Industries everywhere are changing as a result of machine learning (ML) and artificial intelligence (AI). Naturally, everyone is aware of the hoopla surrounding artificial intelligence. From complex algorithms that manage the world's financial markets to voice-activated smart speakers in our living rooms, today's world has come to recognize artificial intelligence because of its many uses and advantages. Artificial intelligence (AI) technologies are transforming several industries, from fraud detection and tailored recommendations to self-driving cars and virtual assistants.

The ability of artificial intelligence (AI) to evaluate vast amounts of complex data and derive insightful information is one of its main benefits. As a result, companies may increase operational efficiency and make data-driven decisions. AI systems, for instance, can evaluate patient data to find trends and forecast illnesses, resulting in quicker and more precise diagnoses.

Artificial Intelligence is becoming a seamless part of our daily lives. Accompanying the excitement surrounding the topic is the scourge of ambiguity: can artificial intelligence be fully utilized to create remarkable systems, or is it merely a mysterious entity? This is exactly what we shall cover in this article: comprehending AI and creating one.

Artificial Intelligence

"Making machines do what human intelligence does" is the most basic definition of artificial intelligence. A machine is capable of performing any task that humans learn or develop instinctively as we age. So how can a machine acquire knowledge? Observation (the technical word for this process is "reinforcement learning") or pattern recognition (sometimes referred to as supervised or unsupervised data analysis).

Formally speaking, artificial intelligence is defined by the aforementioned geek as the branch of computer science that focuses on building intelligent machines that are capable of doing activities that need human intelligence. These tasks involve learning, making decisions, identifying speech, and solving problems.

With these techniques, what has been done? Well, everything from ChatGPT to Tesla's self-driving cars, has these very techniques at its basis with varying depths of knowledge. AI includes many interdisciplinary fields, Natural Language Processing, Computer Vision, audio processing, 3D, and so on, but for this article we will focus on AI in general.

Steps of Building an AI

1. Problem Definition and Data Collection
2. Data Preprocessing
3. Model Selection and Training
4. Model Evaluation and Optimization
5. Deployment and Monitoring

For a detailed version of these steps, you can refer to this great article (coming soon) on how to build AI systems?

What does an AI Engineer/Developer do?

AI Engineers develop new applications and systems to:

• Enhance the performance and efficiency of business processes
• Help the business make better decisions
• Lower costs
• Increase revenue and profits

Simply put, they use software engineering and machine learning to streamline a business with automation.

Many of an AI Engineer’s tasks overlap with those of a Machine Learning Engineer. Some of the responsibilities of an AI Engineer include:

• Coordinating with business leaders and software development teams to determine what business processes can be improved by using AI
• Creating and maintaining the AI development process and the infrastructure that it runs on.
• Applying machine learning techniques for image recognition.
• Applying natural language processing techniques to text and voice transcripts to pull insights and analytics from this data.
• Building and maintaining chatbots that interact with customers.
• Developing AI-driven solutions that mimic human behavior to accomplish repetitive tasks currently done by people.
• Building, training, and perfecting machine learning models.
• Simplifying the machine learning process so that other business applications can interact with them using APIs.
• Building recommendation engines, for shopping sites, streaming services, and other applications.
• Developing data pipelines that streamline the process of transforming raw data into the structured data necessary for AI processes.

Educational Requirement for AI

If you want to be an AI expert, you need to have a good educational background in computer science, a great foundation in mathematics, or a related field. The time it takes to learn AI will often depend on the route you take; whether it's self-taught or through formal education such as a university program.

A bachelor's degree in a relevant subject, such as information technology, computer engineering, statistics, or data science, is the very minimum needed for entry into the area of artificial intelligence engineering.

A Master’s degree will help you achieve a deeper understanding of AI concepts, topics, and theories. It’s also a valuable way to gain first-hand experience and meet other professionals in the industry. All of this can translate to helping you gain an important advantage in the job market and often a higher salary.

In a self-taught route, the duration can vary significantly as it largely depends on your prior knowledge, dedication, and available learning resources. It can take several months to a year or more to gain a solid understanding of AI concepts, programming languages such as Python, mathematics, and various machine learning algorithms through self-study. Self-paced online courses, tutorials, and practical projects can accelerate the learning process.

