Building Recommender Systems with Machine Learning and AI
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       Video Length : 17h50m0s
  • format_list_bulleted
       Tasks Number : 120
  • group
       Students Enrolled : 1716
  • equalizer
       Medium Level
  • Curriculum
  • 1. Getting Started
    • videocam
      Getting the Most From This Course
      10m0s
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      Install Anaconda, course materials, and create movie recommendations!
      10m0s
    • videocam
      Course Roadmap
      10m0s
    • videocam
      Types of Recommenders
      10m0s
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      Understanding You through Implicit and Explicit Ratings
      10m0s
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      Top-N Recommender Architecture
      10m0s
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      Review the basics of recommender systems.
      10m0s
  • 2. Introduction to Python
    • videocam
      The Basics of Python
      10m0s
    • videocam
      Data Structures in Python
      10m0s
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      Functions in Python
      10m0s
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      Booleans, loops, and a hands-on challenge
      10m0s
  • 3. Evaluating Recommender Systems
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      Train/Test and Cross Validation
      10m0s
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      Accuracy Metrics (RMSE, MAE)
      10m0s
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      Top-N Hit Rate - Many Ways
      10m0s
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      Coverage, Diversity, and Novelty
      10m0s
    • videocam
      Churn, Responsiveness, and A/B Tests
      10m0s
    • videocam
      Review ways to measure your recommender.
      10m0s
    • videocam
      Walkthrough of RecommenderMetrics.py
      10m0s
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      Walkthrough of TestMetrics.py
      10m0s
    • videocam
      Measure the Performance of SVD Recommendations
      10m0s
  • 4. A Recommender Engine Framework
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      Our Recommender Engine Architecture
      10m0s
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      Recommender Engine Walkthrough, Part 1
      10m0s
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      Recommender Engine Walkthrough, Part 2
      10m0s
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      Review the Results of our Algorithm Evaluation.
      10m0s
  • 5. Content-Based Filtering
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      Content-Based Recommendations, and the Cosine Similarity Metric
      10m0s
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      K-Nearest-Neighbors and Content Recs
      10m0s
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      Producing and Evaluating Content-Based Movie Recommendations
      10m0s
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      Bleeding Edge Alert! Mise en Scene Recommendations
      10m0s
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      Dive Deeper into Content-Based Recommendations
      10m0s
  • 6. Neighborhood-Based Collaborative Filtering
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      Measuring Similarity, and Sparsity
      10m0s
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      Similarity Metrics
      10m0s
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      User-based Collaborative Filtering
      10m0s
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      User-based Collaborative Filtering, Hands-On
      10m0s
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      Item-based Collaborative Filtering
      10m0s
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      Item-based Collaborative Filtering, Hands-On
      10m0s
    • videocam
      Tuning Collaborative Filtering Algorithms
      10m0s
    • videocam
      Evaluating Collaborative Filtering Systems Offline
      10m0s
    • videocam
      Measure the Hit Rate of Item-Based Collaborative Filtering
      10m0s
    • videocam
      KNN Recommenders
      10m0s
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      Running User and Item-Based KNN on MovieLens
      10m0s
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      Experiment with different KNN parameters.
