100% Free Companion Notes & Implementation Guides
Master AI & Machine Learning from First Principles.
Clear, intuitive lecture notes, visual mathematical breakdowns, and production-ready code guides designed to complement every Decode AIML YouTube tutorial.
Why learn with Decode AIML
Built for understanding, not just memorizing.
Mathematical Intuition
Deep-dive geometric and algebraic breakdowns behind every core algorithm—from echelon forms to gradient descent.
Clean, Distilled Notes
Skip the endless pausing. Get structured HTML notes, key formulas, and takeaways designed for fast review.
Production Code Snippets
Clean Python, NumPy, and PyTorch implementations ready to copy, test, and run locally.
Interactive Practice (Coming Soon)
Quizzes, coding sandboxes, and problem sets coming soon to help reinforce your understanding.
Learning Tracks
Pick a track and follow the lectures in order. Scroll sideways to see all 8.
Linear Algebra for ML
- Vector Spaces & Subspaces
- Linearly Dependent & Independent Vectors
- Basis & Span
- Systems of Linear Equations (Ax = b)
- Matrix Echelon Forms & Gaussian Elimination
- Matrix Inverses & Gauss-Jordan Method
- Eigenvalues & Eigenvectors
- Properties & Applications of Eigenvalues
Classical Machine Learning | Basic to Advanced
- Machine Learning Lifecycle & Project Workflow
- Linear & Logistic Regression
- Gradient Descent Intuition
- K-Nearest Neighbors (KNN) & Distance Metrics
- Curse of Dimensionality & KD-Trees
- Naive Bayes (Multinomial, Bernoulli, Gaussian)
- Laplace Smoothing & Log-Space Calculations
- Decision Trees & Hyperparameter Tuning
- Ensemble Learning (Bagging, Boosting, Stacking)
- Random Forest, AdaBoost, XGBoost, LightGBM, CatBoost
- Support Vector Machines (SVM)
Core Python - Basic to Advanced
- Environment Setup & Virtual Environments
- Core Programming Fundamentals
- Data Structures & Object-Oriented Python
- Advanced Pythonic Concepts for Data Science
Complete NumPy & Pandas
- NumPy Array Operations & Vectorization
- Pandas Series & DataFrames
- Data Cleaning & Manipulation
- Handling Missing Values, Duplicates, and Outliers
Calculus and Optimization
- Limits, Derivatives, and Partial Derivatives
- Gradient Vectors and Hessian Matrices
- Convex Optimization
- Optimization Algorithms in ML
Statistics and Probability
- Descriptive & Inferential Statistics
- Probability Distributions
- Hypothesis Testing
- Statistical Significance & P-Values
Deep Learning & Neural Networks
- Forward and Backward Propagation
- Loss Functions & Activations
- CNNs & RNN Architectures
- Transformers & Self-Attention Mechanisms
Data Science & Analytics
- Exploratory Data Analysis (EDA)
- Feature Engineering & Dimensionality Reduction (PCA)
- Data Visualization Patterns & Correlations