An Introduction to Machine Learning

Welcome to an introduction to machine learning we embark on a captivating voyage through the foundations and frontiers of machine learning, unraveling the complexities that empower computers to learn, adapt, and make decisions on their own.

We’ll navigate through the basics of math, statistics, and programming, providing you with the tools necessary to comprehend and implement the core principles of machine learning. From data engineering to the algorithms that define the landscape of artificial intelligence, this book serves as your entry point into the fascinating world where data transforms into actionable insights.

What sets this guide apart are the hands-on experiences woven seamlessly into each main topic. Core exercises will sharpen your skills, and substantial projects will not only solidify your understanding but also serve as valuable assets in your professional portfolio. Whether you’re aspiring to be a Data Scientist, Machine Learning Engineer, or simply aiming to augment your skills, this book offers a structured and engaging approach.

Check out the content pages bundled with this book to see more.

• 1. Introduction to Machine Learning
• 2. Big Data - Intro Data Engineering using Spark and Hadoop
• 3. Data Preprocessing
• 4. Supervised Learning
• 5. Unsupervised Learning
• 6. Deep Learning and Neural Networks
• 7. Reinforcement Learning
• 8. Computer Vision
• 9. Natural Language Processing
• 10. Time Series

License and Copyright Notice

The contents of this book are licensed for free consumption under the following license: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Contribute

This is an open-source material on machine learning, covering the foundations of machine learning and its new applications. The goal is to make it accessible to everyone, utilizing only free software. If you find this book valuable and wish to support it, you can become a member of my YouTube channel. However, if you are not ready to support me financially, you can still subscribe to my YouTube channel, give a star on Github, or help by fixing typos, suggesting edits, or providing feedback.

References

In all sections, we will have other references to check out. The main references in Machine Learning/Artificial intelligence are:

• [Mitchell, 1997]

• [Hastie et al., 2009]

• [Russell et al., 2020]

• [Sutton and Barto, 2018]

For Big Data and Data Engineering

• [Chambers and Zaharia, 2018]

• [White, 2012]

[CZ18]

B. Chambers and M. Zaharia. Spark: The Definitive Guide: Big Data Processing Made Simple. O'Reilly Media, 2018. ISBN 9781491912300. URL: https://books.google.com.br/books?id=oitLDwAAQBAJ.

[HTF09]

T. Hastie, R. Tibshirani, and J.H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. Springer, 2009. ISBN 9780387848846. URL: https://books.google.com.br/books?id=eBSgoAEACAAJ.

[Mit97]

Tom M Mitchell. Machine learning. Volume 1. McGraw-hill New York, 1997.

[RRN20]

S.J. Russell, S. Russell, and P. Norvig. Artificial Intelligence: A Modern Approach. Pearson series in artificial intelligence. Pearson, 2020. ISBN 9780134610993. URL: https://books.google.com.br/books?id=koFptAEACAAJ.

[SB18]

R.S. Sutton and A.G. Barto. Reinforcement Learning, second edition: An Introduction. Adaptive Computation and Machine Learning series. MIT Press, 2018. ISBN 9780262039246. URL: https://books.google.com.br/books?id=sWV0DwAAQBAJ.

[Whi12]

T. White. Hadoop: The Definitive Guide. O'Reilly Media, 2012. ISBN 9781449338770. URL: https://www.oreilly.com/library/view/hadoop-the-definitive/9780596521974/.