Books

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.

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!

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.

Need to collect terabytes of data across all aspects of your operations? Need to transform complex scientific datasets into innovative software that is deployed across the pipeline, accelerating the pace and quality of all business decisions to unbelievable levels? Having problems with performance or maintenance? If so, this book is for you!

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).

A journey to discover how to change our constraints into competitive advantages for the @NewDataEra

This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. Many resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. Based on material taught at leading universities.

Python has grown in recent years, due to trends of data science and ML, and I think the main reason is because it’s essentially for a person to keep track of everything that is going on. And it makes “introducing” people to Python an interesting. Nowadays, audiences need to learn python in fast way for data science and that what we will give them.

tldr: Don't read this if all you want is from tensroflow import keras

With the rise of Data Science and Machine Learning, Data Engineering is quickly becoming a in-demand skill. Data Engineering requires a unique skillset that is hard to learn without experience. I will teach you how to write scalable data pipelines and more!

Learn how to analyze big datasets in a distributed environment without being bogged down by theoretical topics. The API is vast and other learning tools make the mistake of trying to cover everything. This book only covers what you need to know, so you can explore other parts of the API on your own!

Contains reprinted articles in full color (including more than 230 figures) from 16 volumes of Memory Dump Analysis Anthology (Diagnomicon) related to pattern-oriented software diagnostics with additional comments showing the historical development of this autonomous and distinctive discipline over the last 18 years.

This book will help you easily build beautiful plots in Python using the powerful plotnine package, which has been adapted from the popular ggplot2 package in R. If you'd like to create highly customised plots, including replicating the styles of XKCD and fivethirtyeight, this is your book.