Machine Learning Study Plan
A comprehensive 14-week curriculum covering fundamental and advanced topics in machine learning with interactive Framer Motion visualizations.
Recommended Study Path
Prerequisites
Foundations Study Plan
- Python Foundations
- NumPy & Data Wrangling
- Mathematical Foundations
Complete the Foundations study plan first →
Foundations
Weeks 1-4
- Ch 1-2: Intro & Linear Regression
- Ch 3-4: Classification & Evaluation
Core concepts: regression, classification, metrics
Core ML
Weeks 5-8
- Ch 5-6: Trees & Ensembles
- Ch 7-8: SVM & Clustering
Decision trees, random forests, K-means
Advanced
Weeks 9-14
- Ch 9-10: Neural Networks & CNNs
- Ch 11-13: NLP, RL & Genertic AI
Deep learning, NLP, reinforcement learning
All Chapters
Introduction to ML
What is machine learning? Types of learning, the ML pipeline, and understanding bias-variance tradeoff.
Linear Regression
Your first ML algorithm: predicting continuous values with linear models, gradient descent, and regularization.
Classification
Binary and multiclass classification with logistic regression, sigmoid function, and feature engineering.
Model Evaluation
How to properly evaluate ML models: metrics, cross-validation, and hyperparameter tuning.
Decision Trees
Tree-based models: intuitive, interpretable, and powerful for both classification and regression.
Ensemble Methods
Combining multiple models: Random Forests, AdaBoost, Gradient Boosting, and model stacking.
Support Vector Machines
Maximum margin classifiers: linear SVM, soft margin, the kernel trick, and support vector regression.
Clustering
Unsupervised learning: K-Means, hierarchical clustering, DBSCAN, and cluster evaluation methods.
Neural Networks
Deep learning fundamentals: perceptrons, MLPs, backpropagation, activation functions, and optimizers.
CNNs & Sequence Models
Specialized architectures: convolutions, pooling, RNNs, LSTMs, and attention mechanisms.
NLP Fundamentals
Machine learning for text: preprocessing, embeddings, text classification, and transformer models.
Reinforcement Learning
Learning from interaction: MDPs, Q-learning, policy gradients, and deep reinforcement learning.
Generative & Production ML
Generative models (autoencoders, VAEs, GANs) and MLOps basics for production deployment.
Timed Tests
Practice Problem Sets
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.