Books

Isn't it weird that ML software is super important, yet crazy fragile? If ML software is valuable but so unstable, how come data scientists and ML engineers are rarely trained on the basics of building profitable software systems? What to do about it?

Over the years I have invented and/or collected various clever and/or stupid ways of solving Fizz Buzz. In this book are ten solutions I found particularly interesting, each one the inspiration for a discussion of different aspects of coding, Python, testing, Fizz Buzz, mathematics, software design, technical interviewing, and related topics.

هل تريد تعلم خوارزميات تعلم الالة بشكل عملي واضح وتطبيقي. في هذا الكتاب ستجد مرجع لخوارزميات تعلم الآلة مع أمثلة تطبيقية بلغة بايثون بلغة واضحة وسهلة الفهم

A breezy guide to using Python's built-in support for logging. The guide covers general concepts relevant to logging, logging APIs in Python and how to use them, and good practices, gotchas, and performance considerations associated with these APIs.

Questo libro insegna a usare Python nell'ambiente di Windows e affronta molti aspetti della "vita quotidiana del programmatore", partendo dalle nozioni più semplici. Dall'uso corretto dell'interprete dei comandi fino agli aspetti più complessi come la distribuzione, il controllo di versione, il testing, la documentazione, e molto altro ancora.

Once you’ve completed Part 1 and Part 2, you’ll be able to build nearly any project that you want, and deploy it for the world to use. If you’re stuck for ideas, you can go through the examples given in Part 3, which consists of practical tutorials to build everything from web scrapers to chat bots.

Python 3 Combat Guide is a practical guide, full of actual experience and common sense, to avoid errors and to develop Python 3 Software Projects and System Administration programs.Targeted to SDM, Team Manager and Developers, defines practical strategies and tricks, and teach the basis of Unit Testing in pytest.You'll learn OOP easily.

This book contains detailed stepwise solutions to Regression problems for beginners. It is a collection of solutions in Python and R programming language.

Learn to master the Google Cloud platform (GCP).

Learn how to apply the Clean Architecture. The book strongly focuses on practical aspects and is illustrated with tons of code snippets. Code samples are in Python.

Even books that have "learn" in the title introduce readers to hopelessly complex topics like object-oriented programming or concurrency. It turns out YAGNI (You Ain't Gonna Need It). Why teach students topics they won't use either ever, or not for a few years?

"Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." - Stephen Hawking. Learn how to solve real-world problems with Deep Learning models (NLP, Computer Vision, and Time Series). Go from prototyping to deployment with PyTorch and Python!

These three-volume book series cover a wide variety of topics in machine learning focusing on supervised and unsupervised learning, intended for data scientist and machine learning experts providing a very concise description of the scikit-learn library. The first volume covers the generalized linear models (linear & logistic regression).

Learn how to write Python and beyond. You will not only learn the syntax of Python, but you will also create prototype applications and binaries that you can share with your family and friends.