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Udemy - Building Recommender Systems with Machine Learning and AI [Course Drive]

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Udemy - Building Recommender Systems with Machine Learning and AI [Course Drive] (Size: 4.4 GB)
  1. Case Study YouTube, Part 1.mp4 26.9 MB
  1. Case Study YouTube, Part 1.srt 7.9 KB
  1. Content-Based Recommendations, and the Cosine Similarity Metric.mp4 61.6 MB
  1. Content-Based Recommendations, and the Cosine Similarity Metric.srt 19.8 KB
  1. Deep Learning Introduction.mp4 17.6 MB
  1. Deep Learning Introduction.srt 3.7 KB
  1. Hybrid Recommenders and Exercise.mp4 18.4 MB
  1. Hybrid Recommenders and Exercise.srt 6 KB
  1. Intro to Deep Learning for Recommenders.mp4 42.7 MB
  1. Intro to Deep Learning for Recommenders.srt 5.3 KB
  1. Measuring Similarity, and Sparsity.mp4 59.1 MB
  1. Measuring Similarity, and Sparsity.srt 12 KB
  1. More to Explore.mp4 38.9 MB
  1. More to Explore.srt 5.5 KB
  1. Our Recommender Engine Architecture.mp4 32.7 MB
  1. Our Recommender Engine Architecture.srt 16.3 KB
  1. Principal Component Analysis (PCA).mp4 61.2 MB
  1. Principal Component Analysis (PCA).srt 15.7 KB
  1. The Cold Start Problem (and solutions).mp4 27.8 MB
  1. The Cold Start Problem (and solutions).srt 14.6 KB
  1. TrainTest and Cross Validation.mp4 29 MB
  1. TrainTest and Cross Validation.srt 9.2 KB
  1. Udemy 101 Getting the Most From This Course.mp4 19.7 MB
  1. Udemy 101 Getting the Most From This Course.srt 4 KB
  1. [Activity] Introduction and Installation of Apache Spark.mp4 53.3 MB
  1. [Activity] Introduction and Installation of Apache Spark.srt 9.2 KB
  1. [Activity] The Basics of Python.mp4 43 MB
  1. [Activity] The Basics of Python.srt 9.6 KB
  10. Clickstream Recommendations with RNN's.mp4 48.7 MB
  10. Clickstream Recommendations with RNN's.srt 16.8 KB
  10. Fraud, The Perils of Clickstream, and International Concerns.mp4 58.2 MB
  10. Fraud, The Perils of Clickstream, and International Concerns.srt 10.6 KB
  10. KNN Recommenders.mp4 24.8 MB
  10. KNN Recommenders.srt 9.1 KB
  10. [Activity] Handwriting Recognition with Tensorflow, Part 3.mp4 50.4 MB
  10. [Activity] Handwriting Recognition with Tensorflow, Part 3.srt 13.4 KB
  11. Introduction to Keras.mp4 16.5 MB
  11. Introduction to Keras.srt 6.8 KB
  11. Temporal Effects, and Value-Aware Recommendations.mp4 54 MB
  11. Temporal Effects, and Value-Aware Recommendations.srt 8.3 KB
  11. [Activity] Running User and Item-Based KNN on MovieLens.mp4 23.8 MB
  11. [Activity] Running User and Item-Based KNN on MovieLens.srt 5.1 KB
  11. [Exercise] Get GRU4Rec Working on your Desktop.mp4 7.5 MB
  11. [Exercise] Get GRU4Rec Working on your Desktop.srt 5.8 KB
  12. Exercise Results GRU4Rec in Action.mp4 62.7 MB
  12. Exercise Results GRU4Rec in Action.srt 18.1 KB
  12. [Activity] Handwriting Recognition with Keras.mp4 88.5 MB
  12. [Activity] Handwriting Recognition with Keras.srt 21.3 KB
  12. [Exercise] Experiment with different KNN parameters..mp4 41.3 MB
  12. [Exercise] Experiment with different KNN parameters..srt 9.7 KB
  13. Bleeding Edge Alert! Deep Factorization Machines.mp4 57.4 MB
  13. Bleeding Edge Alert! Deep Factorization Machines.srt 12.6 KB
  13. Bleeding Edge Alert! Translation-Based Recommendations.mp4 21.3 MB
  13. Bleeding Edge Alert! Translation-Based Recommendations.srt 5.3 KB
  13. Classifier Patterns with Keras.mp4 24.8 MB
  13. Classifier Patterns with Keras.srt 8.8 KB
  14. More Emerging Tech to Watch.mp4 27.6 MB
  14. More Emerging Tech to Watch.srt 11.6 KB
