The Best books to start with Machine Learning — Machine Learning for Beginners.

The Best books to start with Machine Learning — Machine Learning for Beginners.If you have the question, which machine learning books should I start with, this is a definitive guide for that. It was curated by the data scientists at Lysten.aiPhoto by Tatiana from PexelsThis blog was originally published here.Machine Learning is fairly a new paradigm in the field of software programming. But even then, it has gained a tremendous amount of popularity and adoption, in a very short time.Having said that, a lot of developers refrain from utilising it in their projects because Machine Learning and Artificial Intelligence, seem very intimidating at first glance. And fairly so, because machine learning involves a lot of statistics, linear algebra and calculus, to begin with. But to deal with this problem, in the last five to six years a lot of libraries and frameworks have been created, to handle these calculations. Plus, the computing capacity of personal computers has increased tremendously, and hence handling large datasets and complex calculations, locally, has become feasible.In order to start with machine learning, one at least needs to be familiar with basics of computer programming like declaring variables, using mathematical and logical operators, writing if-else conditions, loops and creating functions. If your uninitiated with that, I’d suggest you to start with a course like Harvard CS50 offered by David J. Malan. This course freely available to stream on YouTube. Think link to the introduction video of this course has been shared below.Trending AI Articles:1. Neural networks for solving differential equations2. Turn your Raspberry Pi into homemade Google Home3. Keras Cheat Sheet: Neural Networks in Python4. Making a Simple Neural Networkhttps://medium.com/media/25ae2202a0daa6b11e7b606e911d2f26/hrefAnother requirement according to me, which is a “good-to-have” is the basic knowledge of Python. Python is an extremely versatile programming language, which can be used for writing programs for various purposes. As I said, Python is not a mandatory requirement for Machine Learning, but as currently most of the production ready frameworks are available easily in Python, it has become the de facto standard for creating machine learning programs. Plus, according to me, it is extremely easy and a fun process to learn.If you consider yourself proficient in both. Then I think it is a good to time start reading some books in this area. Hence, in order to make it easy for you, I have created a short list of books, that you should definitely read. I have carefully curated this list by keeping in mind the diverse interests and technical capabilities of various people. Hence I can confidently say that, I have a book recommendation for each one of you.I have analysed all the books based on the following framework which I have created to categorise them properly, based on level of understanding, versus whether they’re hands-on or theoretical.The framework created by me for analysing the books in this blogThe books I will be talking about here are as follows:Make your own Neural NetworkThe Hundred Page Machine Learning BookHands–On Machine Learning with Scikit–Learn and TensorFlowDeep LearningMake your own Neural Networkby Tariq Rashidhis book was my introduction to the field of deep learning and neural networks. This book explains the concept of machine learning starting from the very basics of Linear Regression and Logistic Regression, and ends at Multilevel Perceptrons to do Image Recognition.The best part about this book is that it assumes no prior knowledge in machine learning or even computer programming. The only basic requirement I see is the ability read basic English and the basic knowledge of high school level math.The author has also provided preprocessed data sets and a github repository, hence it is easy to start getting your hands dirty as soon as possible. This book is quite basic, but does the most crucial job of getting even the most layman to get excited about the field of Machine Learning and Deep Learning.Cover of the book “Make your own Neural Network”About the AuthorTariq Rashid has a degree in Physics, a Masters in Machine Learning and Data Mining, is active in London’s tech scene, leads the London Python meetup group (almost 3000 members) and loves doing talks/workshops whenever he can. For a day job, he works mostly in technology and digital strategy, but really he’s trying to introduce design thinking. He loves open source, and was lucky enough to lead on open source reform for the UK Government.Ever since he was a kid, Tariq has loved the beauty and excitement of science, maths and computing. He devoured everything he could get his hands on in the libraries near his home, in England, especially books on fractals and programming the BBC micro, this was a long time before the world wide web!Despite all the fun and adventures over the 30 years since then, he still thinks too many amazing ideas are badly explained. His personal mission is to do the hard work to make beautiful exciting ideas simple and accessible enough for anyone to understand and appreciate.He also runs the following 3 communities — data science, children’s code club, and an algorithmic art group:http://datasciencecornwall.blogspot.comhttps://www.meetup.com/coderdojocornwall/photos/http://algorithmicartmeetup.blogspot.comWhere to buy this bookhttps://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G/The Hundred Page Machine Learning Bookby Andriy BurkovWhen I first discovered this book, I assumed it to be a very surface level introduction to each topic, having very simple and “engineered” coding exercises to give you a brief about the topics mentioned. But boy, I was so wrong!To my surprise this book is quite detailed and