Here's a highly acclaimed entry-level course from us, where we take folks from 0 to 90, in about 2 months!

Skills required for AI & ML

Stage 1 - Programming Languages For AI Development

Python

Python is the go-to programming language for AI engineers/developers due to its readability and rich ecosystem of AI tools. For instance, you can use libraries like scikit-learn for machine learning or TensorFlow for deep learning. Its simplicity makes it accessible for beginners, and it’s widely used in fields like natural language processing (NLP), computer vision, and reinforcement learning.

Julia

Julia is a rising star in the AI world. It’s designed for data science and offers a balance between speed and ease of use. An example is Flux.jl, a Julia library for building neural networks. Julia’s speed makes it ideal for high-performance computing, which is essential in tasks like simulating complex systems or conducting large-scale data analysis.

R

R, although less user-friendly than Python, has a strong presence in the statistical and data analysis community. It’s excellent for tasks like data visualization and statistical modeling. Packages like ggplot2 and dplyr are widely used in data science for creating insightful visualizations and data manipulation.

Our recommendation: Start with Python.

Resources:

1. Youtube: Python for Beginner
2. ‍‍Course: Python tutorial

Pro Tip: Use the above one up to file handling and you'll have a good grasp of the language!

Stage 2 - Essential Skills in Mathematics for AI and ML

Foundations in Mathematics are the backbone of Artificial Intelligence (AI) and Machine Learning (ML). A solid grasp of mathematical concepts enables practitioners to understand, design, and optimize algorithms effectively. This article outlines the essential mathematical skills required to excel in the fields of AI and ML.

1. Linear Algebra

Linear algebra is fundamental to many AI and ML algorithms, particularly those involving large datasets and high-dimensional data. Key concepts include:

• Vectors and Matrices: Basic structures used to represent data.
• Matrix Operations: Addition, multiplication, inversion, and transposition of matrices.
• Eigenvalues and Eigenvectors: Important for understanding transformations and dimensionality reduction techniques like Principal Component Analysis (PCA).

Proficiency in linear algebra is crucial for understanding the mechanics of algorithms such as neural networks and support vector machines.

Resources:

1. Linear Algebra Use this one to have aof basic understanding.
2. YouTube: Essence of Linear Algebra from 3blue1brown for better visual understanding of the topics

2. Calculus

Calculus, particularly differential and integral calculus, is vital for optimizing machine learning models. Important topics include:

• Derivatives: Understanding the rate of change of functions, is acrucial for gradient-based optimization methods.
• Partial Derivatives: Necessary for dealing with functions of multiple variables, common in ML models.
• Integrals: Useful for concepts such as expectation and continuous probability distributions.

Calculus is essential for training algorithms, particularly in backpropagation for neural networks.

Resources:

1. Khan Academy has short, practical multivariable calculus lessons. They cover the most important concepts.
2. MIT OpenCourseWare offers a rigorous multivariable calculus class. The video lectures and course materials are all included.

3. Optimization

Optimization techniques are used to minimize or maximize objective functions, a critical step in training ML models. Key concepts include:

• Convex Optimization: Deals with convex functions, which have unique global minima, making optimization easier.
• Gradient Descent: An iterative method for finding the minimum of a function, widely used in training ML models.
• Stochastic Gradient Descent: A variation of gradient descent that uses random samples, enhancing efficiency for large datasets.

Understanding optimization helps in tuning models for better performance and efficiency

Resources:

1. ‍Khan Academy

Stage 3 - Essential Skills in Statistics for AI and ML

Understanding and effectively applying statistical concepts is crucial for success in the field of Machine Learning (ML). Statistics provide the foundation for interpreting data, building models, and validating the outcomes of ML algorithms. This article explores key statistical concepts that every ML practitioner should grasp to build robust and reliable models. Statistics is a good tool for doing Exploratory data analysis.

1. Descriptive Statistics

Descriptive statistics summarize and describe the main features of a dataset. They provide simple summaries ofa the sample and the measures. Key concepts include:

• Mean: The average of a dataset.
• Median: The middle value in a dataset when sorted.
• Mode: The most frequently occurring value in a dataset.
• Standard Deviation and Variance: Measures of the dispersion or spread in a dataset.