  14. [Exercise] Predict Political Parties of Politicians with Keras.mp4 100.2 MB
  14. [Exercise] Predict Political Parties of Politicians with Keras.srt 19.8 KB
  15. Intro to Convolutional Neural Networks (CNN's).mp4 78.2 MB
  15. Intro to Convolutional Neural Networks (CNN's).srt 19.8 KB
  16. CNN Architectures.mp4 22.5 MB
  16. CNN Architectures.srt 6.9 KB
  17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp4 82.3 MB
  17. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).srt 18.5 KB
  18. Intro to Recurrent Neural Networks (RNN's).mp4 49.6 MB
  18. Intro to Recurrent Neural Networks (RNN's).srt 17.2 KB
  19. Training Recurrent Neural Networks.mp4 20.7 MB
  19. Training Recurrent Neural Networks.srt 7.3 KB
  2. Accuracy Metrics (RMSE, MAE).mp4 40.3 MB
  2. Accuracy Metrics (RMSE, MAE).srt 9.3 KB
  2. Apache Spark Architecture.mp4 17.4 MB
  2. Apache Spark Architecture.srt 11.3 KB
  2. Bonus Lecture Discounts to continue your journey!.html 7.1 KB
  2. Case Study YouTube, Part 2.mp4 26.3 MB
  2. Case Study YouTube, Part 2.srt 15.7 KB
  2. Data Structures in Python.mp4 24.4 MB
  2. Data Structures in Python.srt 10.4 KB
  2. Deep Learning Pre-Requisites.mp4 37 MB
  2. Deep Learning Pre-Requisites.srt 19.7 KB
  2. Exercise Solution Hybrid Recommenders.mp4 33.2 MB
  2. Exercise Solution Hybrid Recommenders.srt 9.2 KB
  2. K-Nearest-Neighbors and Content Recs.mp4 19.6 MB
  2. K-Nearest-Neighbors and Content Recs.srt 8.9 KB
  2. Restricted Boltzmann Machines (RBM's).mp4 31.7 MB
  2. Restricted Boltzmann Machines (RBM's).srt 18 KB
  2. Similarity Metrics.mp4 30.7 MB
  2. Similarity Metrics.srt 20.9 KB
  2. Singular Value Decomposition.mp4 24.3 MB
  2. Singular Value Decomposition.srt 14.4 KB
  2. [Activity] Install Anaconda, course materials, and create movie recommendations!.mp4 104.1 MB
  2. [Activity] Install Anaconda, course materials, and create movie recommendations!.srt 17.1 KB
  2. [Activity] Recommender Engine Walkthrough, Part 1.mp4 37.9 MB
  2. [Activity] Recommender Engine Walkthrough, Part 1.srt 8.1 KB
  2. [Exercise] Implement Random Exploration.mp4 2.2 MB
  2. [Exercise] Implement Random Exploration.srt 2 KB
  20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.mp4 119.8 MB
  20. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras.srt 25.1 KB
  3. Case Study Netflix, Part 1.mp4 27.6 MB
  3. Case Study Netflix, Part 1.srt 8.4 KB
  3. Course Roadmap.mp4 27.6 MB
  3. Course Roadmap.srt 9.9 KB
  3. Exercise Solution Random Exploration.mp4 24.2 MB
  3. Exercise Solution Random Exploration.srt 4.6 KB
  3. Functions in Python.mp4 12.3 MB
  3. Functions in Python.srt 5.7 KB
  3. History of Artificial Neural Networks.mp4 84.2 MB
  3. History of Artificial Neural Networks.srt 24.9 KB
  3. Top-N Hit Rate - Many Ways.mp4 24.5 MB
  3. Top-N Hit Rate - Many Ways.srt 10.1 KB
  3. User-based Collaborative Filtering.mp4 34.2 MB
  3. User-based Collaborative Filtering.srt 15.7 KB
  3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp4 55.6 MB
  3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.srt 12.3 KB
  3. [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp4 52.4 MB
  3. [Activity] Producing and Evaluating Content-Based Movie Recommendations.srt 11.4 KB
  3. [Activity] Recommendations with RBM's, part 1.mp4 144.4 MB
  3. [Activity] Recommendations with RBM's, part 1.srt 27.7 KB
  3. [Activity] Recommender Engine Walkthrough, Part 2.mp4 39.6 MB
  3. [Activity] Recommender Engine Walkthrough, Part 2.srt 8.7 KB
  3. [Activity] Running SVD and SVD++ on MovieLens.mp4 37.5 MB
  3. [Activity] Running SVD and SVD++ on MovieLens.srt 7 KB
  4. Case Study Netflix, Part 2.mp4 26.6 MB
