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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Chapter 6. Job Recommendation Engine

We have already seen how to develop a recommendation system for the e-commerce product in Chapter 4, Recommendation Systems for e-Commerce, Now, we will apply the same concepts that you learned in Chapter 4, Recommendation Systems for e-Commerce but the type and format of the dataset is different. Basically, we will build a job recommendation engine. For this application, we have taken into account the text dataset. The main concept of building the recommendation engine will not change, but this chapter gives you a fair idea of how to apply the same concepts to different types of datasets.

In this chapter, we will cover the following topics:

  • Introducing the problem statement

  • Understanding the datasets

  • Building the baseline approach

    • Implementing the baseline approach

    • Understanding the testing matrix

    • Problems with the baseline approach

    • Optimizing the baseline approach  

  • Building the revised approach

    • Implementing the revised approach

    • Testing the revised approach

    • Problems...

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