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.

Click here to get the book.

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.

Lecture notes for the Statistical Machine Learning course taught at the Department of Information Technology, University of Uppsala (Sweden.) Available as a PDF, here (original) or here (mirror). B.1 A general iterative solution B.2 Commonly used search directions

Containerize your Apps with Docker and Kubernetes is an excellent free book from Gabriel N. Schenker In this post, I explain the significance of deploying apps with Docker and Kubernetes and also some of my thinking at the University of Oxford artificial intelligence cloud and edge impleme... course. Chapter 1: What Are Containers and Why Should I Use Them? Containers are the best way to implement a DevOps architecture. This book explains the end-to-end deployment of containers for an Azure environment – including container orchestration through Kubernetes. The book explains the software supply chain and the friction within it – and then presents containers as a means to reduce this friction and add enterprise-grade security on top of it.

This tutorial is the second book in the'in a weekend' series – after Classification and Regression in a weekend. The idea of the'in a weekend' series of books is to study one complex section of code in a weekend to master the concept. Cloud computing changes the development paradigm. Specifically, it combines development and deployment (the DevOps approach). In complex environments, the developer has to know more than the coding.