Develop Recommendation Engine with PYTHON

Develop Recommendation Engine with PYTHON, available at $44.99, has an average rating of 3.75, with 25 lectures, 1 quizzes, based on 20 reviews, and has 3552 subscribers.

You will learn about 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 Learn the fundamental concepts about Recommendation Engine This course is ideal for individuals who are any machine learning engineer or data scientist who want to learn about trending machine learning application or any professional who want to know the secrets behind the recommendation of the products It is particularly useful for any machine learning engineer or data scientist who want to learn about trending machine learning application or any professional who want to know the secrets behind the recommendation of the products.

Enroll now: Develop Recommendation Engine with PYTHON

Summary

Title: Develop Recommendation Engine with PYTHON

Price: $44.99

Average Rating: 3.75

Number of Lectures: 25

Number of Quizzes: 1

Number of Published Lectures: 25

Number of Published Quizzes: 1

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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
Learn the fundamental concepts about Recommendation Engine

Who Should Attend

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

Target Audiences

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

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.

What you’ll learn in this course?

Fundamental concepts about Recommendation Engine
Collaborative Filtering Recommendation
Content Based Filtering Recommendation
Hybrid Recommendation Engine

Course Curriculum

Chapter 1: Everything is Recommendation

Lecture 1: What is Recommendation System?

Chapter 2: Quiz

Chapter 3: Quick Recap

Lecture 1: Quick Recap – NUMPY

Lecture 2: Quick Recap – Pandas

Chapter 4: Pandas Refresher

Lecture 1: Setting up the virtual environment

Lecture 2: Using CSV, XLSX, dictionary and list

Lecture 3: Using URL and html page

Lecture 4: Reading SQL Query

Lecture 5: Using XML and JSON

Lecture 6: head(), tail(), shape(), info(), describe(), count() and pandas options

Lecture 7: colon operator, loc, iloc

Lecture 8: mean, median, max, min, corr, idxmax, idxmin, describe

Lecture 9: Data Sorting

Lecture 10: Data Filtering

Chapter 5: Let's create a basic Recommendation System..

Lecture 1: Loading the datasets

Lecture 2: Calculating weighted average

Lecture 3: Adding little bit complexity into it

Lecture 4: BASIC Recommendation System turns into COMPLEX Recommendation System

Chapter 6: Content Filter Recommendation System

Lecture 1: Loading the datasets and vectorize the column 'OVERVIEW' of the movie using NLP

Lecture 2: Logistic Regression(Building the model)

Lecture 3: Define function to get the recommended movies

Chapter 7: Collaboration Based Recommendation System

Lecture 1: Load, Merge and Rename

Lecture 2: Marking some threshold values

Lecture 3: Building pivot table and applying KNN ML algo

Chapter 8: Datasets and Notebooks

Lecture 1: Download datasets used in this course

Chapter 9: Bonus Section

Lecture 1: Bonus Lecture

Instructors

Pranjal Srivastava
Docker | Kubernetes | AWS | Azure | ML | Linux | Python

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 7 votes
5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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