Python for Machine Learning

A practical introduction to machine learning with Python and scikit-learn — data preprocessing, feature engineering, supervised learning (regression and classification), model evaluation, ensembles, unsupervised learning, hyperparameter tuning, a primer on neural networks, and deployment basics. Assumes the Python and Python for Data Analysis notebooks as prerequisites.

Introduction to Machine Learning with Python

What is Machine Learning?Setting Up Your ML Environment

Data Preprocessing for ML

Train/Test Split and Why It Matters Scaling and Encoding Features

Feature Engineering and Selection

Creating and Transforming Features Feature Selection Techniques

Supervised Learning: Regression

Linear Regression Regression Evaluation Metrics

Supervised Learning: Classification

Logistic Regression and Decision Trees Classification Evaluation Metrics

Model Evaluation and Cross-Validation

K-Fold Cross-Validation Overfitting, Underfitting, and the Bias-Variance Tradeoff

Ensemble Methods

Random Forests Gradient Boosting

Unsupervised Learning: Clustering

K-Means Clustering Hierarchical Clustering

Dimensionality Reduction

Principal Component Analysis (PCA)The Curse of Dimensionality

Hyperparameter Tuning

Grid Search and Randomized Search Pipelines for Clean ML Workflows

Introduction to Neural Networks and Deep Learning

How Neural Networks Work An Overview of Deep Learning Frameworks

Model Deployment Basics

Saving and Loading Models Serving a Model with a Simple API

Machine Learning Best Practices and Pitfalls

Data Leakage and How to Avoid It Bias, Fairness, and Responsible ML

Python for Machine Learning — scikit-learn, Models, and Deployment | DevNotes