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A comprehensive 14-week curriculum covering fundamental and advanced topics in machine learning with interactive Framer Motion visualizations.
Foundations Study Plan
Complete the Foundations study plan first →
Weeks 1-4
Core concepts: regression, classification, metrics
Weeks 5-8
Decision trees, random forests, K-means
Weeks 9-14
Deep learning, NLP, reinforcement learning
What is machine learning? Types of learning, the ML pipeline, and understanding bias-variance tradeoff.
Your first ML algorithm: predicting continuous values with linear models, gradient descent, and regularization.
Binary and multiclass classification with logistic regression, sigmoid function, and feature engineering.
How to properly evaluate ML models: metrics, cross-validation, and hyperparameter tuning.
Tree-based models: intuitive, interpretable, and powerful for both classification and regression.
Combining multiple models: Random Forests, AdaBoost, Gradient Boosting, and model stacking.
Maximum margin classifiers: linear SVM, soft margin, the kernel trick, and support vector regression.
Unsupervised learning: K-Means, hierarchical clustering, DBSCAN, and cluster evaluation methods.
Deep learning fundamentals: perceptrons, MLPs, backpropagation, activation functions, and optimizers.
Specialized architectures: convolutions, pooling, RNNs, LSTMs, and attention mechanisms.
Machine learning for text: preprocessing, embeddings, text classification, and transformer models.
Learning from interaction: MDPs, Q-learning, policy gradients, and deep reinforcement learning.
Generative models (autoencoders, VAEs, GANs) and MLOps basics for production deployment.
Sharpen your skills with coding challenges and system design problems.
Curriculum designed to take you from ML fundamentals to advanced deep learning and production deployment.