Machine Learning in Code | Incognition

Machine Learning in Code

Build real machine learning models in Python with scikit-learn — preparing features, training, evaluating and tuning — the code form of everything the concept courses taught.

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One Pattern for Every Model

Features and Labels as X and y

Built-In Practice Datasets

Splitting the Data

Your First Model

Asking for Predictions

Classify Iris Flowers

Scaling Features

Fit on Training Data Only

Encoding Categories

Target Encoding

Different Columns, Different Steps

A Leak-Free Pipeline

Gradient Descent by Hand

Linear Regression

More Than One Feature

Curves With Straight-Line Tools

Measuring Regression Error

Too Simple, Too Complex

Predict Diabetes Progression

Logistic Regression

K-Nearest Neighbours

Support Vector Machines

Compare Classifiers on Breast Cancer Data

Accuracy and Its Trap

The Confusion Matrix

Precision, Recall and F1

ROC Curves and AUC

When One Class Is Rare

Cross-Validation

Choose a Metric and Defend It

Hyperparameters in Code

Learning and Validation Curves

Feature Importance

Selecting Features

Tune, Select, and Test Once

Hierarchical Clustering

Find Groups in Wine Data

Which Features Mattered?

How a Feature Moves a Prediction

Explaining One Prediction

Explain a Model to Someone Else