Machine Learning

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

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.

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

A guide to advances in machine learning for financial professionals, with working Python code

Get efficient in performing data mining and machine learning using IBM SPSS Modeler

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.

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.

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services