February 4

# Best Machine Learning Books for Beginners

Blog, Ebooks, Resources

If you're not sure how to get started with Machine Learning, our top 3 picks of the best Machine Learning books for beginners in this blog post will help you make your first steps.

We don't stop at just 3 ML books, though.

There are loads of great machine learning books out there, so we've also collated a list of our top picks of the best python machine learning books for beginners and the best deep learning books for beginners.

Before we get into that, though, we're going to take a quick look at what Machine Learning is, why it's important and what you can do with it.

More...

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## What is Machine Learning?

For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem.

For example, imagine you want to teach a computer how to add a pair of numbers together.

The sum 3+5 has a single solution, 8, and so you can write a single line of code for the computer to understand.

That's easy.

Now consider that there an infinite number of ways in which the solution, 8, can be arrived at; 0+8, 1+7, 2+6, etc..

There are also an infinite number of solutions that can be reached by adding a pair of numbers.

I hope you can see that it's not possible to write a line of code for every possible summation, so you need an alternative method.

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data to develop their own algorithms so that they carry out certain tasks.

Instead of coding for every possible pair of summations, you could instead give the computer a sample of summations along with the corresponding answers and inviting it to 'learn' the correct answers. In this way, you will have created a Machine Learning summation algorithm that could correctly (most of the time) give you the answer to summing any pair of numbers.

If you're interested in learning more about Machine Learning, our pick of the best machine learning books for beginners below will help you with that.

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## What is Machine Learning Used For?

Artificial IntelligenceMachine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them.

They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for machine learning algorithms, and so everyone is interested in them again.

These days machine learning algorithms are being used for:

• Data Security & Fraud Detection
• Medical Diagnosis
• Marketing & Sales
• Online Search Engines
• Image & Speech Recognition
• Smart Cars & Smart Cities

You'll find out more about these in our pick of the top 3 ML books below.

3 Inspirational Machine Learning Books for Aspiring Data Scientists @chi2innovations #datascience #machinelearning

## Can I Teach Myself Machine Learning?

When I was doing my PhD in Artificial Neural Networks about 20 years ago, the answer to this question was a resounding 'No'.

There weren't any machine learning books for beginners.

You learnt about Machine Learning by reading academic papers and specialised machine learning books. You were taught by people with PhDs to do machine learning to world class standard.

It wasn't for the faint-of-heart or gifted amateurs. It was a serious academic endeavour and was incredibly difficult.

My, how the times have changed...

Now there are loads of machine learning books for beginners, python machine learning books and even deep learning books for beginners too.

The tools to practice machine learning are incredible, with everything already built for you (we had to code everything by hand back then).

Machine learning is now an exercise in plug-and-play. Just point it at your data and watch the predictions roll out.

OK, it's not quite as simple as that, and if you want to get accurate and robust models you'll need to finesse the options and learn to interpret the results correctly, but the bottom line is that its wayyyyy easier for a beginner to get started with machine learning than it used to be.

And that's what this post is about - below is our pick of the best machine learning books for beginners to help you get started.

Did you know that you can get Data Science audiobooks for FREE with an Audible Trial?

Check them out!

## Top 3: The Best Machine Learning Books For Beginners

In this post - the 6th in a series of 8 in which we bring you 21 Inspirational Books for All Aspiring Data Scientists, we highlight the best machine learning books for beginners to Data Science - or any other discipline:

These 3 ML books are all highly recommended reading and are amongst the best books to learn machine learning.

If our pick of the best machine learning books doesn't quite inspire you, there are some python machine learning books and deep learning books for beginners further down the post.

Enjoy!

#1

4.8 of 5 stars

### Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

##### Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

By using concrete examples, minimal theory, and production-ready Python frameworks, author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems...

#### FULL BLURB

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks – scikit-learn and TensorFlow – author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use scikit-learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details
#2

5 of 5 stars

### Introduction to Machine Learning

##### A Gentle Introduction to the Field - Concepts, ALGORITHMS and Real-World Applications

Scott Landschof

Let’s face the disturbing reality: Facebook stores and records everything you do and uses this information to sell you products. Whenever you like a page, open a browser, send a message or add a friend, Facebook stores information about you...

#### FULL BLURB

Have you ever wondered how Facebook makes money?

It’s FREE for everyone, isn’t it?

