Machine Learning

Get to grips with key structural changes in TensorFlow 2.0

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

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

Build automated and highly-scalable end-to-end Machine Learning models and pipelines in Azure using Tensorflow, Spark, and Kubernetes.

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

Solve real-world data problems with R and machine learning

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

Leverage the power of reward-based training for your deep learning models with Python

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

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.

Combine popular machine learning techniques to create ensemble models using Python