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2022-03-28T19:22:49Z

DevelopmentData ScienceRecommendation Engine

Recommendation Engine Bootcamp with 3 Capstone Projects

Master recommendation systems Industry Projects using using modern recommendation techniques and methodologies
Highest rated
Rating: 4.7 out of 54.7 (102 ratings)
14,349 students
Created by Data Is Good Academy
Last updated 3/2022
English
English [Auto]

What you'll learn

  • Learn about the different types of Recommender Systems.
  • Learn about Content based recommendation system.
  • Learn about Collaborative based filtering.
  • Learn about Singular Value Decomposition.
  • Learn recommending movies, books using the recommendation system.
  • Learn about Surprise Library for recommendation systems.
  • Learn how to use Correlation for Recommending similar Movies and Genres.
  • Learn how to use different Techniques used for Imputing Missing Values.
  • Practice Quizzes and Exercises of this course.
  • Project Course 1: Movie Recommender Systems.
  • Project Course 2: Open Jobs Analyzer and Recommendation System.
  • Project Course 3: Books Recommendation Engine.

Requirements

  • Good knowledge of Python programming.
  • Knowledge of Probability and Statistics concepts.
  • Knowledge of Machine Learning Algorithms.
  • A willingness to learn and practice.
  • A positive attitude to success.
  • Determination and Desire to Learn new things.

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

  • Introduction to Recommendation Engine.

  • 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.

  • and much, much more!


Not only this, you will also work on three very exciting projects such as


  • Project Course 1: Movie Recommender Systems

  • Project Course 2: Open Jobs Analyzer and Recommendation System

  • Project Course 3: Books Recommendation Engine.


I am always available to answer your questions and help you along your data science journey. See you in class!

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.

Instructor Support - Quick Instructor Support for any queries.

I'm looking forward to see you in the course!


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.

Who this course is for:

  • Data Analysts.
  • Data Scientists.
  • Job-seekers.
  • Anyone who wants to explore data science.
  • Students and professionals who want to improve the training capabilities.

Instructor

Data Is Good Academy
An Google, Facebook, Kaggle Grandmasters team
Data Is Good Academy
  • 4.3 Instructor Rating
  • 6,246 Reviews
  • 247,279 Students
  • 30 Courses

We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all.

We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet.

Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject.

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