books reagrding the same:

1. Bayesian Reasoning and Machine Learning by David Barber (Cambridge University Press)

Advantages:

• Provides a comprehensive introduction to Bayesian methods and their applications in machine learning
• Covers both theoretical foundations and practical implementation details
• Includes numerous examples and exercises to reinforce the concepts

Disadvantages:

• May be challenging for readers without a strong background in probability and statistics
• Some topics may be outdated due to the rapid progress in the field

Amazon Purchase Link:

Amazon

2.Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K.I. Williams (MIT Press)

Advantages:

• Provides a comprehensive and in-depth treatment of Gaussian processes, a powerful tool for Bayesian nonparametric modeling
• Covers both the theoretical foundations and practical applications of Gaussian processes
• Includes extensive references and pointers to further resources

Disadvantages:

• May be too technical and mathematical for readers without a strong background in machine learning and statistics
• Focuses primarily on Gaussian processes, with limited coverage of other machine learning techniques

Amazon Purchase Link:

Gaussian Processes for Machine Learning

3.Convex Optimization by Stephen Boyd and Lieven Vandenberghe (Cambridge University Press)

Advantages:

• Comprehensive coverage of convex optimization, a fundamental topic in machine learning and optimization
• Includes a wide range of applications and case studies to illustrate the concepts
• Provides a solid mathematical foundation for understanding optimization algorithms

Disadvantages:

• Highly technical and mathematical, requiring a strong background in linear algebra and calculus
• May be challenging for readers without prior experience in optimization and machine learning

Amazon Purchase Link:

Convex Optimization

4.Algorithms for Reinforcement Learning by Csaba Szepesvári (Morgan & Claypool)

Advantages:

• Provides a thorough introduction to the theoretical foundations of reinforcement learning
• Covers a wide range of algorithms, from dynamic programming to Monte Carlo methods
• Includes exercises and examples to help readers understand the concepts

Disadvantages:

• Focuses more on the theoretical aspects of reinforcement learning, with limited coverage of practical implementation details
• May be too technical for readers without a strong background in machine learning and optimization

Amazon Purchase Link:

Algorithms for Reinforcement Learning

5.Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (MIT Press)

Advantages:

• Provides a solid foundation in the theoretical and algorithmic aspects of machine learning
• Covers a wide range of topics, from supervised learning to unsupervised and semi-supervised methods
• Includes exercises and programming assignments to reinforce the concepts

Disadvantages:

• May be too theoretical and mathematical for readers looking for a more practical, hands-on approach
• Does not cover the latest advancements in deep learning and other cutting-edge machine learning techniques

Amazon Purchase Link:

Foundations of Machine Learning

6.Natural Language Processing with Transformers, Revised Edition by Lewis Tunstall, Leandro von Werra, and Thomas Wolf (O'Reilly Media)

Advantages:

• Comprehensive coverage of using transformer models for natural language processing tasks
• Hands-on examples and code using the Hugging Face Transformers library
• Includes the latest advancements in generative AI and large language models

Disadvantages:

• May require prior experience with deep learning and NLP concepts
• Rapid progress in the field means some content may become outdated quickly

Amazon Purchase Link:

Natural Language Processing with Transformers, Revised Edition

Books for Deploying ML, and Designing ML Systems for ML Applications

1.Machine Learning Engineering by Andriy Burkov

This book provides a comprehensive guide to the practical aspects of deploying and maintaining machine learning systems in production. It covers topics such as model serving, monitoring, and scaling.

• Advantages: Covers a wide range of deployment-related topics, provides hands-on examples, and is written by an experienced ML engineer.
• Disadvantages: May be too technical for beginners, and some content may become outdated quickly due to the rapid progress in the field.
• Amazon Purchase Link: Machine Learning Engineering

2.Introduction to MLOps by Mark Treveil (O’Reilly Publications)

"Introduction to MLOps" by Mark Treveil is a comprehensive guide that explores the intersection of machine learning and operations. This book delves into the methodologies and tools necessary to manage the lifecycle of machine learning models, from development to deployment and monitoring. It's designed to help practitioners understand how to streamline ML workflows, ensure reproducibility, and maintain the health of ML systems in production.

Advantages:

• Provides a detailed overview of MLOps practices.
• Includes practical examples and case studies.
• Written by experts in the field with hands-on experience.

Disadvantages:

• Might be too advanced for complete beginners.
• Some concepts may require a background in both ML and DevOps.

Amazon Purchase Link: Introduction to MLOps

3.Designing Machine Learning Systems by Chip Huyen (O’Reilly Publications)

The book guides readers through the essential principles and best practices for building reliable and scalable machine learning systems,with a focus on architecture and design, the book covers crucial aspects such as data pipelines, model versioning, and MLOps. Drawing on real-world case studies and the extensive experience of the author Chip Huyen, Huyen provides a comprehensive and practical approach to creating robust ML solutions, for both beginners and long term practitioners.

• Advantages: Provides a holistic view of ML system design, includes real-world case studies, and is written by an experienced ML engineer.
• Disadvantages: May be more suitable for experienced ML practitioners, and some content may overlap with other system design books. Amazon Purchase Link: Designing Machine Learning Systems

4.Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, and Michael Munn (Amazon)

Introduction: This book presents a collection of design patterns for common machine learning problems, helping readers build more robust and maintainable ML systems.

• Advantages: Covers a wide range of ML design patterns, provides practical examples, and is written by experienced ML engineers.
• Disadvantages: May be more suitable for intermediate to advanced ML practitioners, and the content may be less comprehensive than a dedicated system design book.Amazon Purchase Link: Machine Learning Design Patterns

5.Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable System - by Martin Kleppman

Designing Data-Intensive Applications by Martin Kleppmann is a comprehensive guide that helps software engineers navigate the diverse landscape of technologies for storing and processing data. The book examines the pros and cons of various tools and approaches, enabling readers to make informed decisions about what's best for their application.

The benifits and drawbacks of the book are listed below:

Benefits:

• Offers a thorough and useful summary of the essential concepts and trade-offs in data system architecture.
• Covers a wide range of subjects, including as complexity, fault tolerance, scalability, and consistency.
• Provides information on the distributed systems research that underpins contemporary databases.
• Features architectural and real-world examples from well-known internet services
• Written by seasoned researchers and software developers with in-depth knowledge of the subject

Drawbacks:

• Does not offer comprehensive guidance on how to install or utilize particular software packages or APIs
• Prioritizes data system architecture over deployment, operations, security, and management.
• Focus on specific technologies, which may not provide detailed coverage of all the technologies available
• Not a step-by-step guide, lacks practical examples.
• Updates and relevance may be lacking, requiring supplementation with latest information
• Assumes readers have a solid foundation in computer science and software engineering.

‍

" In conclusion, navigating the vast landscape of machine learning literature can be daunting, but these books attempt to serve as navigator guides, each offering unique insights and taking us a step closer to expertise, but this list by no means exhaustive, nor do we claim books being the only “legitmate” way to learn, they are supposed to be used in compliment with other resources like repositories (Link to top ML repos),blogs,newsletters and video courses, a increasingly large majority of which is available for free, books.

Cement is useless on its own but when mixed with water, stones and sunlight it becomes concrete, similarly Books are cement bags of learning, on their own we cannot fully comprehend it, but when mixed with practicing of code, numericals with, implementation and deployment with the architecture of good systems design can help us build a foundationally strong knowledge of ML, So, dive into these books, roll up your sleeves, and let practical experience elevate your journey into the exciting world of machine learning."

‍

Author

This article was written by  and edited by our writers team.