Understanding these concepts helps in getting a quick overview of the data distribution and spotting any anomalies or patterns.

2. Probability Distributions

Probability distributions describe how the values of a random variable are distributed. Common distributions in ML include:

• Normal Distribution: Also known as the Gaussian distribution, it's symmetrical and describes many natural phenomena.
• Binomial Distribution: Used for binary outcomes, like success/failure scenarios.
• Poisson Distribution: Describes the number of events occurring within a fixed interval of time or space.

Knowledge of these distributions is essential for selecting appropriate models and understanding their behavior.

3. Hypothesis Testing

Hypothesis testing is a method used to make inferences about a population based on sample data. It involves:

• Null Hypothesis (H0): A statement of no effect or no difference.
• Alternative Hypothesis (H1): A statement indicating the presence of an effect or difference.
• P-value: The probability of observing the test results under the null hypothesis.
• Confidence Interval: A range of values that is likely to contain the population parameter.

4. Resampling Methods

Resampling methods are used to assess the accuracy of sample estimates. Common techniques include:

• Cross-Validation: Dividing the data into subsets to evaluate model performance and ensure it generalizes well to unseen data.
• Bootstrapping: Repeated sampling with replacement from the dataset to estimate the distribution of a statistic.

These methods are essential for model validation and selection.

5. Dimensionality Reduction

Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving as much information as possible. Techniques include:

• Principal Component Analysis (PCA): Transforms the data into a set of orthogonal components.
• t-Distributed Stochastic Neighbor Embedding (t-SNE): A non-linear technique for reducing dimensions, particularly useful for visualization.

Reducing dimensionality helps in simplifying models, reducing computational costs, and mitigating the curse of dimensionality.

Resources: Khan Academy

Pro Tip: Basic Introductory Videos are sufficient for a start

Conclusion

A robust understanding of these mathematical concepts is indispensable for anyone aspiring to excel in AI and ML. These skills provide the foundation for developing, analyzing, and optimizing algorithms, enabling practitioners to tackle complex problems effectively. As the field of AI and ML continues to evolve, a solid mathematical background will remain a critical asset for innovation and success.

Stage 4 - Hands-on Experience with ML Libraries and Frameworks

Once you have selected a programming language, it is essential to gain hands-on experience with popular machine-learning libraries and frameworks. These tools can greatly simplify the process of building and deploying AI models. Here are some widely used ML libraries and frameworks, you can start learning them in the order in which it is mentioned.

1. Numpy: NumPy (Numerical Python) is a foundational library for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures.
2. Pandas:  Pandas is a powerful data manipulation and analysis library built on top of NumPy. It is widely used for data wrangling, manipulation, and analysis.
3. Matplotlib: Matplotlib is a plotting library used for creating static, interactive, and animated visualizations in Python. It is highly customizable and integrates well with NumPy and Pandas.
4. Seaborn: Seaborn helps you explore and understand your data. Its plotting functions operate on data frames and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.
5. Scikit-Learn: Scikit-learn is an open-source Python library that provides a wide range of machine-learning algorithms and tools for data mining and data analysis. It is built on the NumPy, SciPy, and Matplotlib libraries, and provides a consistent interface for many common machine learning tasks such as classification, regression, clustering, and dimensionality reduction.
6. TensorFlow: Developed by Google, TensorFlow is one of the most popular and powerful open-source ML libraries. It provides extensive support for building machine learning models, including deep learning architectures. TensorFlow offers a high-level API called Keras, which makes it easier to build and train models. With TensorFlow, you can tackle a wide range of AI tasks, from image and speech recognition to natural language processing.
7. PyTorch: PyTorch is another widely adopted ML framework that focuses on flexibility and dynamic computational graphs. Developed by Facebook's AI research lab, PyTorch has gained popularity for its intuitive syntax and efficient model training capabilities. It is widely used for cutting-edge research and applications in computer vision, natural language processing, and reinforcement learning.

Resources:  

1. The best way to learn about these libraries is to go through their documentation and experiment on top of that. Click on the hyperlink attached to their name for the documentation.
2. You can also use a video tutorial for learning them: Numpy, Pandas, Scikit-Learn, Tensorflow, Pytorch

Stage 5 - Understanding Machine Learning Algorithms

Supervised Learning: Regression and Classification Models

Supervised learning is a fundamental concept in machine learning, and it involves training a model on labeled data. In the case of regression models, the goal is to predict continuous or numerical values. On the other hand, classification models aim to predict discrete or categorical values.

