Machine Learning for Dummies An Amazing ML Guide

If you are someone who is looking to learn Machine learning and have a passionate desire and guts to go deeper in it then the Machine Learning for Dummies is your key to absorb almost everything related to ML.

In all aspects, Machine Learning for Dummies is good except its title, in the beginning, I was very unhappy with it, no one likes if some call them dumb, but after reading it, I simply ignore it.



Machine Learning for Dummies covers almost everything about ML and build a good understanding of this subject. It is not an advanced level book but yes if a beginner wants to read it he will never get disappointed.

And also if you are someone who doesn’t even code ever in your life and just starting, still machine learning for dummies will help you allot with machine learning, the only thing is that you can’t build something with this its practical guide.




Machine Learning for Dummies is divided into six parts. If you are interested in it, you can download it from the link at bottom of this article for absolutely free.

Machine Learning for Dummies will teach you about various different types of machine learning, that include Supervised learningUnsupervised learningReinforcement learning.




In supervised learning, the machine is taught by examples, whereas in unsupervised learning the machine study data to identify patterns,  there are only input variables (X) but no corresponding output variables.

If we talk about reinforcement learning, the machine is provided with a set of actions, parameters and end values. If you want to know more about ML algorithms that divided into these three types, feel free to check out below article.

Machine Learning For Dummies explains how to get started, provides detailed discussions of how the underlying algorithms work, uses languages such as Python and R to make machine learning possible specifies how to do practical things using.

Machine Learning for Dummies also covers lots of other concepts of ML such that statistics, linear models, demystifying the math, leveraging similarity, neural networks, complexity with neural networks.

The writers also talk about support vector machines, big data and much more in this “Machine Learning For Dummies”.

Machine Learning for Dummies talks in detail about the programming languages and tools integral to machine learning, most of the codes are written in Machine Learning for Dummies are in Python and R.




In Machine Learning For Dummies, there is not so much coding, and whatever the coding is there it is in Python and R the writer has used to teach machines to find patterns and analyze results.

The knowledge in this book flows in plain and simple English.

Machine Learning for Dummies is written by John Paul Mueller and Luca Massaron. John Paul Mueller is a well-known author, he has already written 108 books and more than 600 articles, whereas Luca Massaron is a data scientist and a specialist in multivariate statistical analysis and machine learning.

Table of content of Machine Learning for Dummies:

Part 1: Introducing How Machines Learning 7

CHAPTER 1: Getting the Real Story about AI 9

CHAPTER 2: Learning in the Age of Big Data 23

CHAPTER 3: Having a Glance at the Future 35

Part 2: Preparing Your Learning Tools 45

CHAPTER 4: Installing an R Distribution 47

CHAPTER 5: Coding in R Using RStudio 63

CHAPTER 6: Installing a Python Distribution 89

CHAPTER 7: Coding in Python Using Anaconda 109

CHAPTER 8: Exploring Other Machine Learning Tools 137

Part 3: Getting Started with the Math Basics 145

CHAPTER 9: Demystifying the Math Behind Machine Learning 147

CHAPTER 10: Descending the Right Curve 167

CHAPTER 11: Validating Machine Learning 181

CHAPTER 12: Starting with Simple Learners 199

Part 4: Learning from Smart and Big Data 217

CHAPTER 13: Preprocessing Data 219

CHAPTER 14: Leveraging Similarity 237

CHAPTER 15: Working with Linear Models the Easy Way 257

CHAPTER 16: Hitting Complexity with Neural Networks 279

CHAPTER 17: Going a Step beyond Using Support Vector Machines 297

CHAPTER 18: Resorting to Ensembles of Learners 315

Part 5: Applying Learning to Real Problems 331

CHAPTER 19: Classifying Images 333

CHAPTER 20: Scoring Opinions and Sentiments 349

CHAPTER 21: Recommending Products and Movies 369

Part 6: The Part of Tens 383

CHAPTER 22: Ten Machine Learning Packages to Master 385

CHAPTER 23: Ten Ways to Improve Your Machine Learning Models 391

Download Machine Learning for Dummies for free
Start learning machine learning with this 12 free and amazing ML courses



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