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!
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.
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