Recommendation Engine Bootcamp with 3 Capstone Projects
- Description
- Curriculum
- FAQ
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Welcome to the best online course on Recommendation Engine.
Master various recommendation engines including Content based filtering, collaborative filtering, Singular value decomposition.
Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.
A recommendation engine is a type of data filtering tool using machine learning algorithms to recommend the most relevant items to a particular user or customer.
It operates on the principle of finding patterns in consumer behavior data, which can be collected implicitly or explicitly.
This course gives you a thorough understanding of the Recommendation systems.
In this course, you will cover
- Use cases of recommender systems.
- Content-based filtering.
- Filtering movies based on genres.
- User-based collaborative filtering.
- Item-based collaborative filtering.
- Singular value decomposition using Surprise library.
Not only this, you will also work on three very exciting projects.
You will learn to create a movie recommendation engine as well as a book recommendation engine and Open job analyzer system.
It will be fun working on such exciting projects.
You will see how easy it is to recommend new books or movies based on the user’s past preferences.
I guarantee you will love this course.
All the resources used in this course will be shared with you.
Don’t wait and Enroll now.
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13
Introduction to Collaborative Filtering -
14
Preprocessing the Data for Collaborative Filtering
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15
Implementation of User Based Collaborative Filtering
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16
Interpreting the Results obtained from User Based Filtering
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17
Implementation of Item Based Collaborative Filtering
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18
Quiz on Collaborative Based Filtering -
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Quiz Solution
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28
Setting up the Environment
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29
Taking a Deep Dive into the Dataset
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30
Understanding the Problem Statement
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31
Missing Values Imputation
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32
Top 10 Profitable Movies
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33
Manipulating the Duration and Language Column
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34
Extracting the Movie Genres
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35
Top 10 Most Popular Movies on Social Media
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36
Analyzing Which Genre is Most Bankable?
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37
Loss and Profit Analysis on English and Foreign Movies
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38
Gross Comparison of Long and Short Movies
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39
Association between IMDB Rating and Duration
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40
Comparing Critically acclaimed Actors
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41
Top Movies based on Gross, and IMDB
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42
Recommending Movies based on Languages and Actors
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43
Recommending Similar Genres and Movies
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44
Key Takeaways from this Project
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45
Quiz on Movie Recommender Systems
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46
Understanding the Problem Statement
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47
Setting up the Environment
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48
Taking a Deep Dive into the Job Dataset
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49
Analyzing the Job Metrices
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50
Finding Important Metrics for Salary
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51
Taking a Deep Dive at the Naukri Dataset
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52
Finding Locations with Highest Job Vacancies
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53
Analyzing the Experience required for Jobs
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54
Most Demanded Degrees for Jobs
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55
Analyzing the Industries with highest no. of Jobs
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56
Analyzing the Top Skills required for Jobs
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57
Cleaning the Rest of the Dataset
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58
Gathering Vital Information from the Dataset
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59
Making a Function to Search for Jobs
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60
Understanding Relation between Industries and Education
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61
Key Takeaways and Findings from the Project
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62
Quiz on Open Jobs Analyzer and Recommendation System