  4. Case Study Netflix, Part 2.srt 8.4 KB
  4. Coverage, Diversity, and Novelty.mp4 13.7 MB
  4. Coverage, Diversity, and Novelty.srt 11.9 KB
  4. Improving on SVD.mp4 23.1 MB
  4. Improving on SVD.srt 10 KB
  4. Stoplists.mp4 19.9 MB
  4. Stoplists.srt 11.3 KB
  4. Types of Recommenders.mp4 26.8 MB
  4. Types of Recommenders.srt 7.3 KB
  4. [Activity] Bleeding Edge Alert! Mise en Scene Recommendations.mp4 46.5 MB
  4. [Activity] Bleeding Edge Alert! Mise en Scene Recommendations.srt 9.5 KB
  4. [Activity] Playing with Tensorflow.mp4 145.6 MB
  4. [Activity] Playing with Tensorflow.srt 24.9 KB
  4. [Activity] Recommendations from 20 million ratings with Spark.mp4 50.7 MB
  4. [Activity] Recommendations from 20 million ratings with Spark.srt 9.2 KB
  4. [Activity] Recommendations with RBM's, part 2.mp4 76.7 MB
  4. [Activity] Recommendations with RBM's, part 2.srt 15.5 KB
  4. [Activity] Review the Results of our Algorithm Evaluation..mp4 34.6 MB
  4. [Activity] Review the Results of our Algorithm Evaluation..srt 7 KB
  4. [Activity] User-based Collaborative Filtering, Hands-On.mp4 48.6 MB
  4. [Activity] User-based Collaborative Filtering, Hands-On.srt 10.7 KB
  4. [Exercise] Booleans, loops, and a hands-on challenge.mp4 13.9 MB
  4. [Exercise] Booleans, loops, and a hands-on challenge.srt 6.9 KB
  5. Amazon DSSTNE.mp4 42.3 MB
  5. Amazon DSSTNE.srt 10.1 KB
  5. Churn, Responsiveness, and AB Tests.mp4 60.9 MB
  5. Churn, Responsiveness, and AB Tests.srt 12.1 KB
  5. Item-based Collaborative Filtering.mp4 52.3 MB
  5. Item-based Collaborative Filtering.srt 9.9 KB
  5. Training Neural Networks.mp4 38.3 MB
  5. Training Neural Networks.srt 13.8 KB
  5. Understanding You through Implicit and Explicit Ratings.mp4 20.7 MB
  5. Understanding You through Implicit and Explicit Ratings.srt 9.6 KB
  5. [Activity] Evaluating the RBM Recommender.mp4 37.7 MB
  5. [Activity] Evaluating the RBM Recommender.srt 7.3 KB
  5. [Exercise] Dive Deeper into Content-Based Recommendations.mp4 24.1 MB
  5. [Exercise] Dive Deeper into Content-Based Recommendations.srt 9.3 KB
  5. [Exercise] Implement a Stoplist.mp4 1.4 MB
  5. [Exercise] Implement a Stoplist.srt 1.4 KB
  5. [Exercise] Tune the hyperparameters on SVD.mp4 11.9 MB
  5. [Exercise] Tune the hyperparameters on SVD.srt 4.4 KB
  6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp4 26.5 MB
  6. Bleeding Edge Alert! Sparse Linear Methods (SLIM).srt 7.8 KB
  6. DSSTNE in Action.mp4 113.7 MB
  6. DSSTNE in Action.srt 12.9 KB
  6. Exercise Solution Implement a Stoplist.mp4 26.7 MB
  6. Exercise Solution Implement a Stoplist.srt 4.7 KB
  6. Top-N Recommender Architecture.mp4 37.1 MB
  6. Top-N Recommender Architecture.srt 12.8 KB
  6. Tuning Neural Networks.mp4 31 MB
  6. Tuning Neural Networks.srt 8.9 KB
  6. [Activity] Item-based Collaborative Filtering, Hands-On.mp4 26.8 MB
  6. [Activity] Item-based Collaborative Filtering, Hands-On.srt 5.4 KB
  6. [Exercise] Tuning Restricted Boltzmann Machines.mp4 33.6 MB
  6. [Exercise] Tuning Restricted Boltzmann Machines.srt 4.2 KB
  6. [Quiz] Review ways to measure your recommender..mp4 12.8 MB
  6. [Quiz] Review ways to measure your recommender..srt 6 KB
  7. Exercise Results Tuning a RBM Recommender.mp4 11.8 MB
  7. Exercise Results Tuning a RBM Recommender.srt 2.7 KB
  7. Filter Bubbles, Trust, and Outliers.mp4 92.4 MB
  7. Filter Bubbles, Trust, and Outliers.srt 13.4 KB
  7. Introduction to Tensorflow.mp4 92.5 MB
  7. Introduction to Tensorflow.srt 28.7 KB
  7. Scaling Up DSSTNE.mp4 10.4 MB
  7. Scaling Up DSSTNE.srt 4.6 KB
  7. [Activity] Walkthrough of RecommenderMetrics.py.mp4 64.3 MB
