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.

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

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

An introduction to the use of the scikit-learn package in the application of machine learning algorithms, as a way to optimize the programming process to focus our efforts into improving results.

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

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