Machine Learning

The Machine Learning category introduces the fundamental ideas and techniques behind machine learning using Python. The focus is on understanding how models learn from data, how to train and evaluate them, and how to choose the right approach for a given problem.

Rather than jumping straight into complex theory, this category emphasizes practical learning with simple, interpretable models and real datasets.

What You’ll Learn

In this category, you’ll learn how machine learning works and how to apply it using Python.

By the end of Machine Learning, you’ll be able to:

  • Understand the difference between common machine learning tasks
  • Train and evaluate basic machine learning models
  • Prepare data for model training
  • Interpret model results and performance
  • Avoid common beginner mistakes in machine learning

Learning Path

Introduction to Machine Learning

Learn what machine learning is, how it differs from traditional programming, and where it is commonly used. Lessons include:

  • What Is Machine Learning?
  • Types of Machine Learning Tasks
  • Supervised vs Unsupervised Learning
  • Common Machine Learning Use Cases
  • The Machine Learning Workflow

Preparing Data for Machine Learning

Learn how data is structured for training models and why data quality matters. Lessons include:

  • Training, Validation, and Test Sets
  • Feature Scaling and Normalization
  • Handling Categorical Data
  • Avoiding Data Leakage
  • Building Reproducible Datasets

Core Machine Learning Algorithms

Learn how common algorithms work and when to use them. Lessons include:

  • Linear and Logistic Regression
  • k-Nearest Neighbors (kNN)
  • Decision Trees
  • Support Vector Machines (SVM)
  • Naive Bayes Classifiers

Model Training and Evaluation

Learn how to measure model performance and improve results. Lessons include:

  • Model Training Basics
  • Evaluation Metrics (Accuracy, Precision, Recall)
  • Confusion Matrices
  • Cross-Validation
  • Detecting Overfitting and Underfitting

Improving and Comparing Models

Learn how to refine models and compare different approaches. Lessons include:

  • Hyperparameter Tuning Basics
  • Feature Selection Techniques
  • Model Comparison Strategies
  • Bias–Variance Tradeoff
  • When to Stop Improving a Model

Who This Category Is For

This category is ideal if you:

  • Have basic Python and data-handling skills
  • Want to understand machine learning from the ground up
  • Prefer practical explanations over heavy math
  • Want to build models you can explain and trust

Start with the introduction and progress through the lessons in order. Each lesson builds on previous concepts and includes practical examples to reinforce learning.