Machine Learning

Get to grips with key structural changes in TensorFlow 2.0

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.

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

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 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.

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

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

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x

Discover the skill-sets required to implement various approaches to Machine Learning with Python

Leverage the power of reward-based training for your deep learning models with Python

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