Best Machine Learning Books- Scripting Your Own Space Odyssey

Machine learning as a field of learning is among the most sought after these days. Growing demand for predictive modeling and data interpretation has made this need rather fertile. A highly skewed supply versus demand curve is further testimony to this fact. The interpretation of this curve is fairly simple. A lot of people, including academicians, largely fancy the wide range of applications that Machine Learning facilitates. However, it is this same lot that fails to follow through with this infatuation and acquaint themselves with its theory. As a result, the gap between enthusiasts and actual practitioners keeps expanding. In order to bridge this gap, an acute academic awareness regarding the scope of machine learning needs urgent development. This can only be made possible by introducing these enthusiasts to a curated crop of the best machine learning books. Only through this indulgent academic endeavor can one hope for the spread of such awareness.

The best machine learning books are broadly classified into machine learning theory books and machine learning practical application books. The following section throws ample light on the best machine learning books that are inclusive of both theory and application and act as the best complementary material for the best online course for machine learning out there:

An Introduction to Statistical Learning- Gareth M. James, Trevor Hastie, Daniela Witten, Robert Tibshirani

An introduction to Statistical Learning is frequently counted among one of the best machine learning books out there. Composed by a bevy of eminent professionals and academics this book is a must read for all machine learning enthusiasts. The book makes for an excellent read for all those trying to understand the foundations of the subject. Fundamentally, it deals with analyzing an entity that is at the core of every machine learning problem - datasets, with the help of mathematics and statistics.

The fundamental roadblocks that people run into while learning machine learning lies in the manner, they interpret their datasets. Primarily this is because of two reasons. The first one stems from a lack of acquaintance with real life data and it's nuances. The second reason lies in the lack of a context awareness on the data.

An introduction to statistical learning tackles these fundamental problems by striving to make its readers acclimatized to varying datasets. In doing so, it adopts a highly methodical approach of discourse for its users. Additionally, it also introduces readers to navigate the R software which adds further clarity to the in-text readings.

The Deep Learning Book- Ian Goodfellow

The deep learning book by Apple Inc. researcher Ian Goodfellow is a necessary treatise on machine learning's various aspects. Its highly informative textual discourse constantly ranks it among the best machine learning books.

The subject matter of Deep Learning by good fellow covers base on the conceptual and mathematical backgrounds of the subject. The text is primarily from an industrial and research-oriented perspective. It is one of those rare books that have struck the right balance between theory and practice. A thorough reading of the book enables the users to envision machine learning theory in a practical construct.

The book is actively endorsed by industry expert Andrew Ng who was also Goodfellow's supervisor at Stanford. In composing this lucid text Goodfellow collaborated with fellow researchers Yoshua Bengio and Aaron Courville. This text is frequently counted among the mandatory reads that should be part of every data scientist's arsenal. It throws light on highly pertinent topics such as deep feedforward networks, sequence modeling, optimization algorithms and convolutional networks. SpaceX CEO Elon musk has hailed the book as the only comprehensive literature on the topic of Deep Learning.

The Hundred Page Machine Learning Book- Andriy Burkov

The Hundred Page Machine Learning Book is widely considered to be among the finest publications in recent times. It actively counts among the best machine learning books because of its absolute brevity. As emphasized in the title, this book actually takes readers through a journey in data science via hundred pages only. The compactness has incited greater participation from among those who in their introductory phases are disheartened by other voluminous binds.

Written by industry expert Andriy Burkov, the text throws much needed light on the expectations held of data scientists. Burkov's two decades worth of industry experience reflects aptly in his body of work. He holds a PhD in artificial intelligence from the University of Laval. He is currently serving as the director of data science at Canada based machine learning firm Gartner. His project work at Gartner includes using an algorithm that extracts useful information out of unstructured or semi-structured data.

Deep Learning with Python - Francois Chollet

Deep Learning with Python by French Engineer Francois Chollet into the application-oriented side of Machine Learning. The book expertly navigates the users into Deep Learning along the ropes of the programming language Python. In doing so it also expands abundantly on the usage of the powerful Keras library within Python's collections itself and doubles up as the perfect guide if you are taking a python crash course online. As most data scientists prefer Python in their daily tidings, this book becomes an absolutely essential read for them.

Chollet's Deep Learning with Python is an incredibly useful read for especially those who wish to master Python from scratch. It simplifies the complex math for all those who get intimidated by the computational aspects of Machine Learning. Some of the most important topics in this book include sentiment analysis using tensor flow and sequence classification with RNNs. In these particular aspects it certainly counts among the best machine learning books of our times.

Hands on Machine Learning with Scikit Learn, Keras and Tensor Flow- Aurelien Geron

Hands on Machine Learning by former Googler Aurelien Geron is arguably among the best machine learning books ever. The book serves as an expert guide into indispensable ML package such as Scikit, Tensor Flow and the Keras library. Hands on Machine Learning is the proverbial self-help book for curious industry veterans and budding ML enthusiasts. Aurelion Geron in his manner of discourse has made sure that the tonality is kept absolutely accessible across all readers.

In Hands on Machine Learning, Geron has made appropriate use of his rich industrial knowledge in maintaining an intuitive flow. The flow of the book is gradual in terms of the difficulty of the topics that are present. The book transitions seamlessly from simpler concepts such as linear and logistic regression into the topics of deep neural networks. Illustrations and practical guides lace the book to balance out the theoretical concepts that are in the text.

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