Books

Hiring Data Scientists and Machine Learning Engineers is a concise, practical guide to help you hire the right people for your organization. The book will help you navigate the plethora of data science related roles and skills and help you create an effective hiring strategy to suit your organization's needs.

This series will introduce you to the Python programming language. It’s aimed at beginning programmers, but even if you’ve written programs before and just want to add Python to your list of languages, Introducing Python will get you started.

Recently, researchers have found a theory that explains every aspect of conscious experience. This book explores this theory and introduces you to neuroscience and gives you instructions how to build a conscious machine.

Are you interested in learning about graph theory and applied network analysis, leveraging your Python skills? Then this is the book for you! See how network science & graph theory connects with a variety of data analysis problems, and use it to solve your next data science challenge!

G>G>G> 研究、生活、幸福。G>G>

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?

Focus on building working solutions

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

Welcome to the first book in the Week End Series, this series helped me a lot before, and I hope it helps you now. And this book is about the techniques that you should learn to deal with data, any data scientist, and not only data scientists, but every one that faces data problems

This machine learning book series aims at providing real hands-on training from general concepts and architecture to low-level details and mathematics. The first epoch covers the simplest linear associative network, proposes a brick notation for algebraic expressions, shows required calculus derivations, and illustrates gradient descent.

In Errors of Regression Models you’ll learn how to choose the most appropriate statistics to measure the accuracy of your regression-based prediction model.Written in plain English with no technical jargon, Errors of Regression Models is perfect for beginners!Discover how to measure the accuracy of your regression models quickly and effectively.

Welcome to one of the On Weekend Series, this series helped me a lot before, and I hope it helps you now. And this book is about the techniques that you should learn to learn, understand and bulid a convolution neural network. I hope you find what you need in this book.

Learn to master the Google Cloud platform (GCP).

"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).