Getting Started with Machine Learning in Python [Video]

Getting Started with Machine Learning in Python [Video]

Rudy Lai

A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems
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Video Details

ISBN 13 9781788477437
Course Length 2 hours 53 minutes

Table of Contents

Video Description

Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills.

In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.

You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.

If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you!

All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python-

Style and Approach

This extensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you. A practical, hands-on course packed with step-by-step instructions, working examples, and helpful advice. You will learn how Machine Learning can be used to create artificial intelligence.

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What You Will Learn

  • Core concepts of Machine Learning so you can understand fellow data scientists
  • Clean your data to optimize how it feeds into your Machine-Learning models.
  • Perform regression in a supervised learning setting, so that you can predict numbers, prices, and conversion rates.
  • Perform classification in a supervised-learning setting, teaching the model to distinguish between different plants, discussion topics, and objects.
  • Use decision tree models and random forests, creating models that are explainable but powerful.
  • Go past linear models with SVMs and polynomial regression, tackling relationships that are non-linear.
  • Measure and evaluate your Machine-Learning pipeline, so that you can improve your solution over time.

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Table of Contents

Video Details

ISBN 139781788477437
Course Length2 hours 53 minutes
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