English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 06m | 1.03 GB
Welcome to Machine Learning with ML.NET. In this course, instructor Pranav Rastogi guides you through the concepts of machine learning, what you can build with these concepts, and how to get started. First, Pranav explains what ML.NET is and what you can do with the framework. He covers how to build a ML model for sentiment analysis of customer reviews and explains how to classify incoming GitHub issues into one of the many tags (labels) using a multiclass classification algorithm. Pranav shows you how to recommend movies for users using collaborative filtering-based recommendation approach. He concludes by discussing how deep learning enables many more scenarios, using sound, images, text and other data types.
Table of Contents
1 ML.NET Machine learning introduction
2 ML.NET introduction
3 Getting started with ML.NET
4 Build an ML model for sentiment analysis
5 Build an ML model for GitHub issue classification
6 Build an ML model for predicting taxi fares
7 Build an ML model for movie recommendations
8 Deep learning with ML.NET Image classification
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