AI & Robotic
Showing 961–972 of 1170 results
Practical Guide to Neural Network Training: Working with Leading Frameworks
Today’s deep learning frameworks make it easier than ever to work with neural networks. This course will teach you how to efficiently build and train a neural network, while evaluating and addressing some common challenges.
Practical Intro To Reinforcement Learning Using Robotics
Learn to build robots that can automatically learn to behave well in their environments without explicit instructions.
Practical Multi-Armed Bandit Algorithms In Python
Acquire skills to build digital AI agents capable of adaptively making critical business decisions under uncertainties.
Practicing Machine Learning Interview Questions in Python
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Predicting CTR with Machine Learning in Python
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Prepare to develop AI solutions on Azure
As an aspiring Azure AI Engineer, you should understand core concepts and principles of AI development, and the capabilities of Azure services used in AI solutions.
Preparing Data for Machine Learning with Java
Data is at the heart of machine learning. This course will teach you how to bring data into Java from various sources, as well as how to perform basic tidying up and transformations in view of further processing by specialized Java ML libraries.
Preparing Data with Generative AI
Data preparation can be time-consuming and error-prone. This course will teach you how to use Generative AI tools like ChatGPT and Claude to automate and enhance data preparation tasks, making your data analysis more efficient and reliable.
Preprocessing for Machine Learning in Python
Learn how to clean and prepare your data for machine learning!
Prevent Overfitting in Model Training
Overfitting can have significant adverse impacts on the performance and generalization ability of a machine learning model. This course will teach you various techniques to overcome this problem and develop a model that performs well on unseen data.
Production Machine Learning Systems
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
Programming Discrete Math Concepts for Beginners
A beginner-friendly guide to programming concepts in discrete mathematics, covering essential topics and practical applications in computer science.