To become an AI expert, it is essential to understand various regression and classification algorithms and when to apply them. Here are some commonly used algorithms in supervised learning:

1. Linear Regression: This algorithm is used for predicting continuous values based on linear relationships between input features and the target variable. It is a useful algorithm for tasks such as predicting housing prices based on features like square footage, number of bedrooms, and location.
2. Logistic Regression: Despite its name, logistic regression is primarily used for classification tasks. It models the probability of an event occurring based on input features. It is a popular algorithm for tasks like predicting whether an email is spam or not based on features like subject line, sender, and content.
3. Decision Trees: Decision trees are powerful algorithms that can be used for both regression and classification tasks. They partition the data based on selected features and create a tree-like model. Each internal node represents a feature, each branch represents a decision, and each leaf node represents a possible outcome. Decision trees are easily interpretable and can handle both numerical and categorical data.
4. Random Forest: Random forest is an ensemble learning algorithm that combines multiple decision trees to make predictions. Each tree in the forest is trained on a random subset of the data and features, helping to reduce overfitting. It is known for its high accuracy and robustness, making it suitable for complex tasks such as image classification or fraud detection.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds an optimal hyperplane that separates the data points of different classes. SVM can handle both linearly separable and non-linearly separable data by using kernel functions to transform the input features into higher-dimensional space.
6. Naive Bayes: Naive Bayes is a simple yet effective classification algorithm based on Bayes' theorem. It assumes that the features are independent of each other, given the class label. Naive Bayes is often used in text classification tasks and spam filtering.

By understanding the principles and use cases of these regression and classification algorithms, you will have a strong foundation in supervised learning, which is crucial for solving real-world AI problems.

Unsupervised Learning: Clustering and Dimensionality Reduction

Unsupervised learning is another important aspect of AI that focuses on exploring and discovering patterns or structures in data without the need for labeled examples. It is a valuable technique for tasks such as clustering and dimensionality reduction.

1. Clustering: Clustering algorithms are used to group similar data points together based on their similarities or dissimilarities. It helps in identifying natural groupings or categories within the data. One popular algorithm for clustering is K-means, which partitions the data into K clusters by minimizing the sum of distances between the data points and their assigned cluster-center. Other clustering algorithms include Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.
2. Dimensionality Reduction: In many AI applications, data can have a high-dimensional feature space. Dimensionality reduction techniques aim to reduce the number of features while preserving important information. Principal Component Analysis (PCA) is one commonly used method that transforms the data into a lower-dimensional space by finding the directions (principal components) along which the data varies the most. Another popular technique is t-SNE (t-distributed Stochastic Neighbor Embedding), which is particularly useful for visualizing high-dimensional data in 2D or 3D.

By mastering clustering and dimensionality reduction algorithms, you will be equipped to handle unsupervised learning tasks and gain insights from unstructured or unlabeled data.

Reinforcement Learning: Training Intelligent Agents

Reinforcement Learning is a branch of AI that focuses on training intelligent agents to make sequential decisions in an environment to maximize a reward signal. In this approach, an agent learns by interacting with the environment and receiving feedback in the form of rewards or punishments.

1. Markov Decision Process (MDP): Reinforcement learning is often modelled as a Markov Decision Process (MDP), which consists of a set of states, actions, transition probabilities, and rewards. The goal is to find an optimal policy that maps states to actions in order to maximize cumulative rewards.
2. Q-learning: Q-learning is a popular reinforcement learning algorithm that learns an action-value function (Q-function) through a trial-and-error process. The Q-function estimates the expected cumulative reward for taking a particular action in a given state. The algorithm iteratively updates the Q-values based on the reward received and the estimated Q-values of the next state.
3. Deep Q-Networks (DQN): DQN is an extension of Q-learning that uses deep neural networks to approximate the action-value function. By leveraging the power of deep learning, DQN has achieved impressive results in complex environments such as playing Atari games. The network takes in the current state as input and -
4. Policy Gradient Methods: Policy gradient methods are another popular class of reinforcement learning algorithms. Unlike value-based methods like Q-learning, policy gradient methods directly optimize the policy function that maps states to actions. This allows for more flexible and continuous action spaces. The algorithm estimates the gradient of the policy function by collecting samples through interaction with the environment and uses techniques such as Monte Carlo or REINFORCE to update the policy parameters.