Let’s face the disturbing reality: Facebook stores and records everything you do and uses this information to sell you products. Whenever you like a page, open a browser, send a message or add a friend, Facebook stores information about you. If you read the Terms and Conditions form on Facebook you will discover that you authorize full access to all your browser history. If you access your profile on your phone, Facebook can access your GPS to track your locations and all your movements.

All this information Facebook stores about you is then ‘studied’ with extremely complex and sophisticated Machine Learning algorithms. These ‘learn’ about you and your interests, your habits and your patterns. Using this knowledge, Facebook will advertise you products and services that match your unique interests. It knows what you want and it tries to sell it to you, constantly. By selling these uniquely targeted ad spaces Facebook makes money, a lot of it.

In this book I will teach you about this new and revolutionary approach to computer programming known as Machine Learning. In the first part of the book we will develop an appreciation for the importance and relevance of the field in today’s society. We will seek to answer fundamental questions such as

• Where is machine learning being used today?
• How does Machine Learning impact my life?
• How will machine Learning Shape my Future?

In the second part of the book we will dive into the technical details of machine learning. We will explore the different families of algorithms, the mathematics behind them and how they can be used to solve real problems. The algorithms we will cover are:

• Decision Tree Algorithms
• Instance Based Algorithms (eg. K-Nearest Neighbour)
• Regression Algorithms (eg. Logistic Regression)
• Bayesian Algorithms (eg. Naïve Bayes)
#3

4.4 of 5 stars

### Machine Learning For Absolute Beginners

##### A Plain English Introduction (Machine Learning from Scratch)

Oliver Theobald

Machine Learning for Absolute Beginners has been written and designed for absolute beginners...

#### FULL BLURB

Well, hold on there...

Before you embark on your epic journey into the world of machine learning, there is a lot of basic theory to march through first.

Machine Learning for Absolute Beginners has been written and designed for absolute beginners.

It opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.

In this step-by-step guide you will learn:

• The very basics of Machine Learning that all beginners need to master
• Association Analysis used in the retail and E-commerce space
• Recommender Systems as you’ve seen online, including Amazon
• Decision Trees for visually mapping and classifying decision processes
• Regression Analysis to create trend lines and predict trends
• Data Reduction and Principle Component Analysis to cut through the noise
• k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings
• Introduction to Deep Learning/Neural Networks
• Bias/Variance to optimize your machine learning model
• Careers in the field

## 6 More Machine Learning Books For Beginners

If our top 3 list of best machine learning books above isn't for you, then here's a list of another 6 ML books for your bookshelf:

## 6 Python Machine Learning Books For Beginners

If you're looking for a machine learning with python book, then you might be more interested in this list of python machine learning books for beginners (and the more advanced):

## 6 Deep Learning Books For Beginners

The more advanced amogst you might be looking to delve a little deeper and look into deep learning (see what I did there?!??). In that case, these 6 deep learning books for beginners (and the more advanced) might just hit the spot - they are amongst the best deep learning books you'll find anywhere.

## Machine Learning Courses

If books aren't really your thing and you prefer to learn by video course, we have a post dedicated to The Best Machine Learning Courses for Data Science.

Check them out - you won't be disappointed!

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## The Best Machine Learning Books For Beginners - Summary

These days you almost can't call yourself a data scientist until you have some machine learning knowledge and experience.

Hopefully, the machine learning books in this post will help you get a strong start.

Are there any machine learning books or deep learning books that you think should be in this post?

This post forms part of a series on the best books to get started in Data Science.

For more detail, choose from the options below:

Tags

artificial intelligence, data science books, ebooks, machine learning

## 45+ Awesome Gifts for Data Scientists, Statisticians and Other Geeks

45+ Awesome Gifts for Data Scientists, Statisticians and Other Geeks

## Computational Statistics is the New Holy Grail – Experts

Computational Statistics is the New Holy Grail – Experts

## 3 Crucial Tips for Data Processing and Analysis

3 Crucial Tips for Data Processing and Analysis

## Correlation Is Not Causation – Pirates Prove It!

Correlation Is Not Causation – Pirates Prove It!

## How to Conduct Hypothesis Tests: From Formulating the Hypotheses to Analyzing the Results

How to Conduct Hypothesis Tests: From Formulating the Hypotheses to Analyzing the Results

## Demystifying Statistical Tests: A Guide to Choosing the Right One

Demystifying Statistical Tests: A Guide to Choosing the Right One