Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
Duration: 1h 38m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 177 MB
Level: Intermediate | Genre: eLearning | Language: English
Free Download Building a Recommendation System with Python Machine Learning & AI
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Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. In this course, learn about patterns, services, processes, and best practices for designing and implementing machine learning using AWS. Instructor Lynn Langit takes a look at general machine learning concepts, including key machine learning algorithm types. She also examines available service types, such as AWS Machine Learning, Lex, Polly, and Rekognition, which you can use to predict image and video labels. Plus, she steps through how to work with platforms like AWS SageMaker, which includes hosted Jupyter notebooks.
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Level: Intermediate | Genre: eLearning | Language: English
Free Download Amazon Web Services Machine Learning Essential Training
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