Machine Learning

Supervised Learning Fundamentals

• What you Need to Know

  • Linear Models and Regression

    • Simple and multiple linear regression
    • Logistic regression for classification
    • Regularization techniques (Ridge, Lasso, Elastic Net)
    • Resources:
      • Linear Regression Tutorial - Scikit-learn linear models
      • Logistic Regression - Classification with logistic regression
      • Regularization Guide - Ridge and Lasso regression

  • Tree-Based Methods

    • Decision trees and tree construction algorithms
    • Random Forest and ensemble methods
    • Gradient boosting (XGBoost, LightGBM)
    • Resources:
      • Decision Trees - Decision tree algorithms
      • Random Forest Guide - Breiman's original random forest paper
      • XGBoost Tutorial - Gradient boosting framework

  • Instance-Based Learning

    • K-Nearest Neighbors (KNN) algorithm
    • Distance metrics and similarity measures
    • Curse of dimensionality and feature selection
    • Resources:
      • KNN Algorithm - K-nearest neighbors implementation
      • Distance Metrics - Similarity and distance functions
      • Feature Selection for KNN - Feature selection techniques

Unsupervised Learning Methods

• What you Need to Know

  • Clustering Algorithms

    • K-means clustering and centroid-based methods
    • Hierarchical clustering and dendrograms
    • Density-based clustering (DBSCAN, OPTICS)
    • Resources:
      • Clustering Guide - Comprehensive clustering algorithms
      • K-means Tutorial - K-means implementation in Python
      • Hierarchical Clustering - Hierarchical clustering with SciPy

  • Dimensionality Reduction

    • Principal Component Analysis (PCA) implementation
    • t-SNE for visualization and non-linear reduction
    • Factor analysis and independent component analysis
    • Resources:
      • PCA Tutorial - Principal component analysis
      • t-SNE Guide - t-distributed stochastic neighbor embedding
      • Dimensionality Reduction Comparison - Comparison of reduction methods

  • Association Rules and Market Basket Analysis

    • Apriori algorithm and frequent itemsets
    • Association rule metrics (support, confidence, lift)
    • Market basket analysis applications
    • Resources:
      • Association Rules - Apriori algorithm implementation
      • Market Basket Analysis - Practical market basket analysis
      • MLxtend Library - Machine learning extensions

Model Evaluation and Validation

• What you Need to Know

  • Cross-Validation Techniques

    • K-fold cross-validation and stratified sampling
    • Leave-one-out and bootstrap validation
    • Time series cross-validation for temporal data
    • Resources:
      • Cross-Validation Guide - Model validation techniques
      • Time Series CV - Time series validation methods
      • Bootstrap Methods - Bootstrap validation

  • Performance Metrics for Classification

    • Accuracy, precision, recall, and F1-score
    • ROC curves and Area Under Curve (AUC)
    • Confusion matrices and classification reports
    • Resources:
      • Classification Metrics - Classification evaluation guide
      • ROC and AUC - Google's ROC tutorial
      • Precision-Recall Curves - Classification curve analysis

  • Performance Metrics for Regression

    • Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
    • Mean Absolute Error (MAE) and R-squared
    • Residual analysis and model diagnostics
    • Resources:
      • Regression Metrics - Regression evaluation methods
      • Residual Analysis - Penn State residual diagnostics
      • Model Diagnostics - Statistical model diagnostics

Hyperparameter Tuning and Model Selection

• What you Need to Know

  • Grid Search and Random Search

    • Hyperparameter optimization strategies
    • Cross-validation for parameter selection
    • Computational efficiency and search space design
    • Resources:
      • Hyperparameter Tuning - Grid search and random search
      • Parameter Optimization - Optimization techniques
      • Bayesian Optimization - Advanced parameter optimization

  • Model Comparison and Selection

    • Bias-variance tradeoff analysis
    • Learning curves and validation curves
    • Statistical tests for model comparison
    • Resources:
      • Model Selection - Learning and validation curves
      • Bias-Variance Analysis - Bias-variance decomposition
      • Model Comparison - Statistical model comparison

Introduction to Deep Learning

• What you Need to Know

  • Neural Network Fundamentals

    • Perceptrons and multi-layer networks
    • Activation functions and backpropagation
    • Training neural networks with gradient descent
    • Resources:
      • Neural Networks Course - Andrew Ng's deep learning course
      • Neural Networks and Deep Learning - Free online neural networks book
      • TensorFlow Beginner Tutorial - Introduction to deep learning

  • Deep Learning Frameworks

    • TensorFlow and Keras for deep learning
    • PyTorch for research and experimentation
    • Model building and training workflows
    • Resources:
      • TensorFlow Tutorials - Official TensorFlow learning resources
      • PyTorch Tutorials - PyTorch framework tutorials
      • Keras Documentation - High-level neural network API

  • Deep Learning Applications

    • Convolutional Neural Networks for image data
    • Recurrent Neural Networks for sequential data
    • Transfer learning and pre-trained models
    • Resources:
      • CNN Tutorial - Convolutional neural networks
      • RNN Tutorial - Recurrent neural networks
      • Transfer Learning - Using pre-trained models

Time Series Analysis and Forecasting

• What you Need to Know

  • Time Series Decomposition

    • Trend, seasonal, and residual components
    • Additive vs multiplicative decomposition
    • Stationarity testing and transformation
    • Resources:
      • Time Series Decomposition - Forecasting book decomposition chapter
      • Statsmodels Decomposition - Time series decomposition
      • Time Series with Pandas - Time series functionality

  • Forecasting Models

    • ARIMA models and seasonal ARIMA
    • Exponential smoothing methods
    • Prophet for automated forecasting
    • Resources:
      • ARIMA Modeling - ARIMA implementation
      • Prophet Documentation - Facebook's forecasting tool
      • Time Series Forecasting - Forecasting methods comparison

Ready to Visualize Insights? Continue to Module 4: Data Visualization to master data storytelling, visualization design, and communicating insights effectively.