Machine Learning Tutorial

1. Machine Learning Introduction

What machine learning is, and supervised, unsupervised and reinforcement learning explained with concrete examples.

2. The ML Workflow

The real end-to-end ML workflow: collecting and cleaning data, splitting, training, evaluating, deploying and monitoring.

3. Feature Engineering

Encoding categorical variables, scaling and normalization, and handling missing data with real examples.

4. Common ML Algorithms

Linear and logistic regression, decision trees and k-means clustering, explained with intuition and real use cases.

5. Neural Networks Basics

The perceptron, stacking layers into a network, activation functions, and backpropagation at a high level.

6. Overfitting & Evaluation Metrics

Overfitting vs underfitting, and evaluating models with accuracy, precision, recall, F1 and cross-validation.

7. Regularization & Hyperparameter Tuning

L1/L2 regularization, dropout, grid vs random search, and cross-validation for tuning models properly.

8. Model Deployment & MLOps

Serving a model online or in batch, versioning models and training data, and monitoring for drift.

9. Machine Learning Interview Questions

Commonly asked machine learning interview questions with clear, practical answers.