A Comprehensive Guide to Machine Learning

Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang

Detailed resource on machine learning, data science, and artificial intelligence. Authored by experienced experts, suitable for beginners and experienced learners.

Technical TutorialsData ScienceMachine Learning

Introduction

A Comprehensive Guide to Machine Learning is a detailed resource authored by Soroush Nasiriany, Garrett Thomas, William Wang, and Alex Yang. The book provides comprehensive insight into machine learning, data science, and artificial intelligence.

Highlights

Detailed coverage of machine learning, data science, and artificial intelligence
Written by experienced authors in the field
Comprehensive resource for learning and understanding the subject matter

Recommendation

This course is recommended for anyone interested in gaining a thorough understanding of machine learning, data science, and artificial intelligence. It is suitable for both beginners and experienced learners in the field.

How GetVM Works

Learn by Doing from Your Browser Sidebar

Access from Browser Sidebar

Access from Browser Sidebar

Simply install the browser extension and click to launch GetVM directly from your sidebar.

Select Your Playground

Select Your Playground

Choose your OS, IDE, or app from our playground library and launch it instantly.

Learn and Practice Side-by-Side

Learn and Practice Side-by-Side

Practice within the VM while following tutorials or videos side-by-side. Save your work with Pro for easy continuity.

Explore Similar Hands-on Tutorials

Getting Started with Artificial Intelligence , 2nd Edition 25

Technical TutorialsData ScienceMachine Learning

Comprehensive introduction to AI, covering machine learning and data science. Practical guide to building enterprise applications with real-world examples.

Machine Learning For Dummies, IBM Limited Edition 19

Technical TutorialsData ScienceMachine Learning

Comprehensive guide to machine learning and data science, suitable for beginners and experienced professionals. Authored by experts Daniel Kirsch and Judith Hurwitz.

A Programmers Guide to Data Mining 14

Technical TutorialsData SciencePython

Comprehensive guide to data mining techniques, including recommendation systems, classification, and clustering. Beginner-friendly introduction for programmers with hands-on exercises and Python code.

Data Mining Concepts and Techniques 25

Technical TutorialsData ScienceMachine Learning

Comprehensive coverage of data mining concepts and techniques, including data preprocessing, classification, clustering, and association rule mining. Essential resource for students, researchers, and professionals in data mining, machine learning, and data analysis.

Foundations of Data Science 5

Technical TutorialsComputer ScienceData Science

Dive into the core principles and techniques of data science with this comprehensive course by renowned experts. Gain a strong foundation in algorithms, machine learning, and more.

Fundamentals of Data Visualization 4

Technical TutorialsData AnalysisData ScienceData Visualization

Comprehensive guide to understanding the principles and techniques of data visualization, covering design, perception, and communication of visual data. Practical insights and tools for creating effective visualizations.

Hands-On Data Visualization 9

Technical TutorialsData ScienceJavaScript

Comprehensive guide to data visualization techniques and best practices. Learn to design interactive charts and customized maps for your website using free and easy-to-learn tools.

High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications 10

Technical TutorialsComputer ScienceData ScienceMathematics

Comprehensive exploration of high-dimensional data analysis, covering real-world applications in medical imaging, computer vision, and more. Valuable resource for researchers and practitioners.

Mining of Massive Datasets 28

Technical TutorialsData Science

Comprehensive guide to data mining, machine learning, and analysis of massive datasets, including techniques for similarity search, data-stream processing, and graph analysis.