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30-Day Money-Back Guarantee
Development Data Science Recommendation Engine

Develop Recommendation Engine with PYTHON

Learn to apply recommendation techniques used by Amazon, Netflix, Youtube, IMDB
Rating: 4.5 out of 54.5 (10 ratings)
3,477 students
Created by Pranjal Srivastava
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn Collaborative Filtering Recommendation technique
  • Learn Content Based Filtering Recommendation technique
  • Learn to build Hybrid Recommendation Engine
  • Learn the techniques used by Amazon, Netflix to recommend products to the customer

Course content

7 sections • 15 lectures • 59m total length

  • Preview02:31

  • Quick Recap - NUMPY
    00:03
  • Quick Recap - Pandas
    00:03

  • Preview02:57
  • Preview08:28
  • Adding little bit complexity into it
    03:31
  • BASIC Recommendation System turns into COMPLEX Recommendation System
    08:51

  • Loading the datasets and vectorize the column 'OVERVIEW' of the movie using NLP
    06:45
  • Logistic Regression(Building the model)
    03:06
  • Define function to get the recommended movies
    05:53

  • Load, Merge and Rename
    07:42
  • Marking some threshold values
    06:02
  • Building pivot table and applying KNN ML algo
    03:13

  • Download datasets used in this course
    00:02

  • Bonus Lecture
    00:13

Requirements

  • Anaconda installed in pc
  • Python installed in pc
  • Little bit knowledge of python programing, pandas and numpy

Description

In this course, you'll going to learn about recommendation system. Also known as recommender engines. According to Netflix, there 70% of the videos seen by recommending the videos to the user. Not only Netflix, Amazon also claims most products, they because of their recommendation system. There is a wide range of techniques to be used to build recommender engines. In this learning path, It will mostly cover all the easy to moderate kind of techniques with hands on experience.

What is Recommendation System?

Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales. Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.

Two types of Recommendation systems are Collaborative Based and Content based filters Recommending system. You'll be excel both the methods after the completion of course. Other than this you'll also learn more about cosine, Pearson correlation as well different types of machine learning algorithms like Logistic regression and K-nearest to get the best recommendation.


Who this course is for:

  • any machine learning engineer or data scientist who want to learn about trending machine learning application
  • any professional who want to know the secrets behind the recommendation of the products

Instructor

Pranjal Srivastava
SME of AWS & IBM Cloud
Pranjal Srivastava
  • 3.6 Instructor Rating
  • 1,930 Reviews
  • 53,964 Students
  • 57 Courses

I am passionate developer, machine learning enthusiast, coder and bug fixer. Developed many applications on various platforms including python, java, android, php, etc.

I have worked over cloud on IBM Bluemix, AWS, and Microsoft Azure. Prefer digital marketing and SEO in my free time.

I am IBM certified Python developer.

Created own Programming language in Hindi .

#ILoveIndia

#LoveOpenSource   #SmartProgrammer


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