Julia for Machine Learning
Chapter 1: Getting Started with Julia for Machine Learning
Why Julia for Machine Learning? Strengths and Comparisons
Setting Up Your Julia Machine Learning Environment
Julia's Type System and Multiple Dispatch for Scientific Computing
Essential Julia Syntax for Data Operations
Working with Arrays and Matrices in Julia
Introduction to DataFrames.jl
Package Management with Pkg.jl
Hands-on practical: Environment Setup and Basic Data Operations
Chapter 2: Data Manipulation and Preparation in Julia
Loading and Saving Data with DataFrames.jl
Data Cleaning: Handling Missing Values and Outliers
Data Transformation: Scaling, Encoding, and Binning
Feature Engineering Principles
Applying Feature Engineering in Julia
Data Visualization with Plots.jl and Makie.jl
Hands-on practical: Data Cleaning and Feature Creation
Chapter 3: Implementing Supervised Learning Algorithms
Overview of the MLJ.jl Ecosystem
Building and Training Linear Models with MLJ.jl
Implementing Decision Trees and Ensemble Methods
Support Vector Machines (SVMs) using Julia Packages
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
Cross-Validation and Hyperparameter Tuning in MLJ.jl
Hands-on practical: Training and Evaluating Supervised Models
Chapter 4: Unsupervised Learning and Dimensionality Reduction
Clustering with K-Means in Julia
Density-Based Clustering: DBSCAN
Principal Component Analysis (PCA) for Dimensionality Reduction
Other Dimensionality Reduction Techniques
Evaluating Clustering Performance
Hands-on practical: Applying Clustering and PCA
Chapter 5: Building Machine Learning Pipelines and Workflows
Introduction to MLJ Pipelines
Constructing Preprocessing and Model Pipelines
Saving and Loading Trained Models and Pipelines
Composing and Tuning Complex Workflows
Strategies for Reproducible ML Experiments in Julia
Hands-on practical: Creating and Managing ML Pipelines
Chapter 6: Introduction to Deep Learning with Flux.jl
Fundamentals of Neural Networks
Getting Started with Flux.jl: Tensors and Layers
Building Feedforward Neural Networks
Defining Loss Functions and Optimizers
Training Neural Networks in Flux.jl
Automatic Differentiation with Zygote.jl
Working with Gradients
Hands-on practical: Building and Training a Simple Neural Network
Implementing Decision Trees and Ensemble Methods
Was this section helpful?
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A classic textbook offering a statistical treatment of decision trees, random forests, and gradient boosting.
- Random Forests, Leo Breiman, 2001 Machine Learning, Vol. 45 DOI: 10.1023/A:1010933404324 - The original research paper that introduced the Random Forests algorithm.
- Greedy Function Approximation: A Gradient Boosting Machine, Jerome H. Friedman, 2001 The Annals of Statistics, Vol. 29 (The Institute of Mathematical Statistics) DOI: 10.1214/aos/1013203451 - A foundational paper that describes the Gradient Boosting Machine algorithm.
- MLJ.jl Documentation, Anthony F. Nolan, Franz Kiraly, Samuel O. B. N. Breeding, Thibaut Lienart, Joshua Agar, Adam B. Lewandowski, Simon Byrne, Ludovic Prot, Michael Herbst, Valentin Chabriel, and Diego Arenas, 2025 - Official documentation for the MLJ.jl framework, offering guidance on implementing and using machine learning models, including decision trees and ensemble methods, in Julia.