Real-World Machine Learning Projects with Scikit-Learn [Video]

Real-World Machine Learning Projects with Scikit-Learn [Video]

Nikola Živković

1 customer reviews
Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projects
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Video Description

Scikit-Learn is one of the most powerful Python Libraries with has a clean API, and is robust, fast and easy to use. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more.

In this course you will build powerful projects using Scikit-Learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.

By the end of the course you will be adept at working on professional projects using Scikit-Learn and Machine Learning algorithms.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Real-World-Machine-Learning-Projects-...

Style and Approach

The course takes the approach of firstly defining the problem and then giving you the solution, along with the steps to solve it practically by using Python using Scikit-Learn. You will build examples from scratch, progressing from simpler problems to complicated ones

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What You Will Learn

  • Work with Scikit-Learn's Machine Learning tools to build efficient real -world projects using Scikit-Learn 
  • Predict demand for your products (to help your business adapt) by using Regression Trees
  • Use Support Vector Machines to learn how to train your model to predict the chances of heart disease 
  • Analyze the population and generate results in line with ethnicity and other factors using K-Means Clustering
  • Understand the buying behavior of your customers using Customer Segmentation to drive the sales of your products

Authors

Video Details

ISBN 139781789131222
Course Length2 hours 34 minutes
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