Resources:

1. You can start with An Introduction to Statistical Learning: with Applications in Python, here you can have a good understanding of  the algorithms at your own pace.
2. If you are looking for a course, you can start with  Machine Learning Specialization by deeplearning.ai. It’s a bit outdated in terms of technology used in the coursework, but is good to understand the basics.

Stage 6 - Deep Learning

Deep learning is a subset of machine learning that uses multi-layered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence in our lives today. In short, deep learning is a method by which we are trying to mimic the human brain through neurons so that machines don’t need human intervention to learn.

Some Deep Learning algorithms

1. Convolutional Neural Network(CNN): CNNs are a deep learning algorithm that processes structured grid data like images. They have succeeded in image classification, object detection, and face recognition tasks.
2. Recurrent Neural Network(RNN): Recurrent networks are perfect for time-related data and they are used in time series forecasting. They are designed to feedback, such that the output re-enters the input. It is a way of making information go back from the output to the input in order to send it throughout the network. As a result, they can retain previous facts and incorporate them into their forecasts.
3. Long Short-Term Memory Networks (LSTMs): LSTMs are a unique type of RNN that can memorize long-term dependencies. They are intended to eliminate the long-term dependency problem which makes them better to use for tasks such as speech recognition and time series prediction
4. Auto-Encoders: Autoencoders are a special type of unsupervised feedforward neural network. The main application of Autoencoders is to accurately capture the key aspects of the provided data to provide a compressed version of the input data, generate realistic synthetic data, or flag anomalies
5. Generative Adversarial Networks (GANs): GANs perform unsupervised learning tasks in machine learning. The goal of generative modelling is to autonomously identify patterns in input data, enabling the model to produce new examples that feasibly resemble the original dataset. The Generator and the Discriminator are the names of two models. The main point that they are in a rivalry with each other for is scrutiny. They try to first grasp the variations in a data set, and later make the dataset. They are generally used for direct photo production, even allowing for the remote possibility of unconventional datasets.
6. Transformers: Transformers is a new technology that was mainly developed for language applications but is quickly taking the place of RNNs as they become redundant. They are built on a “vision”, which is a powerful tool for a network to concentrate on a specific piece of information.

Training Deep Learning Models with Backpropagation

Once the structure of a neural network is defined, the next step is to train it using a technique called backpropagation. Backpropagation is a gradient-based optimization algorithm that adjusts the weights of the network based on the errors it makes during the training process.

1. Forward Pass: In the forward pass of backpropagation, the inputs are fed into the network, and the activations of each neuron in each layer are calculated. This process continues until the output layer is reached, producing a predicted output.
2. Calculate Loss: The predicted output is then compared to the actual output, and a loss function is used to measure the error between them. Common loss functions include mean squared error (MSE) for regression problems and cross-entropy for classification problems.
3. Backward Pass: In the backward pass, the gradients of the weights and biases in the network are calculated using the chain rule of calculus. These gradients represent the sensitivity of the network's output with respect to its weights.
4. Weight Update: The calculated gradients are then used to update the weights and biases in the network. The learning rate, which determines the step size of the weight update, is an important hyperparameter that needs to beskills in carefully chosen to ensure optimal training. This process of forward pass, calculating loss, backward pass, and weight update is repeated for a number of iterations or epochs, until the network's performance improves and converges to a satisfactory level.

Implementing Deep Learning Architectures

Once the structure of a neural network is defined, the next step is to implement it by coding the architecture in a programming language such as Python using popular deep learning libraries like TensorFlow or PyTorch.