academic in its approach. It’s surprising how Andriy even managed to cover so many topics with their theoretical explanations, examples and the mathematical equations within the limits of 100 pages. It’s quite unreal.This is the book I’d recommend to people who want to start exploring the science part of “Data Science”, from a bird’s eye view. It’s great enough to initiate you into theory and is a good resource to orient the beginners in Data Science.Cover of the book “The Hundred Page Machine Learning Book”About the AuthorAndriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. He holds a Ph.D. in Artificial Intelligence, and for the last seven years, he’s been leading a team of machine learning developers at Gartner. His speciality is natural language processing. His team works on building state-of-the-art multilingual text extraction and normalization systems for production, using both shallow and deep learning technologies.His next book Machine Learning Engineering is almost complete and about to be released soon.Where to buy this bookhttps://www.amazon.com/dp/199957950X/Hands–On Machine Learning with Scikit–Learn and TensorFlowby Aurélien GéronOn the spectrum of theoretical to hands-on, this book lies on the hands-on side. It contains a large array of coding samples and exercises along with brief doses of necessary theory, required to understand the concepts and apply them to the use cases you are facing.This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib. Also, if you care about what’s under the hood you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).This book is the your best bet if you want to dive as soon as possible into the application of machine learning and artificial intelligence in real-world projects.Cover of the book 1st edition of “Hands–On Machine Learning with Scikit–Learn and TensorFlow”About the AuthorAurélien Géron is a Data Science consultant and trainer, former lead of YouTube’s automatic video classification team at Google. During his 20-year career in I.T., he founded several successful companies, now employing over 300 people, including Wifirst (founder and CTO from 2002 to 2012), a leading Wireless ISP in France. He also wrote multiple courses and published several technical books, including the book Hands-on Machine Learning with Scikit-Learn and TensorFlow (O’Reilly Media, 2017), currently the #1 best-selling book on Amazon in the Machine Learning category.The second edition of this book has recently been published, and is called as “Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow” (O’Reilly Media, 2019)Where to buy this bookhttps://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/Deep Learningby Ian Goodfellow, Yoshua Bengio and Aaron CourvilleThis book is the one I am currently reading. It is written by 3 pioneering researchers in the field of deep learning. Hence, It is quite an exhaustively written book, which can also serve as an academic textbook at graduate or doctoral level of study. It contains a lot of theoretical concepts and involves a lot of mathematical equations, for which the understanding of how to read mathematical notations and the ability translate the equations to computer code is required. This book actually contains almost, no computer code.This book is a perfect start for someone who can write basic machine learning programs and understands the basic theories in data science and Machine Learning, but wants to get deeper into the nitty-gritties of the math involved. It skims over concepts like Linear Algebra, Calculus, Signal Processing etc and then directly gets into the application of these concepts to various types of neural networks like CNN, RNN, LSTM, GAN and Autoencoders, and explains the math quite a lot of depth.Cover of the book “Deep Learning”About the AuthorsIan Goodfellow, is currently the Director of Machine Learning in the Special Projects Group, at Apple. He’s also credited as the inventor of Generative Adversarial Networks, a leading method in which neural networks can generate texts, images and even videos.Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. In addition to holding the Canada Research Chair in Statistical Learning Algorithms, he is the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world’s largest university-based research group in deep learning.In 2018, Yoshua Bengio collected the largest number of new citations in the world, thanks to his many publications as a computer scientist. He is also recipient of the Turing Award 2018, along with Geoffrey Hinton and Yann LeCunn.Aaron Courville is a computer scientist whose current work focuses on the research of deep learning models and methods. He is particularly interested in developing probabilistic models and novel inference methods. While he has mainly focused on applications to computer vision, he is also interested in other domains such as natural language processing, audio signal processing, speech understanding and just about any other artificial-intelligence-related task.Where to buy this bookMore Information about this book and the buying links can be found on the following link http://www.deeplearningbook.org/ConclusionAll of these books are a great start, but it really depends on your objective and level of theoretical understanding that these books can help you out. Putting all of them back into the framework that we introduced before this is how it looks.The way these books fit into the framework introduced earlier.For more of such, blogs, articles and How-Tos on Machine Learning, Artificial Intelligence and Tech checkout our blog.Don’t forget to give us your 👏 !https://medium.com/media/c43026df6fee7cdb1aab8aaf916125ea/hrefThe Best books to start with Machine Learning — Machine Learning for Beginners. was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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