Machine Learning

Build powerful deep learning models using fewer learning samples with the power of scikit-learn and Keras

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries

Build automated and highly-scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Your one-stop guide to working with the browser-based JavaScript library for training and deploying machine learning models effectively.

Understand how machine learning works and get hands-on experience in using R to build algorithms that can solve several real-world problems.

Get to grips with key structural changes in TensorFlow 2.0

Combine popular machine learning techniques to create ensemble models using Python

Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.

Introducing the study of machine learning and deep learning algorithms for financial practitioners.

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.

A practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis.

Infuse an extra layer of intelligence into your Go applications with machine learning and AI

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.