Machine Learning Tutorial

Getting Started

1. 1Machine Learning Introduction
2. 2Types of Machine Learning
3. 3The ML Workflow with scikit-learn

Preparing Data

1. 4Preparing Data with pandas
2. 5Train/Test Split
3. 6Scaling & Encoding Features

Regression

1. 7Linear Regression
2. 8Regression Metrics
3. 9Polynomial Features & Regularisation

Classification

1. 10Logistic Regression
2. 11Classification Metrics
3. 12k-Nearest Neighbours
4. 13Decision Trees
5. 14Random Forests & Gradient Boosting
6. 15Support Vector Machines
7. 16Naive Bayes

Unsupervised Learning

1. 17Clustering: k-means & DBSCAN
2. 18Dimensionality Reduction with PCA

Building Better Models

1. 19Feature Engineering
2. 20Pipelines & ColumnTransformer
3. 21Overfitting & the Bias-Variance Trade-off
4. 22Cross-Validation & Hyperparameter Tuning
5. 23Imbalanced Data
6. 24Saving & Loading Models

Neural Networks & Generative AI

1. 25Neural Networks with MLPClassifier
2. 26Deep Learning: CNNs, RNNs & Transformers
3. 27PyTorch, TensorFlow & Keras
4. 28LLMs & Generative AI

ML in Practice

1. 29Responsible AI
2. 30MLOps Basics
3. 31Old vs New scikit-learn
4. 32Project: Salary Estimator