1. Choosing a Deep Learning Library: There are several deep learning libraries available, each with its pros and cons. TensorFlow and PyTorch are two popular choices due to their extensive documentation, active communities, and strong support for neural network implementation.
2. Building the Neural Network: The neural network architecture can be built using the library's provided functions and classes. This involves defining the number of layers, the types of neurons in each layer (e.g., fully connected, convolutional, recurrent), and the activation functions to be used.
3. Initializing the Model: Once the architecture is defined, the model needs to be initialized with appropriate weights and biases. This step is crucial as it affects the network's performance during training.
4. Configuring Loss Function and Optimizer: During training, it is important to define the loss function that quantifies the difference between the predicted and actual outputs. The choice of the loss function depends on the nature of the problem being solved. Additionally, an optimizer is required to update theweights and biases in the network to minimize the loss and improve the model's accuracy. Popular optimizers include stochastic gradient descent (SGD), Adam, and RMSprop.

Resources:

1. Deep Learning from Scratch: Building with Python from First Principles. The book progresses in an extremely understandable manner from basics to advanced architectures, implementing everything from scratch.
2. If you want to start with the basic courseworkthat on this, you can start with Deep Neural Networks with PyTorch from IBM on Coursera. It’s a basic one and would be a good start.
3. For courseworkbetter visual representation of the Neural Network. Go with 3blue1brown videos on YouTube.

Practical Considerations in Deep Learning

Overfitting(Dropout): When the model performs well on the training set but does not generalize well for the test set or new data. You can prevent overfitting by including more data, using smaller networks, or by regularization.

Batch Normalization: Batch normalization involves normalizing the input to each layer to have zero mean and unit variance. This improves the gradient flow and allows faster convergence of the optimization algorithm. Additionally, batch normalization acts as a regularization technique, enabling the model to generalize better. Batch normalization (BN) eliminates the so-called internal covariate shift that affects the convergence speed of large-scale neural networks such as convolutional neural networks.

Weight Initialization: While building and training neural networks, it is crucial to initialize the weights appropriately to ensure a model with high accuracy. If the weights are not correctly initialized, it may give rise to the Vanishing Gradient problem or the Exploding Gradient problem.

Activation Functions: The activation function is a function that is attached to an artificial neural network to facilitate a network to understand complex patterns in the data. To a human neuron, the activation function would be situated somewhere after, determining which is the next to fire to the adjacent one. This is exactly the case for neurons in an ANN as well. It reads in the output signal of the previous cell and converts it into a form that the next cell can accept as input.

Vanishing Gradient: The Vanishing Gradient Problem happens when the model loses the patterns it has learned, as the gradients are so small, they become close to zero. The vanishing gradient problem is harmful. This refers to the way the model's weights are updated when the output is backpropagated through the network layers from the output layers to earlier layers. The consequences of the vanishing gradient problem are the slow convergence of the network, the network being stuck in low minima, and an impaired deep representation learning problem.

Stage 7 - Applying AI and ML in Real-world Projects

Hands-on Experience with Industry-Specific Use Cases

Hands-on experience with industry-specific use cases is crucial for becoming an AI expert. It not only helps you gain practical knowledge but also allows you to understand the unique challenges and requirements of each industry. Here are some ways to gain hands-on experience in different sectors:

1. Healthcare: Start by exploring healthcare datasets and learning about medical terminologies. You can analyze publicly available datasets, such as the MIMIC-III database, to understand how ML algorithms can be applied to predict disease outcomes or identify potential risk factors. Building healthcare-related ML models and collaborating with healthcare professionals can help you gain a deeper understanding of the industry.
2. Finance: Gain practical experience by working on financial datasets and understanding the different types of financial data, such as historical stock prices or credit card transactions. You can build ML models for predicting stock market trends or detecting fraudulent transactions. Exploring algorithms like XGBoost or LSTM (Long Short-Term Memory) for time series forecasting can be beneficial in understanding the complexities of financial data analysis.
3. Retail: E-commerce platforms provide valuable datasets for building ML models. You can start by analyzing customer behavior data and building recommendation systems that personalize product recommendations based on user preferences. Additionally, understanding inventory management and demand forecasting can be valuable skills inAssistant the retail industry. By analyzing historical sales data and external factors like seasonality and promotions, you can develop ML models to forecast demand accurately. This can help retailers optimize their inventory levels, minimize stockouts, and ultimately improve customer satisfaction.

Stage 8/Stage 0 - The Modern AI Landscape: API Requests, Not Development

Today, when people talk about building AI, they often mean utilizing existing AI models and adapting them for specific tasks through API requests, rather than developing AI models from scratch. This is because developing a completely novel AI model is an extensive process, requiring significant expertise and computational resources.

