Deep Learning Recommendation Algorithms With Python

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