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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

Chapter 3: Implementing Supervised Learning Algorithms

Supervised learning involves creating models that learn from input data paired with corresponding correct outputs. This chapter shifts to the practical implementation of these algorithms using the Julia programming language. You will work extensively with MLJ.jl, a primary framework in Julia designed to provide a unified interface for various machine learning tasks.

You will learn to:

Navigate the MLJ.jl ecosystem for model building and evaluation.
Implement and train linear models, such as linear regression for predicting continuous values and logistic regression for classification tasks.
Construct decision tree models and understand ensemble methods like Random Forests.
Utilize Support Vector Machines (SVMs) for classification problems.
Assess model performance using standard evaluation metrics, including accuracy, precision, recall, and the $F_1$ -score.
Apply cross-validation techniques for more dependable model evaluation and perform hyperparameter tuning to optimize your models within MLJ.jl.

By the end of this chapter, you will be able to select, implement, train, and evaluate several common supervised learning models in Julia.

Sections

3.1 Overview of the MLJ.jl Ecosystem
3.2 Building and Training Linear Models with MLJ.jl
3.3 Implementing Decision Trees and Ensemble Methods
3.4 Support Vector Machines (SVMs) using Julia Packages
3.5 Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
3.6 Cross-Validation and Hyperparameter Tuning in MLJ.jl
3.7 Hands-on practical: Training and Evaluating Supervised Models

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