The current AI landscape is dominated by large language models, such as ChatGPT or Cohere's Command model, which are trained on massive amounts of text data and can generate human-like responses. These models are made accessible to developers and businesses through APIs, allowing for easy integration into various applications.

For example, a business may want to create a chatbot for its website. Instead of developing an AI model from the ground up, they can utilize an existing large language model through an API. They would provide the model with specific training data related to their business, fine-tuning the model to respond accurately to customer inquiries. This way, they can quickly deploy a sophisticated chatbot without needing to develop AI from scratch.

This shift towards API requests has lowered the barrier to entry for businesses and developers looking to incorporate AI into their products. It has also led to a focus on data collection and model fine-tuning, as the quality of the data used to train and adapt these large language models is critical to their performance.

Resources:

1. OpenAI
2. HuggingFace
3. AI Studios of Popular Cloud Service Providers. Example: Azure's AI Studio

Applications of AI

Computer vision

Computer vision enables machines to interpret and understand visual information from images or videos. It’s used in facial recognition, object detection, and even self-driving cars.

Customer service

AI-powered customer service uses chatbots and virtual agents to assist customers with inquiries, troubleshooting, and support. It’s available 24/7 and can handle routine customer interactions efficiently.

Take LTS Group’s Personal Document Recognition and Information Extraction System for instance. The system utilizes identification rules and image processing technology through Google Vision to automatically recognize and extract content from personal documents. This ensures high accuracy and the ability to process a large volume of documents efficiently.

Virtual Assistants

Virtual assistants like Siri, Alexa, and Google Assistant use NLP to perform tasks, answer questions, set reminders, and control smart devices.

For example, LTS Group has developed and deployed an AI-powered Travel Chatbot for our business client in the tourism industry. It provides details on popular destinations, itineraries, activities, and tailored travel recommendations. It also offers information on hotels, restaurants, shopping, and allows for online reservations and pricing comparisons.

Predictive analysis

Predictive analysis uses historical data and AI algorithms to forecast future trends or outcomes. It’s used in various fields, including finance for predicting stock prices and in healthcare for disease outbreak predictions.

Recommendation

Recommendation systems use AI to suggest products, content, or services to users based on their preferences and behavior. They help users discover new things they might like, such as movie recommendations on streaming platforms or product suggestions on e-commerce websites.

Personalization

Personalization tailors content, services, or product recommendations to individual user preferences. It enhances user experiences, like personalized newsfeeds and product offerings.

Fraud prevention

AI is employed to detect and prevent fraudulent activities, such as credit card fraud and identity theft, by analyzing transaction patterns and user behavior to identify anomalies.

Data mining

Data mining uses AI and ML models to discover patterns and insights in large datasets, or big data. AI and ML’s scalability, pattern recognition, automation, speed, and generalization capabilities make them essential for extracting insights and making data-driven decisions from big data, where manual analysis might be impractical or inefficient.

Artificial Intelligence Job Profiles

According to the job site Indeed, the demand for AI skills has more than doubled over the past three years, and the number of job postings is up by 119%. This Artificial Intelligence Tutorial will be incomplete without the different job profiles. So, if Artificial Intelligence appeals to you and you want a Job in the AI field, then here are the different Job Profiles you can apply for if you have AI Skills -

1. Machine Learning Engineer

Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Artificial intelligence is the goal of a machine learning engineer.

They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take action, without being specifically directed to perform those tasks, and according to Glassdoor, their average salary can range from $97L to $100K.

2. Data Scientist

Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc.

The data scientist role is a position for specialists. You can specialize in different types of skills like speech analytics, text analytics (NLP), image processing, video processing, medicine simulations, material simulation, etc. Each of these specialist roles is very limited in number and hence the value of such a specialist is immense, according to Glassdoor their average salary can range from $90k - $100k

3. AI Developer

AI developers need strong programming skills to create algorithms and AI applications. They frequently collaborate with others, including data scientists, to build solutions to specific business concerns. The average salary is around $108

4. AI Engineer

AI engineers, on the other hand, focus on engineering and deployment, which requires a robust foundation in system infrastructure and architecture. AI engineers also should be familiar with scaling models to support their endeavours in deploying and optimizing models, ensuring they provide optimal performance. For the average salary check out this post on Coursera.

