COURSE SCHEDULE
Daily Breakdown
Day 1: Machine Learning Foundations
ML framework, Data problems, and Splitting strategies
Day 2: Machine Learning Algorithms
Linear, Distance-based, Kernel-based, and Tree-based models
Day 3: Fundamentals of Deep Learning
Neural Networks, Deep Learning, Learning Process (Backpropagation), and Optimizers
Day 4: Unsupervised Learning
K-Means clustering, PCA, t-SNE, and Autoencoders
Day 5: Exam
Final Assessment
Extra
Any extra materials will be shared here.