Artificial Intelligence, Machine 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:
And the list goes on and on...
If you're not sure how to get started with machine learning, the 3 books in this blog post will help you make your first steps.
Disclosure: the three books in this post link you to the listed book at your local Amazon store.
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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 3 books to introduce you to Machine Learning and how it is being used in Data Science:
3 Inspirational Machine Learning Books for Aspiring Data Scientists @eelrekab @chi2innovations #datascience #machinelearning
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.
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.
Machine Learning is the Answer!
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
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:
Ready to crank up a virtual server to smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile?
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:
All 8 posts in the series: