Learning Path - Machine Learning with TensorFlow and scikit-learn: Master cutting-edge machine learning techniques to build efficient models with Python

by Sebastian Raschka

Unlock powerful machine learning techniques and solve any machine learning problem you come across with Python Machine learning is becoming more and more transformational to businesses every passing day. Machine Learning with TensorFlow and scikit-learn offers you the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. You'll begin this Learning Path by working on techniques that you need to create and contribute to the field of machine learning. Coverage of the TensorFlow deep learning library and the scikit-learn code are included, as they are two of the most popular frameworks used in machine learning. This Learning Path is loaded with several examples that show you how to leverage the open source Python libraries to create machine learning models that easily solve your everyday tasks and problems. You'll learn how to use complex Scikit-learn features and the TensorFlow computing library for intensive computation, digging deeper to gain more insights into your data than ever before. You will explore topics right from mathematical operations to implementing various supervised, unsupervised, and deep learning algorithms with scikit-learn. By the end of this Learning Path, you'll be equipped with tools that will help you maximize the potential of machine learning. This Learning Path includes content from the following Packt Machine Learning with TensorFlow and scikit-learn is for developers, data scientists, and machine learning enthusiasts who want to learn the principles of machine learning and effectively use it with TensorFlow and scikit-learn in their everyday lives. Prior knowledge of Python is assumed. Basic knowledge of high school math and statistics will be beneficial.

805 pages, Paperback

Published January 9, 2019

About the author

Some of my greatest passions are "Data Science" and machine learning. I enjoy everything that involves working with data: The discovery of interesting patterns and coming up with insightful conclusions using techniques from the fields of data mining and machine learning for predictive modeling.

I am a big advocate of working in teams and the concept of "open source." In my opinion, it is a positive feedback loop: Sharing ideas and tools that are useful to others and getting constructive feedback that helps us learn!

A little bit more about myself: Currently, I am sharpening my analytical skills as a PhD candidate at Michigan State University where I am currently working on a highly efficient virtual screening software for computer-aided drug-discovery and a novel approach to protein ligand docking (among other projects). Basically, it is about the screening of a database of millions of 3-dimensional structures of chemical compounds in order to identifiy the ones that could potentially bind to specific protein receptors in order to trigger a biological response.

In my free-time I am also really fond of sports: Either playing soccer or tennis in the open air or building models for predictions. I always enjoy creative discussions, and I am happy to connect with people. Please feel free to contact me by email or in one of those many other networks!