What You Need to Do

  • Understand the difference between supervised and unsupervised learning
  • Learn regression algorithms: linear regression, polynomial regression
  • Study classification algorithms: logistic regression, k-NN, decision trees
  • Explore clustering algorithms: k-means, hierarchical clustering
  • Master model evaluation techniques: train-test split, cross-validation
  • Learn about bias-variance tradeoff and regularization
  • Implement algorithms using Scikit-learn

Types of Machine Learning

Supervised Learning

Learning from labeled data (input-output pairs):

  • Regression: Predicting continuous values
  • Classification: Predicting categories

Unsupervised Learning

Finding patterns in unlabeled data:

  • Clustering: Grouping similar data points
  • Dimensionality Reduction: Simplifying data

Reinforcement Learning

Learning through trial and error with rewards:

  • Agents learn from interactions
  • Reward-based system

Learning Roadmap

Weeks 1-2

Supervised Learning: Regression algorithms

Weeks 3-4

Supervised Learning: Classification algorithms

Weeks 5-6

Unsupervised Learning: Clustering

Weeks 7-8

Model evaluation and validation techniques

Key Algorithms to Master

  • Linear Regression: For predicting continuous values
  • Logistic Regression: For classification problems
  • k-Nearest Neighbors (k-NN): Instance-based learning
  • Decision Trees: Rule-based approach to classification/regression
  • k-Means Clustering: Unsupervised grouping algorithm
  • Naive Bayes: Probabilistic classifier based on Bayes' theorem