| 001 Bonus Lecture.html | 4.6 KB | ||
| 001 Case Study for Netflix.mp4 | 56.4 MB | ||
| 001 Conclusion.mp4 | 46.3 MB | ||
| 001 Introduction to Collaborative Filtering.mp4 | 80.8 MB | ||
| 001 Introduction to Content Based Filtering.mp4 | 58.9 MB | ||
| 001 Introduction to Recommender systems.mp4 | 40.5 MB | ||
| 001 Introduction to SVD.mp4 | 111.9 MB | ||
| 001 Setting up the Environment.mp4 | 45.5 MB | ||
| 001 Understanding the Problem Statement.mp4 | 39.2 MB | ||
| 002 Case Study for Youtube.mp4 | 58.1 MB | ||
| 002 How to Get Your Certificate of Completion.html | 1.4 KB | ||
| 002 Implementing SVD using Surprise.mp4 | 40.7 MB | ||
| 002 Preprocessing the Data for Collaborative Filtering.mp4 | 72.4 MB | ||
| 002 Preprocessing the Data for Content Based Filtering.mp4 | 76.7 MB | ||
| 002 Setting up the Environment.mp4 | 61.4 MB | ||
| 002 Taking a Deep Dive into the Dataset.mp4 | 48.4 MB | ||
| 002 What are it's Use Cases.mp4 | 45.1 MB | ||
| 003 Filtering Movies Based on Genres.mp4 | 58.7 MB | ||
| 003 Implementation of User Based Collaborative Filtering.mp4 | 62.1 MB | ||
| 003 Interpreting Results Obtained from SVD.mp4 | 46 MB | ||
| 003 Taking a Deep Dive into the Job Dataset.mp4 | 38.6 MB | ||
| 003 Types of Recommender Systems.mp4 | 56.7 MB | ||
| 003 Understanding the Problem Statement.mp4 | 30.7 MB | ||
| 004 Analyzing the Job Metrices.mp4 | 24.8 MB | ||
| 004 Comparing Content, and Collaborative Based Filtering.mp4 | 62 MB | ||
| 004 Evaluating Recommender Systems.mp4 | 53.1 MB | ||
| 004 Interpreting the Results obtained from User Based Filtering.mp4 | 63.6 MB | ||
| 004 Introduction to Transactional Encoder.mp4 | 63.3 MB | ||
| 004 Missing Values Imputation.mp4 | 51.9 MB | ||
| 005 Finding Important Metrics for Salary.mp4 | 57.7 MB | ||
| 005 Implementation of Item Based Collaborative Filtering.mp4 | 63.5 MB | ||
| 005 Q and A.mp4 | 32 MB | ||
| 005 Quiz Solution.mp4 | 47.9 MB | ||
| 005 Recommending Similar Movies to Watch.mp4 | 56 MB | ||
| 005 Top 10 Profitable Movies.mp4 | 50.1 MB | ||
| 006 01.+Content+Based+Filtering.ipynb | 76.8 KB | ||
| 006 Manipulating the Duration and Language Column.mp4 | 51.4 MB | ||
| 006 Quiz Solution.mp4 | 55.7 MB | ||
| 006 Taking a Deep Dive at the Naukri Dataset.mp4 | 27.9 MB | ||
| 006 movies.csv | 447.6 KB | ||
| 006 ratings.csv | 2.4 MB | ||
| 007 Extracting the Movie Genres.mp4 | 49.8 MB | ||
| 007 Finding Locations with Highest Job Vacancies.mp4 | 53.9 MB | ||
| 008 Analyzing the Experience required for Jobs.mp4 | 25.9 MB | ||
| 008 Top 10 Most Popular Movies on Social Media.mp4 | 32.7 MB | ||
| 009 Analyzing Which Genre is Most Bankable_.mp4 | 38.3 MB | ||
| 009 Most Demanded Degrees for Jobs.mp4 | 27.3 MB | ||
| 010 Analyzing the Industries with highest no. of Jobs.mp4 | 32.1 MB | ||
| 010 Loss and Profit Analysis on English and Foreign Movies.mp4 | 42.2 MB | ||
| 011 Analyzing the Top Skills required for Jobs.mp4 | 34 MB | ||
| 011 Gross Comparison of Long and Short Movies.mp4 | 46.2 MB | ||
| 012 Association between IMDB Rating and Duration.mp4 | 47.3 MB | ||
| 012 Cleaning the Rest of the Dataset.mp4 | 36.7 MB | ||
