Mathe's for Machine Learning – Handwriting Complete Notes

What you will learn

• ✓Understand mathematical foundations required for Machine Learning and AI.
• ✓Learn algebra, functions, exponents and logarithms.
• ✓Understand vectors, matrices and linear transformations.
• ✓Perform dot product, cosine similarity and vector operations.
• ✓Understand eigenvalues, eigenvectors and Singular Value Decomposition.
• ✓Learn calculus, derivatives, partial derivatives and gradients.
• ✓Understand chain rule and neural network backpropagation.
• ✓Apply optimization concepts including Gradient Descent, SGD and Adam.
• ✓Understand probability, conditional probability and Bayes' Theorem.
• ✓Learn probability distributions, expectation, variance and standard deviation.
• ✓Understand statistical estimation, MLE and MAP.
• ✓Learn entropy, cross-entropy and KL divergence.
• ✓Understand numerical stability in machine learning computations.
• ✓Apply least squares and regression mathematics.
• ✓Understand L1, L2 regularization and the bias-variance trade-off.
• ✓Learn PCA and dimensionality reduction techniques.
• ✓Connect mathematical concepts with embeddings, softmax and Transformer attention.
• ✓Prepare for AI and Machine Learning technical interviews.