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Machine Learning and Data Science Compendium
  • Introduction
  • Supervised Learning
    • Linear Regression
    • Perceptron
    • Logistic Regression
    • Bayes Classifiers
    • K-Nearest Neighbors
    • Support Vector Machines
    • Decision Trees
  • Ensemble Methods
    • Random Forest
    • AdaBoost
    • Gradient Boosting
    • XGBoost
  • Unsupervised Learning I
    • K-Means Clustering
    • Hierarchical Clustering
    • Affinity Propagation
    • DBSCAN
    • Gaussian Mixture Model (GMM)
    • Mean-Shift Clustering
    • Spectral Clustering
  • Unsupervised Learning II
    • Principal Components Analysis (PCA)
    • Singular Value Decomposition (SVD)
    • t-SNE
    • UMAP
  • Statistical Methods
    • Hypothesis Testing
    • Maximum Likelihood Estimation
    • MAP Estimation
  • Time Series Analysis and Forecasting
    • Exponential Smoothing
    • ARIMA
    • GARCH
  • Deep Learning
    • Artificial Neural Networks
    • Backpropagation
  • Reinforcement Learning
    • Explore-Exploit Dilemma
    • Dynamic Programming
    • Monte Carlo
    • Temporal-Difference Learning
  • Probabilistic Models
    • Hidden Markov Models (HMM)
  • Computer Vision
  • Natural Language Processing (NLP)
  • Practical Machine Learning
    • Train Set vs. Test Set
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Introduction

Introduction#

Welcome to the Machine Learning and Data Science Compendium by the Lazy Programmer.

Check out the table of contents below, or using the sidebar.

  • Supervised Learning
  • Ensemble Methods
  • Unsupervised Learning I
  • Unsupervised Learning II
  • Statistical Methods
  • Time Series Analysis and Forecasting
  • Deep Learning
  • Reinforcement Learning
  • Probabilistic Models
  • Computer Vision
  • Natural Language Processing (NLP)
  • Practical Machine Learning

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Supervised Learning

By The Lazy Programmer
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