Machine Learning
Posts and notes about machine learning.
Series & Posts
1
What is machine learning: a map of the field
2 Data, features, and the ML pipeline
3 Linear regression
4 Bias, variance, and the tradeoff
5 Regularization: Ridge, Lasso, and ElasticNet
6 Logistic regression and classification
7 Evaluation metrics for classification
8 Naive Bayes classifier
9 K-Nearest Neighbors
10 Decision trees
11 Ensemble methods: Bagging and Random Forests
12 Boosting: AdaBoost and Gradient Boosting
13 Support Vector Machines
14 K-Means clustering
15 Dimensionality Reduction: PCA
16 Gaussian mixture models and EM algorithm
17 Model selection and cross-validation
18 Feature engineering 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