  7. [Activity] Walkthrough of RecommenderMetrics.py.srt 14.1 KB
  7. [Exercise] Tuning Collaborative Filtering Algorithms.mp4 19.7 MB
  7. [Exercise] Tuning Collaborative Filtering Algorithms.srt 7.6 KB
  7. [Quiz] Review the basics of recommender systems..mp4 21.3 MB
  7. [Quiz] Review the basics of recommender systems..srt 9.8 KB
  8. AWS SageMaker and Factorization Machines.mp4 15.6 MB
  8. AWS SageMaker and Factorization Machines.srt 9.3 KB
  8. Auto-Encoders for Recommendations Deep Learning for Recs.mp4 26.9 MB
  8. Auto-Encoders for Recommendations Deep Learning for Recs.srt 10.4 KB
  8. [Activity] Evaluating Collaborative Filtering Systems Offline.mp4 15.4 MB
  8. [Activity] Evaluating Collaborative Filtering Systems Offline.srt 2.8 KB
  8. [Activity] Handwriting Recognition with Tensorflow, part 1.mp4 91 MB
  8. [Activity] Handwriting Recognition with Tensorflow, part 1.srt 16.9 KB
  8. [Activity] Walkthrough of TestMetrics.py.mp4 54.4 MB
  8. [Activity] Walkthrough of TestMetrics.py.srt 11.9 KB
  8. [Exercise] Identify and Eliminate Outlier Users.mp4 1.8 MB
  8. [Exercise] Identify and Eliminate Outlier Users.srt 2 KB
  9. Exercise Solution Outlier Removal.mp4 38.5 MB
  9. Exercise Solution Outlier Removal.srt 8.3 KB
  9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp4 68.3 MB
  9. SageMaker in Action Factorization Machines on one million ratings, in the cloud.srt 14.2 KB
  9. [Activity] Handwriting Recognition with Tensorflow, part 2.mp4 90.9 MB
  9. [Activity] Handwriting Recognition with Tensorflow, part 2.srt 19.9 KB
  9. [Activity] Measure the Performance of SVD Recommendations.mp4 21.6 MB
  9. [Activity] Measure the Performance of SVD Recommendations.srt 5.5 KB
  9. [Activity] Recommendations with Deep Neural Networks.mp4 75.4 MB
  9. [Activity] Recommendations with Deep Neural Networks.srt 14.7 KB
  9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp4 9.5 MB
  9. [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.srt 4.9 KB
  ReadMe.txt 204 B
  Visit Coursedrive.org.url 102 B
  ▲ 223 total files

Description


Building Recommender Systems with Machine Learning and AI

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.


Requirements

• A Windows, Mac, or Linux PC with at least 3GB of free disk space.
• Some experience with a programming or scripting language (preferably Python)
• Some computer science background, and an ability to understand new algorithms.

Description

New! Updated for Tensorflow 2.
Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies.
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. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.
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. We'll cover:
• Building a recommendation engine
• Evaluating recommender systems
• Content-based filtering using item attributes
• Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
• Model-based methods including matrix factorization and SVD
• Applying deep learning, AI, and artificial neural networks to recommendations
• Session-based recommendations with recursive neural networks
• Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines
• Real-world challenges and solutions with recommender systems
• Case studies from YouTube and Netflix
• Building hybrid, ensemble recommenders
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.
High-quality, hand-edited English closed captions are included to help you follow along.
I hope to see you in the course soon!

Who this course is for:

• 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



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