Machine Learning

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models

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

Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various 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.

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.

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

Build Android and iOS applications using TensorFlow Lite and Core ML

Leverage the power of Java and its associated machine learning libraries to build powerful predictive models

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level