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Computer science as an academic discipline began in the 1960s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970s, the study of algorithms was added as an important component of theory. The emphasis was on making computers useful.

Statistics is a fundamental skill that data scientists use every day. It is the branch of mathematics that allows us to collect, describe, interpret, visualise, and make inferences about data. Data scientists will use it for data analysis, experiment design, and statistical modelling. Statistics is also essential for machine learning. We will use statistics to understand the data prior to training a model.

As things stand, I am nowhere near where I aspire to reach as a Data Scientist. In my journey so far, I have met many helpful people and come across various useful resources. Whilst I pondered on…

Thomas Edison famously said that success is 90% perspiration and 10% inspiration. Even though the hype around artificial intelligence has never been higher, the reality of what it takes to actually work in the field - and what it takes to use it successfully - is mired in confusion. Indeed, hype makes it look like 100% inspiration; it hides the work involved in building knowledge and learning skills. So, to help tackle that, here are the 3 important elements to machine learning that might structure how you'd approach it. This is the one thing that people overlook.

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In this book, we introduce coding with tensorflow 2.0. We show how to develop with tensorflow 1.0 and contrast how the same code can be developed in tensorflow 2.0. The book emphasizes the unique features of tensorflow 2.0. Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0.

This is not the same book as The Math of Machine Learning, also published by the same department at Berkeley, in 2018, and also authored by Garret Thomas. I hope they will add sections on Ensemble Methods (combining multiple techniques), cross-validation, and feature selection, and then it will cover pretty much everything that the beginner should know. Other popular free books, all written by top experts in their fields, include Foundations of Data Science published by Microsoft's ML Research Lab in 2018, and Statistics: New Foundations, Toolbox, and Machine Learning Recipes published by Data Science Central in 2019.