      10m0s
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      Bleeding Edge Alert! Translation-Based Recommendations
      10m0s
  • 7. Matrix Factorization Methods
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      Principal Component Analysis (PCA)
      10m0s
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      Singular Value Decomposition
      10m0s
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      Running SVD and SVD++ on MovieLens
      10m0s
    • videocam
      Improving on SVD
      10m0s
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      Tune the hyperparameters on SVD
      10m0s
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      Bleeding Edge Alert! Sparse Linear Methods (SLIM)
      10m0s
  • 8. Introduction to Deep Learning [Optional]
    • videocam
      Deep Learning Introduction
      10m0s
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      Deep Learning Pre-Requisites
      10m0s
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      History of Artificial Neural Networks
      10m0s
    • videocam
      Playing with Tensorflow
      10m0s
    • videocam
      Training Neural Networks
      10m0s
    • videocam
      Tuning Neural Networks
      10m0s
    • videocam
      Introduction to Tensorflow
      10m0s
    • videocam
      Handwriting Recognition with Tensorflow, part 1
      10m0s
    • videocam
      Handwriting Recognition with Tensorflow, part 2
      10m0s
    • videocam
      Introduction to Keras
      10m0s
    • videocam
      Handwriting Recognition with Keras
      10m0s
    • videocam
      Classifier Patterns with Keras
      10m0s
    • videocam
      Predict Political Parties of Politicians with Keras
      10m0s
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      Intro to Convolutional Neural Networks (CNN's)
      10m0s
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      CNN Architectures
      10m0s
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      Handwriting Recognition with Convolutional Neural Networks (CNNs)
      10m0s
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      Intro to Recurrent Neural Networks (RNN's)
      10m0s
    • videocam
      Training Recurrent Neural Networks
      10m0s
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      Sentiment Analysis of Movie Reviews using RNN's and Keras
      10m0s
  • 9. Deep Learning for Recommender Systems
    • videocam
      Intro to Deep Learning for Recommenders
      10m0s
    • videocam
      Restricted Boltzmann Machines (RBM's)
      10m0s
    • videocam
      Recommendations with RBM's, part 1
      10m0s
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      Recommendations with RBM's, part 2
      10m0s
    • videocam
      Evaluating the RBM Recommender
      10m0s
    • videocam
      Tuning Restricted Boltzmann Machines
      10m0s
    • videocam
      Exercise Results: Tuning a RBM Recommender
      10m0s
    • videocam
      Auto-Encoders for Recommendations: Deep Learning for Recs
      10m0s
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      Recommendations with Deep Neural Networks
      10m0s
    • videocam
      Clickstream Recommendations with RNN's
      10m0s
    • videocam
      Get GRU4Rec Working on your Desktop
      10m0s
    • videocam
      Exercise Results: GRU4Rec in Action
      10m0s
    • videocam
      Bleeding Edge Alert! Deep Factorization Machines
      10m0s
    • videocam
      More Emerging Tech to Watch
      10m0s
  • 10. Scaling it Up
    • videocam
      Introduction and Installation of Apache Spark
      10m0s
    • videocam
      Apache Spark Architecture
      10m0s
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      Movie Recommendations with Spark, Matrix Factorization, and ALS
      10m0s
    • videocam
      Recommendations from 20 million ratings with Spark
      10m0s
    • videocam
      Amazon DSSTNE
      10m0s
    • videocam
      DSSTNE in Action
      10m0s
    • videocam
      Scaling Up DSSTNE
      10m0s
    • videocam
      AWS SageMaker and Factorization Machines
      10m0s
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      SageMaker in Action: Factorization Machines on one million ratings, in the cloud
      10m0s
  • 11. Real-World Challenges of Recommender Systems
    • videocam
      The Cold Start Problem (and solutions)
      10m0s
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      Implement Random Exploration
      10m0s
    • videocam
      Exercise Solution: Random Exploration
      10m0s
    • videocam
      Stoplists
      10m0s
    • videocam
      Implement a Stoplist
      10m0s
    • videocam
      Exercise Solution: Implement a Stoplist
      10m0s
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      Filter Bubbles, Trust, and Outliers
      10m0s
    • videocam
      Identify and Eliminate Outlier Users
      10m0s
    • videocam
      Exercise Solution: Outlier Removal
      10m0s
    • videocam
      Fraud, The Perils of Clickstream, and International Concerns
      10m0s
    • videocam
      Temporal Effects, and Value-Aware Recommendations
      10m0s
  • 12. Case Studies
    • videocam
      Case Study: YouTube, Part 1
      10m0s
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      Case Study: YouTube, Part 2
      10m0s
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      Case Study: Netflix, Part 1
      10m0s
    • videocam
      Case Study: Netflix, Part 2
      10m0s
  • 13. Hybrid Approaches
    • videocam
      Hybrid Recommenders and Exercise
      10m0s
    • videocam
      Exercise Solution: Hybrid Recommenders
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Building Recommender Systems with Machine Learning and AI


Building Recommender Systems with Machine Learning and AI

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