Lilly Tech Systems
ML Most Used Algorithms
Deep dive into the 7 most important machine learning algorithms with full mathematical foundations, intuitive explanations, production-ready Python code, and real-world practical examples. Master the algorithms that power modern AI.
Your Learning Path
Follow these lessons in order to build a complete understanding, or jump to any algorithm that interests you.
1. Overview
The 7 most used ML algorithms, when to use each, algorithm selection guide, and the complexity-interpretability tradeoff.
2. Linear Regression
The foundation of ML: cost functions, gradient descent, regularization (Ridge, Lasso, ElasticNet), and evaluation metrics.
3. Logistic Regression
Classification with sigmoid, decision boundaries, binary cross-entropy, multi-class strategies, and ROC-AUC evaluation.
4. Decision Trees
Information gain, Gini impurity, entropy, the CART algorithm, pruning strategies, and tree visualization.
5. Random Forest
Ensemble learning, bagging, random feature subsets, out-of-bag scoring, feature importance, and hyperparameter tuning.
6. Gradient Boosting
Sequential boosting, XGBoost vs LightGBM vs CatBoost, early stopping, and why boosting dominates tabular data.
7. Neural Networks
Perceptrons, activation functions, backpropagation, optimizers, and a complete PyTorch training loop.
8. Graph Neural Networks
Graph data, message passing, GCN, GAT, GraphSAGE, and applications from social networks to drug discovery.
9. Comparison
Master comparison table of all algorithms, decision guide by problem type, data size, and interpretability needs.
What You'll Learn
By the end of this course, you'll be able to:
Understand the Math
Grasp the mathematical foundations behind each algorithm — from cost functions and gradients to information theory and graph theory.
Write Production Code
Implement each algorithm in Python using scikit-learn, XGBoost, PyTorch, and PyTorch Geometric with real datasets.
Choose the Right Algorithm
Select the best algorithm for any given problem based on data type, size, interpretability requirements, and performance needs.
Tune & Optimize
Master hyperparameter tuning, regularization techniques, ensemble strategies, and model evaluation metrics for each algorithm.