Deep Learning Recommendation Algorithms with Python

Deep Learning Recommendation Algorithms with Python, available at $54.99, has an average rating of 3.9, with 81 lectures, based on 5 reviews, and has 49 subscribers.

You will learn about 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 systems success This course is ideal for individuals who are Software developers interested in applying machine learning and deep learning to product or content recommendations or Engineers working at, or interested in working at large e-commerce or web companies or Computer Scientists interested in the latest recommender system theory and research It is particularly useful for Software developers interested in applying machine learning and deep learning to product or content recommendations or Engineers working at, or interested in working at large e-commerce or web companies or Computer Scientists interested in the latest recommender system theory and research.

Enroll now: Deep Learning Recommendation Algorithms with Python

Summary

Title: Deep Learning Recommendation Algorithms with Python

Price: $54.99

Average Rating: 3.9

Number of Lectures: 81

Number of Published Lectures: 81

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 systems success

Who Should Attend

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

Target Audiences

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

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 Tensorflowto 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 Pythonprogramming 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.

Course Curriculum

Chapter 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

Chapter 2: 00b Mammoth Interactive Courses Introduction

Lecture 1: 00 About Mammoth Interactive

Lecture 2: 01 How To Learn Online Effectively

Chapter 3: 00c Introduction to Python (Prerequisite)

Lecture 1: 00. Intro To Course And Python

Lecture 2: 01. Variables

Lecture 3: 02. Type Conversion Examples

Lecture 4: 03. Operators

Lecture 5: 04. Collections

Lecture 6: 05. List Examples

Lecture 7: 06. Tuples Examples

Lecture 8: 07. Dictionaries Examples

Lecture 9: 09. Conditionals

Lecture 10: 10. If Statement Examples

Lecture 11: 11. Loops

Lecture 12: 12. Functions

Lecture 13: 13. Parameters And Return Values Examples

Lecture 14: 14. Classes And Objects

Lecture 15: 15. Inheritance Examples

Lecture 16: 16. Static Members Examples

Lecture 17: 17. Summary And Outro

Lecture 18: Source Code

Chapter 4: 01 Build a Basic Movie Recommender System

Lecture 1: 01 Load Data As Pandas Dataframes

Lecture 2: 02 Merge Movies And Ratings Dataframes

Lecture 3: 03 Build A Correlation Matrix

Lecture 4: 04 Test The Recommender

Lecture 5: Source Files

Chapter 5: 02 Projects 2 and 3 Preview – Machine Learning Movie Recommender

Lecture 1: 00 Project Preview

Chapter 6: 03 Machine Learning Fundamentals

Lecture 1: 00A What Is Machine Learning

Lecture 2: 00B Types Of Machine Learning Models

Lecture 3: 00C What Is Supervised Learning

Chapter 7: 04 Introduction to User Similarity

Lecture 1: 01 Load Data Into Dataframes

Lecture 2: 02 Find A Recommendation Based On Different Movie Features

Lecture 3: 03 Calculate Distance Between Users

Lecture 4: 04 Find Similar Users With Euclidean Distance

Lecture 5: Source Files:

Chapter 8: 05 Recommend a Movie Based on User Similarity

Lecture 1: 05 Define Similarity Between Users

Lecture 2: 06 Find Top Similar Users

Lecture 3: 07 Recommend A Movie Based On User Similarity

Lecture 4: Source Files

Chapter 9: 06 Recommend a Movie with a K Nearest Neighbors Classifier

Lecture 1: 08A What Is K Nearest Neighbours

Lecture 2: 08B Recommend A Movie With A K Nearest Neighbors Classifier

Lecture 3: 09 Create A Sample User For Testing

Lecture 4: 10 Recommend Movies To Sample User

Lecture 5: Source Files

Chapter 10: 07 Project 4 Preview – Complex Machine Learning Recommender

Lecture 1: 00 Project Preview

Chapter 11: 08 Data Processing Profiles and Items

Lecture 1: 01 Load Data For Machine Learning

Lecture 2: 02 Process Data For Machine Learning

Lecture 3: 03 Build Categories

Lecture 4: Source Files

Chapter 12: 09 Build Models for User Recommendations

Lecture 1: 04A Regression Introduction

Lecture 2: 04B What Is Regression

Lecture 3: 04C Build A Ridge Regression Model

Lecture 4: 05 Evaluate Model Error

Lecture 5: 06 Visualize Top Features Affecting Rating

Lecture 6: 07 Build A Lasso Regression Model

Lecture 7: 08 Visualize Top Features From Lasso Regression

Lecture 8: 09 Determine Which Model Is Best

Lecture 9: Source Files:

Chapter 13: 10 Build a Model to Predict Ratings

Lecture 1: 01 Load Data For A Neural Network

Lecture 2: 02 Build A Singular Value Decomposition Algorithm

Lecture 3: 03 Calculate Model Error

Lecture 4: Source Files

Chapter 14: 11 Deep Learning Fundamentals

Lecture 1: 01 What Is Deep Learning

Lecture 2: 02 What Is A Neural Network

Lecture 3: 03 What Is Unsupervised Learning

Chapter 15: 12 Build a Neural Network to Predict Ratings

Lecture 1: 04 Build A Neural Network

Lecture 2: 05 Train The Neural Network

Lecture 3: Source File

Chapter 16: 13 Data Analysis with Pandas, Numpy and Sci-kit Learn

Lecture 1: 00 Project Preview

Lecture 2: 01 Load Data Into Dataframes

Lecture 3: 02 Explore Data In Our Dataset

Lecture 4: 03 Build A Rating Pivot Table

Lecture 5: 04 Calculate Average Rating Of A Movie

Lecture 6: 05 Find Ratings For A Movie In Every Slice

Lecture 7: 06 Find Rating Averages For Every Movie In The Slice

Lecture 8: 07 Build An Average Ratings Column

Lecture 9: Source Files:

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

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2 stars: 0 votes

3 stars: 3 votes

4 stars: 0 votes

5 stars: 2 votes

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