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Cloudera Introduction to Data Science: Building Recommender Systems (CIDS)

Detailed Course Outline


  • About This Course
  • About Cloudera
  • Course Logistics
  • Introductions

Data Science Overview

  • What Is Data Science?
  • The Growing Need for Data Science
  • The Role of a Data Scientist

Use Cases

  • Finance
  • Retail
  • Advertising
  • Defense and Intelligence
  • Telecommunications and Utilities
  • Healthcare and Pharmaceuticals

Project Lifecycle

  • Steps in the Project Lifecycle
  • Lab Scenario Explanation

Data Acquisition

  • Where to Source Data
  • Acquisition Techniques

Evaluating Input Data

  • Data Formats
  • Data Quantity
  • Data Quality

Data Transformation

  • File Format Conversion
  • Joining Data Sets
  • Anonymization

Data Analysis and Statistical Methods

  • Relationship Between Statistics and Probability
  • Descriptive Statistics
  • Inferential Statistics
  • Vectors and Matrices

Fundamentals of Machine Learning

  • Overview
  • The Three C’s of Machine Learning
  • Importance of Data and Algorithms
  • Spotlight: Naive Bayes Classifiers

Recommender Overview

  • What is a Recommender System?
  • Types of Collaborative Filtering
  • Limitations of Recommender Systems
  • Fundamental Concepts

Introduction to Apache Spark and MLlib

  • What is Apache Spark?
  • Comparison to MapReduce
  • Fundamentals of Apache Spark
  • Spark’s MLlib Package

Implementing Recommenders with MLlib

  • Overview of ALS Method for Latent Factor Recommenders
  • Hyperparameters for ALS Recommenders
  • Building a Recommender in MLlib
  • Tuning Hyperparameters
  • Weighting

Experimentation and Evaluation

  • Designing Effective Experiments
  • Conducting an Effective Experiment
  • User Interfaces for Recommenders

Production Deployment and Beyond

  • Deploying to Production
  • Tips and Techniques for Working at Scale
  • Summarizing and Visualizing Results
  • Considerations for Improvement
  • Next Steps for Recommenders