| 013 Comparing Critically acclaimed Actors.mp4 | 60.6 MB | ||
| 013 Gathering Vital Information from the Dataset.mp4 | 27.2 MB | ||
| 013 User+Based+Collaborative+Filtering.ipynb | 62.6 KB | ||
| 014 Making a Function to Search for Jobs.mp4 | 33.3 MB | ||
| 014 Top Movies based on Gross, and IMDB.mp4 | 33.5 MB | ||
| 015 Recommending Movies based on Languages and Actors.mp4 | 46.8 MB | ||
| 015 Understanding Relation between Industries and Education.mp4 | 35.2 MB | ||
| 016 Key Takeaways and Findings from the Project.mp4 | 34.1 MB | ||
| 016 Recommending Similar Genres and Movies.mp4 | 68.3 MB | ||
| 017 Key Takeaways from this Project.mp4 | 33.4 MB | ||
| 1. Content_based Recommendation-checkpoint(1).ipynb | 102.4 B | ||
| 1. Content_based Recommendation-checkpoint.ipynb | 102.4 B | ||
| 17. Homework.ipynb | 1.7 KB | ||
| 18. Homework-Solution.ipynb | 14.2 KB | ||
| 2. Collaborative Filtering-checkpoint(1).ipynb | 102.4 B | ||
| 2. Collaborative Filtering-checkpoint.ipynb | 102.4 B | ||
| 20. jobs.csv | 4.3 KB | ||
| 21. naukri.csv | 49.8 MB | ||
| Collaborative Filtering.ipynb | 61.5 KB | ||
| Collaborative_recommendation-checkpoint(1).ipynb | 102.4 B | ||
| Collaborative_recommendation-checkpoint.ipynb | 102.4 B | ||
| Content_based Recommendation.ipynb | 50.7 KB | ||
| Content_based_recommendation-checkpoint(1).ipynb | 102.4 B | ||
| Content_based_recommendation-checkpoint.ipynb | 102.4 B | ||
| Downloaded from 1337x.html | 512 B | ||
| Final Solution.ipynb | 12 MB | ||
| Homework-Solution.ipynb | 96.1 KB | ||
| Homework.ipynb | 1.7 KB | ||
| Item Based CollaboQrative Filtering.ipynb | 62.3 KB | ||
| Movie Recommendation Engine-checkpoint(1).ipynb | 1.1 MB | ||
| Movie Recommendation Engine-checkpoint.ipynb | 1.1 MB | ||
| Movie Recommendation and Analysis.pptx | 282.6 KB | ||
| Open Jobs Analyzer and Recommendation Systems.pptx | 1.2 MB | ||
| Recommendation Engines.pptx | 1.3 MB | ||
| Singular Value Decomposition.ipynb | 62 KB | ||
| User Based Collaborative Filtering.ipynb | 62.6 KB | ||
| movie_dataset.csv | 22.3 MB | ||
| movie_metadata.csv | 1.4 MB | ||
| movies.csv | 482.8 KB | ||
| ratings.csv | 2.4 MB | ||
| ▲ 96 total files | |||
Knowledge should not be limited to those who can afford it or those willing to pay for it. If you found this course useful and are financially stable please consider supporting the creators by buying the course :)
Recommendation Engine Bootcamp with 3 Capstone Projects
Master recommendation systems Industry Projects using using modern recommendation techniques and methodologies
Original Price: CA$104.99
Description
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.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 2.1 GB | freecoursewb | 4 years | 7 | 3 | |
| 349.8 MB | freecoursewb | 4 years | 0 | 0 | |
| 2.9 GB | fcs0310 | 4 years | 0 | 0 | |
|
[ FreeCourseWeb ] Udemy - Let's Make Recommendation Systems Easy with Live Projects Posted by
freecoursewb in Other
|
989.1 MB | freecoursewb | 4 years | 0 | 0 |
|
Udemy - Recommendation Engine Bootcamp with 3 Capstone Projects [Giga Course] Posted by
CourseClub in Other
|
2.9 GB | CourseClub | 4 years | 0 | 0 |
All Comments