AI News, 24 Free Data Science Books

24 Free Data Science Books

These three books by highly respected academics / practitioners, and cover some of the most popular techniques in data mining and machine learning today.

The previous section, Statistical Machine Learning, covers machine learning from the perspective of statisticians: creating statistical valid models of the data that can be used for predictions.

This section, practical machine learning / data mining, deals more with the need to extract information and make predictions from large datasets.

10 Free Must-Read Machine Learning E-Books For Data Scientists & AI Engineers

So you love reading but can’t afford to splurge too much money on books?

We begin the list by going from the basics of statistics, then machine learning foundations and finally advanced machine learning.

One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani One of the most popular entries in this list, it’s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field.

Authors: Shai Shalev-Shwartz and Shai Ben-David This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms.

Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.

It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them.

Authors: Anand Rajaraman and Jeffrey David Ullman As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets.

It starts off by covering the history of neural networks before deep diving into the mathematics and explanation behind different types of NNs.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Every single Machine Learning course on the internet, ranked by your reviews

The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

Here is a succinct description: As would be expected, portions of some of the machine learning courses contain deep learning content.

If you are interested in deep learning specifically, we’ve got you covered with the following article: My top three recommendations from that list would be: Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience.

Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms.

Ng explains his language choice: Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video.

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

few prominent reviewers noted the following: Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews.

Free Must Read Books on Statistics & Mathematics for Data Science

The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics.

Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science.

If you too aspire to work for such top companies in future, it is essential for you to develop a mathematical understanding of data science. Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic. I’ve met many data scientists who struggle to explain predictive models statistically.

In addition to theory, this book also lay emphasis on using ML algorithms in real life setting.

It focuses entirely on understanding real life influence of statistics using popular case studies.

Having been written in a conversational style (rare to find math this way), this book is a great introductory resource on statistics.

It begins with scientific methods of data gathering and end up delivering dedicated chapters on bayesian statistics.

Gilbert unique way of delivering knowledge would give you the intuition and excitement to move forward after every chapter.

It enlists all the necessary chapters such as vectors, linear equations, determinants, eigenvalues, matrix factorization etc in great depth.

The author covers most of the important topics such as gaussian elimination, matrix factorization, lancoz method, error analysis etc.

This is a must read book for intermediate and advanced practitioners in machine learning. This book is written by Luc Devroye, Laszlo Gyorfi and Gabor Lugosi. It covers a wide range of topics varying from bayes error, linear discrimination to epsilon entropy &

neural networks. It provides a convincing explanation to complex theorems with section wise practice problems.

If you haven’t been good at maths till now, follow this book religiously and you should surely see significant improvements in your math understanding.

This isn’t exactly a text book you’d discover, but a quick digital guide on mathematical equations.

The author of this book is Matthias Vallentin. After you finish with essentials of mathematics, this book will help you connect various theorem and algorithm quickly with their formulae.

It’s difficult to derive equations instantly, this book will help you to quickly navigate to your desired problem and solve.

Hence, if you aim for a long term success in data science, make sure you learn to create stories out of maths and statistics.

Top 5 Must-Read Statistics Books For ML Enthusiasts

Machine learning cannot be limited to one subject due to its widespread applications, ranging an adoption in a variety of disciplines including science and engineering.

This classic, no-nonsense book on statistics follows a business-oriented approach, where Levin and Rubin explain the concepts in an easy to understand manner followed with real world examples in each chapter to show the practicality of these concepts.The book also includes learning aids such as review exercises, concepts tests among others.

In addition, working with statistical tools on software packages such as MS-Excel and SPSS is also presented at the end.The book is even adopted by many universities as part of graduate and undergraduate level coursework on statistics.

Wheelan focuses on important topics such as regression analysis, inference and correlation among others to emphasise how crucial data can be manipulated by entities such as organisations and even political parties.

Primarily focussed on statistical research, this book advises the right approach and nuances to be followed when conducting a top-down research or experiment, with picturesque examples.

The book covers all important aspects of modern statistics right from presenting and organising data to realising tough-to-digest topics such as Central Limit theorem, confidence intervals, estimation and many more.

Although, notations may slightly differ from other standard textbook-level statistics, this book is suggested for anyone who wishes to take statistics to next level with a solid understanding of basic concepts.

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