Machine Learning
Posts and notes about machine learning.
Series & Posts
1
What is Machine Learning? Types, examples, and how it works
2 ML pipeline: data splitting, scaling, and feature preparation
3 Linear regression: normal equations and gradient descent
4 Bias-variance tradeoff: underfitting vs overfitting explained
5 Regularization: Ridge, Lasso, and ElasticNet
6 Logistic regression: sigmoid, cross-entropy, and softmax
7 Classification metrics: precision, recall, F1, and ROC-AUC
8 Naive Bayes classifier: Bayes' theorem for ML
9 K-Nearest Neighbors (KNN) algorithm explained
10 Decision trees: Gini impurity and information gain
11 Bagging and Random Forests: ensemble methods explained
12 Boosting: AdaBoost and Gradient Boosting
13 Support Vector Machines: margins, kernels, and soft margins
14 K-means clustering algorithm: formula, K-means++, elbow method, and Python
15 PCA explained: eigenvalues, variance, and dimensionality reduction
16 Gaussian mixture model (GMM): EM algorithm, clustering, and Python implementation
17 Model selection and cross-validation: K-fold, nested CV, and tuning
18 Feature engineering: polynomial features, encoding, and selection
Notes
Introduction to Neural Networks
Deep diveBasic concepts and fundamentals of neural networks for beginners
Semi-Supervised Learning Guide
Deep diveUnderstanding how to leverage both labeled and unlabeled data for better machine learning performance
Reinforcement Learning Mastery
Deep diveComplete guide to reinforcement learning concepts, algorithms, and real-world applications
Unsupervised Learning Explained
Deep diveUnderstanding unsupervised learning techniques for pattern discovery without labeled data
Supervised Learning Deep Dive
Deep diveComprehensive guide to supervised learning algorithms, techniques, and applications
Types of Machine Learning
Deep diveOverview of different machine learning approaches and when to use each
AI/ML Fundamentals
Deep diveIntroduction to Artificial Intelligence and Machine Learning core concepts