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ML 101
Module 1
Module 1

Introduction to Machine Learning

What it is, how it works, and why it matters. This module covers the fundamentals every practitioner needs.

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ML 101
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Definition

What is ML?

A field of AI where systems learn patterns from data instead of being explicitly programmed.

Applications
10,000+
Market Size
$158B
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Core Concepts

Three Types of Learning

  • Supervised — labeled data, predict outcomes
  • Unsupervised — find hidden patterns
  • Reinforcement — learn from rewards
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Process

The Workflow

Data Collection — gather & clean
Feature Engineering — extract signals
Model Training — fit to data
Evaluation — measure accuracy
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Algorithms

Key Algorithms

  • Linear Regression — continuous predictions
  • Decision Trees — interpretable rules
  • Neural Networks — complex patterns
  • Random Forests — ensemble power
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Performance

By The Numbers

Modern ML achieves human-level performance on many tasks, from image recognition to language understanding.

Accuracy
95%+
Training
Minutes
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Caution

Common Pitfalls

Watch Out For
Overfitting, data leakage, selection bias, and insufficient validation. Always split your data into train/test sets.
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Tooling

Tools of the Trade

  • Python & scikit-learn
  • TensorFlow & PyTorch
  • Jupyter Notebooks
  • Pandas & NumPy
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Impact

Real-World Applications

  • Medical diagnosis & imaging
  • Fraud detection in banking
  • Recommendation engines
  • Autonomous vehicles
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Up Next

What's Next

Next Module
Supervised Learning: Regression & Classification
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