The Complete Recommender Systems Masterclass - Build 7 Projects
Learn how to snag the most in demand role in the tech field today!
Created by John Bura
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Skill level
All levels
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Sections
13
03
Lectures
50
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Instructor
John Bura
What's inside
This course includes.
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13
Sections
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50
Lectures
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50
Resources
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Certificate of completion
Included
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Mobile and desktop access
Included
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AI learning assistance
Included
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Course content
Curriculum & lectures.
+ 00a Introduction to Recommender Systems 5 lectures
00-01 Introduction To Recommender Systems
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00-02 How To Evaluate Recommender Systems
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00-03 Content Based Recommendations
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00-04 Neighborhood Based Collaborative Filtering
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Full Course Source Files
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+ 01 Build a Basic Movie Recommender System 5 lectures
01-00 Project Preview
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01-01 Load Data As Pandas Dataframes
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01-02 Merge Movies And Ratings Dataframes
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01-03 Build A Correlation Matrix
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01-04 Test The Recommender
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+ 02 Projects 2 and 3 Preview - Machine Learning Movie Recommender 1 lecture
00 Project Preview
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+ 04 Introduction to User Similarity 4 lectures
01 Load Data Into Dataframes
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02 Find A Recommendation Based On Different Movie Features
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03 Calculate Distance Between Users
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04 Find Similar Users With Euclidean Distance
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+ 05 Recommend a Movie Based on User Similarity 3 lectures
05 Define Similarity Between Users
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06 Find Top Similar Users
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07 Recommend A Movie Based On User Similarity
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+ 06 Recommend a Movie with a K Nearest Neighbors Classifier 4 lectures
08A What Is K Nearest Neighbours
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08B Recommend A Movie With A K Nearest Neighbors Classifier
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09 Create A Sample User For Testing
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10 Recommend Movies To Sample User
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+ 07 Project 4 Preview - Complex Machine Learning Recommender 1 lecture
00 Project Preview
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+ 08 Data Processing Profiles and Items 3 lectures
08-01 Load Data For Machine Learning
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08-02 Process Data For Machine Learning
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08-03 Build Categories
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+ 09 Build Models for User Recommendations 8 lectures
09-04A Regression Introduction
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09-04B What Is Regression
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09-04C Build A Ridge Regression Model
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09-05 Evaluate Model Error
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09-06 Visualize Top Features Affecting Rating
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09-07 Build A Lasso Regression Model
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09-08 Visualize Top Features From Lasso Regression
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09-09 Determine Which Model Is Best
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+ 10 Build a Model to Predict Ratings 3 lectures
10-01 Load Data For A Neural Network
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10-02 Build A Singular Value Decomposition Algorithm
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10-03 Calculate Model Error
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+ 11 Deep Learning Fundamentals. 3 lectures
11-01 What Is Deep Learning
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11-02 What Is A Neural Network
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11-03 What Is Unsupervised Learning
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+ 12 Build a Neural Network to Predict Ratings 2 lectures
12-04 Build A Neural Network
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12-05 Train The Neural Network
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+ 13 Data Analysis with Pandas, Numpy and Sci-kit Learn 8 lectures
13-00 Project Preview
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13-01 Load Data Into Dataframes
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13-02 Explore Data In Our Dataset
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13-03 Build A Rating Pivot Table
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13-04 Calculate Average Rating Of A Movie
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13-05 Find Ratings For A Movie In Every Slice
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13-06 Find Rating Averages For Every Movie In The Slice
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13-07 Build An Average Ratings Column
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Description
About this course.
We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from our extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.
Instructors
Taught by people who ship.
John Bura
Founder and CEO of Mammoth Club and Course Pro, the #1 AI-powered Learning Management System for course and content development, training and evaluation.
Ready to start building?
Learn how to snag the most in demand role in the tech field today!