Published 8/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.19 GB | Duration: 12h 20m
How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.
What you’ll learn
Build a framework for testing and evaluating recommendation algorithms with Python
Understand solutions to common issues with large-scale recommender systems
Create recommendations using deep learning at massive scale
Apply the right measurements of a recommender system’s success
Requirements
Some experience with a programming or scripting language (preferably Python)
Some computer science background, and an ability to understand new algorithms.
Description
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.You’ve seen automated recommendations everywhere – on Netflix’s home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you’ll become very valuable to them.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.Recommender systems are complex; don’t enroll in this course expecting a learn-to-code type of format. There’s no recipe to follow on how to make a recommender system; you need to understand the different algorithms and how to choose when to apply each one for a given situation. We assume you already know how to code.However, this course is very hands-on; you’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.This comprehensive course takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.The coding exercises in this course use the Python programming language. We include an intro to Python if you’re new to it, but you’ll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you’ll need to be able to understand new computer algorithms.
Overview
Section 1: 00a Introduction to Recommender Systems
Lecture 1 01 Introduction To Recommender Systems
Lecture 2 02 How To Evaluate Recommender Systems
Lecture 3 03 Content Based Recommendations
Lecture 4 04 Neighborhood Based Collaborative Filtering
Lecture 5 Source Files
Section 2: 00b Mammoth Interactive Courses Introduction
Lecture 6 00 About Mammoth Interactive
Lecture 7 01 How To Learn Online Effectively
Section 3: 00c Introduction to Python (Prerequisite)
Lecture 8 00. Intro To Course And Python
Lecture 9 01. Variables
Lecture 10 02. Type Conversion Examples
Lecture 11 03. Operators
Lecture 12 04. Collections
Lecture 13 05. List Examples
Lecture 14 06. Tuples Examples
Lecture 15 07. Dictionaries Examples
Lecture 16 09. Conditionals
Lecture 17 10. If Statement Examples
Lecture 18 11. Loops
Lecture 19 12. Functions
Lecture 20 13. Parameters And Return Values Examples
Lecture 21 14. Classes And Objects
Lecture 22 15. Inheritance Examples
Lecture 23 16. Static Members Examples
Lecture 24 17. Summary And Outro
Lecture 25 Source Code
Section 4: 01 Build a Basic Movie Recommender System
Lecture 26 01 Load Data As Pandas Dataframes
Lecture 27 02 Merge Movies And Ratings Dataframes
Lecture 28 03 Build A Correlation Matrix
Lecture 29 04 Test The Recommender
Lecture 30 Source Files
Section 5: 02 Projects 2 and 3 Preview – Machine Learning Movie Recommender
Lecture 31 00 Project Preview
Section 6: 03 Machine Learning Fundamentals
Lecture 32 00A What Is Machine Learning
Lecture 33 00B Types Of Machine Learning Models
Lecture 34 00C What Is Supervised Learning
Section 7: 04 Introduction to User Similarity
Lecture 35 01 Load Data Into Dataframes
Lecture 36 02 Find A Recommendation Based On Different Movie Features
Lecture 37 03 Calculate Distance Between Users
Lecture 38 04 Find Similar Users With Euclidean Distance
Lecture 39 Source Files
Section 8: 05 Recommend a Movie Based on User Similarity
Lecture 40 05 Define Similarity Between Users
Lecture 41 06 Find Top Similar Users
Lecture 42 07 Recommend A Movie Based On User Similarity
Lecture 43 Source Files
Section 9: 06 Recommend a Movie with a K Nearest Neighbors Classifier
Lecture 44 08A What Is K Nearest Neighbours
Lecture 45 08B Recommend A Movie With A K Nearest Neighbors Classifier
Lecture 46 09 Create A Sample User For Testing
Lecture 47 10 Recommend Movies To Sample User
Lecture 48 Source Files
Section 10: 07 Project 4 Preview – Complex Machine Learning Recommender
Lecture 49 00 Project Preview
Section 11: 08 Data Processing Profiles and Items
Lecture 50 01 Load Data For Machine Learning
Lecture 51 02 Process Data For Machine Learning
Lecture 52 03 Build Categories
Lecture 53 Source Files
Section 12: 09 Build Models for User Recommendations
Lecture 54 04A Regression Introduction
Lecture 55 04B What Is Regression
Lecture 56 04C Build A Ridge Regression Model
Lecture 57 05 Evaluate Model Error
Lecture 58 06 Visualize Top Features Affecting Rating
Lecture 59 07 Build A Lasso Regression Model
Lecture 60 08 Visualize Top Features From Lasso Regression
Lecture 61 09 Determine Which Model Is Best
Lecture 62 Source Files
Section 13: 10 Build a Model to Predict Ratings
Lecture 63 01 Load Data For A Neural Network
Lecture 64 02 Build A Singular Value Decomposition Algorithm
Lecture 65 03 Calculate Model Error
Lecture 66 Source Files
Section 14: 11 Deep Learning Fundamentals
Lecture 67 01 What Is Deep Learning
Lecture 68 02 What Is A Neural Network
Lecture 69 03 What Is Unsupervised Learning
Section 15: 12 Build a Neural Network to Predict Ratings
Lecture 70 04 Build A Neural Network
Lecture 71 05 Train The Neural Network
Lecture 72 Source File
Section 16: 13 Data Analysis with Pandas, Numpy and Sci-kit Learn
Lecture 73 00 Project Preview
Lecture 74 01 Load Data Into Dataframes
Lecture 75 02 Explore Data In Our Dataset
Lecture 76 03 Build A Rating Pivot Table
Lecture 77 04 Calculate Average Rating Of A Movie
Lecture 78 05 Find Ratings For A Movie In Every Slice
Lecture 79 06 Find Rating Averages For Every Movie In The Slice
Lecture 80 07 Build An Average Ratings Column
Lecture 81 Source Files
Software developers interested in applying machine learning and deep learning to product or content recommendations,Engineers working at, or interested in working at large e-commerce or web companies,Computer Scientists interested in the latest recommender system theory and research
Homepage
https://www.udemy.com/course/deep-learning-recommendation-algorithms-with-python/
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