Math, Foundations, and Practice!

Topics Include:

• Linear Algebra
• Probability
• Python, Pandas, Numpy, Scikit-Learn, Matplotlib, EDA
• Github
• Supervised & Unsupervised Learning Algorithms
• Classification, Regression & Retrieval
• Practical Considerations (Bias, Variance, Regularisation, Scaling, Feature Selection, etc)
• Bonus sessions on MLOps
• Real-world Projects

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Notes: Classical Machine Learning

1000+ pages of notes, slides and code workbooks to accelerate your journey in mastering classical machine learning!

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P.S.: These notes, slides, and code workbooks have been curated using the Pareto principle to ensure you learn what is required to excel in the real-world!

We've built this program leveraging our experience from premier Ivy League institutions, top-tier research publications, as well building real-world AI systems.