5. Business Intelligence Developer

A Business Intelligence developer spends a lot of time researching and planning solutions for existing problems within the company. The Business Intelligence Developer is responsible for aggregating data from multiple sources in an efficient data warehouse and designing enterprise-level solutions for very large multidimensional databases.

Business intelligence developers play a key role in improving the efficiency and profitability of a business. It’s a career that’s in high demand and commands an annual average salary of $86,459 and $110,416

6. AI Research Scientist

AI Research scientists are responsible for designing, undertaking and analyzing information from controlled laboratory-based investigations, experiments, and trials. You could work for government laboratories, environmental organizations, specialist research organizations or universities and earn an average salary of $130,532

7. Big Data Engineer/Architect

Big data engineers and architects have among the best-paying, life cycle jobs in artificial intelligence. In fact, they command an annual average salary of $86k - $100k.

The Big Data solutions architect is responsible for managing the full life cycle of the solution (Hadoop lifecycle). This includes creating the requirements analysis, the platform selection, the design of the technical architecture, the design of the application design and development, testing, and deployment of the proposed solution.

Why Artificial Intelligence and Machine Learning are Essential Skills

As mentioned in the background info, developing a strong foundation in mathematics, statistics, and programming is essential for mastering Artificial Intelligence (AI) and Machine Learning (ML). These skills serve as the building blocks for understanding complex algorithms and models in the field.

Furthermore, networking and continuous learning are crucial components of becoming an expert in AI and ML. Engaging with the AI/ML community through online forums, attending meetups and conferences, collaborating with other experts, and participating in hackathons can provide valuable insights and opportunities for growth.

Additionally, pursuing higher education such as advanced degrees in AI/ML can offer in-depth knowledge and research opportunities to further enhance expertise in the field. Remember that the journey to becoming an expert in AI and ML requires dedication, perseverance, and a commitment to continuous learning.

In conclusion, mastering AI and ML skills involves a structured roadmap that includes building a strong foundation, networking with the community, continuous learning, and pursuing advanced education. By following these steps and staying updated with the latest advancements in the field, individuals can navigate the path to becoming experts in AI and ML.

In today's rapidly evolving technological landscape, artificial intelligence (AI) and machine learning (ML) have become prominent fields that are driving innovation across industries. With their ability to analyze massive amounts of data, make intelligent predictions, and automate tasks, AI and ML are transforming the way we live, work, and interact.

There is a growing demand for professionals who possess expertise in AI and ML. Companies are seeking individuals who can leverage these technologies to gain insights, optimize operations, and improve decision-making. According to a report by Gartner, AI will create 2.3 million new jobs by 2025. This indicates that becoming an AI expert can open up a world of opportunities.

Moreover, AI and ML have the potential to address some of the world's most pressing challenges. From healthcare and finance to transportation and agriculture, these technologies are being applied to solve complex problems and make significant advancements. By becoming an AI expert, you can contribute to shaping the future and making a positive impact in various domains.

Not only do AI and ML offer promising career prospects, but they also provide intellectual stimulation. These fields are at the forefront of innovation, constantly pushing boundaries and expanding the possibilities of what technology can achieve. As an AI expert, you will have the/ opportunity to be part of groundbreaking research and developments that have the potential to shape the future of AI and ML.

Continuous Learning and Staying Updated in the Field

AI is an ever-growing field, stay updated with the latest developments in the industry, and follow some influential professionals on social media like X, and Linkedin. Read research papers for a better understanding of the algorithms, follow blog posts on the medium, subscribe to email newsletters, and listen to AI podcasts. Participate in the online ML challenges on Kaggle, where you can solve Kagglereal-world problems, and also earn money.

Some newsletter to follow: TLDR AI, Alpha Signal

Podcast: AI in Business, The AI podcast

We ourselves have a podcast, "Tech My Breath Away" where we talk to emerging innovators. Infact, upon release we were ranked #7 in Technology in India.

Authors

This article is written by Gaurav Sharma, a student of 123 of AI's AlphaML and SigmaML programs, and edited by the 123 of AI team.