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.

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

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.

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

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

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

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

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.

To write the machine learning and deep learning applications that create your business edge