Data Science

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.

En el manual expongo, de forma clara y sencilla, los conceptos básicos de un análisis exploratorio de datos a nivel descriptivo y cómo llevarlo a la práctica con el software estadístico R y datos reales. El libro está pensado para que el lector avance paso a paso en su proceso de auto-aprendizaje, por lo que se proporcionan muchos ejemplos.

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.

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery!

The second edition of Getting Started with Data Journalism - updated, with expanded sections, as well as new examples and exercises. This new edition brings the book up to date because, while the basic skills needed for data journalism have not changed, the tools and techniques have evolved since the first edition of this book was published.

General trace and log analysis patterns allow the application of uniform diagnostics and anomaly detection across diverse software environments. This pattern language covers any execution artifact from a small debugging trace to a distributed log with billions of messages from hundreds of computers, thousands of components, threads, and processes.