Machine Learning

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras

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

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications

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.

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data

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

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.

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

Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time

Your hands-on reference guide to developing, training, and optimizing your machine learning models