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The Complete Recommender Systems Masterclass - Build 7 Projects
00a Introduction to Recommender Systems
01 Introduction To Recommender Systems (9:08)
02 How To Evaluate Recommender Systems (14:54)
03 Content Based Recommendations (4:37)
04 Neighborhood Based Collaborative Filtering (2:22)
Source Files
00b Project 1 Preview - Movie Recommender
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
00c Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:24)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
Intro to Python Slides
Python_Language_Basics
01 Build a Basic Movie Recommender System
00 Project Preview (1:59)
01 Load Data As Pandas Dataframes (12:17)
02 Merge Movies And Ratings Dataframes (8:30)
03 Build A Correlation Matrix (6:19)
04 Test The Recommender (6:55)
Source Files
02 Projects 2 and 3 Preview - Machine Learning Movie Recommender
00 Project Preview (4:51)
03 Machine Learning Fundamentals
00A What Is Machine Learning (5:26)
00B Types Of Machine Learning Models (12:17)
00C What Is Supervised Learning (11:03)
04 Introduction to User Similarity
00 Data
01 Load Data Into Dataframes (6:50)
02 Find A Recommendation Based On Different Movie Features (16:03)
03 Calculate Distance Between Users (5:59)
04 Find Similar Users With Euclidean Distance (9:26)
Source Files
05 Recommend a Movie Based on User Similarity
05 Define Similarity Between Users (6:29)
06 Find Top Similar Users (8:05)
07 Recommend A Movie Based On User Similarity (8:08)
Source Files
06 Recommend a Movie with a K Nearest Neighbors Classifier
08A What Is K Nearest Neighbours (8:07)
08B Recommend A Movie With A K Nearest Neighbors Classifier (12:23)
09 Create A Sample User For Testing (11:09)
10 Recommend Movies To Sample User (3:08)
Source Files
07 Project 4 Preview - Complex Machine Learning Recommender
00 Project Preview (4:37)
08 Data Processing Profiles and Items
00 Data
01 Load Data For Machine Learning (15:14)
02 Process Data For Machine Learning (11:25)
03 Build Categories (9:31)
Source Files
09 Build Models for User Recommendations
04A Regression Introduction (8:58)
04B What Is Regression (19:55)
04C Build A Ridge Regression Model (13:43)
05 Evaluate Model Error (7:04)
06 Visualize Top Features Affecting Rating (11:27)
07 Build A Lasso Regression Model (8:01)
08 Visualize Top Features From Lasso Regression (8:07)
09 Determine Which Model Is Best (3:27)
Source Files
10 Build a Model to Predict Ratings
00 Data
01 Load Data For A Neural Network (9:16)
02 Build A Singular Value Decomposition Algorithm (10:14)
03 Calculate Model Error (11:27)
Source FIles
11 Deep Learning Fundamentals
01 What Is Deep Learning (7:42)
02 What Is A Neural Network (8:47)
03 What Is Unsupervised Learning (8:17)
12 Build a Neural Network to Predict Ratings
04 Build A Neural Network (15:16)
05 Train The Neural Network (12:27)
Source Files
13 Data Analysis with Pandas, Numpy and Sci-kit Learn
00 Data
00 Project Preview (2:38)
01 Load Data Into Dataframes (5:28)
02 Explore Data In Our Dataset (3:49)
03 Build A Rating Pivot Table (5:22)
04 Calculate Average Rating Of A Movie (5:51)
05 Find Ratings For A Movie In Every Slice (6:17)
06 Find Rating Averages For Every Movie In The Slice (7:54)
07 Build An Average Ratings Column (13:25)
Source Files
01 Introduction To Recommender Systems
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