Machine Learning
Showing 193–204 of 233 results
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.
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.
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.
Put Your Machine Learning on Autopilot
In this session, you'll learn about Automated Machine Learning (AutoML) and how the latest advances in AutoML allow you to put your machine learning models into autopilot mode while maintaining full visibility and control.
PyTorch for Classification
Build AI classification models with PyTorch using binary and multi-label techniques.
Recommendation Systems on Google Cloud
In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
Recurrent Neural Networks (RNNs) for Language Modeling with Keras
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Reinforcement Learning from Human Feedback (RLHF)
In this course we explore one corner of the expanding AI universe, and review some of the basic principles found in reinforcement learning from human feedback (RLHF), the technology underlying great AI tools such as ChatGPT, Bard, and more.
Reinforcement Learning with Gymnasium in Python
Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.