Better Software Through Measurement

Pluralsight

Course Summary

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Course Description

This course will show you how to generate recommendations for your users, filter messages based on users' preferences, decide which web page performs best, keep track of timings in your application, and discover groups among items. These techniques are at the heart of many of the largest search engines and online retailers, but can be used to good effect for smaller companies. Throughout the course, the emphasis will be on examining and extending working sample code. The algorithms will be presented intuitively and you do not need any advanced math background.

Course Description

This course will show you how to generate recommendations for your users, filter messages based on users' preferences, decide which web page performs best, keep track of timings in your application, and discover groups among items. These techniques are at the heart of many of the largest search engines and online retailers, but can be used to good effect for smaller companies. Throughout the course, the emphasis will be on examining and extending working sample code. The algorithms will be presented intuitively and you do not need any advanced math background.

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Course Syllabus

Instrumentation: Streaming Metrics
- 19m 8s

—Measure Everything 3m 36s
—DEMO: Streaming Mean and Variance 2m 36s
—Quantiles with QDigest 5m 21s
—DEMO: QDigest Implementation 2m 46s
—Calculating Quantiles 1m 19s
—DEMO: Quantile Implementation 2m 28s
—Summary 1m 0s

Optimizing Conversion: A/B Testing
- 12m 11s

—Introduction 0m 17s
—The Basic Idea 2m 57s
—Introducing Google Content Experiments 1m 30s
—Demo: Defining A Goal 0m 49s
—Demo: Setting up A Content Experiment 1m 38s
—Waiting for Results 2m 32s
—Demo: Estimating Sample Size 1m 13s
—Summary 1m 13s

Recommendations: Item-based Recommendations
- 21m 23s

—Introduction 0m 25s
—Problem: Show Suggested Products 0m 53s
—The Fine Foods Data Set 2m 49s
—Recommendation Algorithm and Item Similarity 2m 43s
—Demo: Calculating Similarity Scores 3m 9s
—Demo: Top Similar Items 2m 2s
—Recommendations from Estimated Ratings 1m 30s
—Demo: Recommendations 5m 15s
—Further Ideas and Summary 2m 33s

Personalized Recommendations: Naive Bayesian Classifier
- 22m 46s

—Introduction 0m 32s
—Problem: Personalized Links 1m 21s
—Demo: Displaying Links 3m 56s
—Naive Bayesian Classifier 1m 52s
—Demo: Capturing user Likes/Dislikes 5m 8s
—Implementing A Naive Bayesian Classifier 1m 59s
—Demo: Implementing The Likelihoods Function 3m 57s
—Demo: Sorting and Displaying Recommendations 2m 44s
—Summary 1m 14s

Finding Groups: k-means Clustering
- 21m 58s

—Introduction 0m 38s
—Problem: Discovering Groups 2m 4s
—Demo: Getting A Friend List 1m 11s
—Demo: Sports Teams in Common 1m 35s
—Friend Versus Sports Team Matrix 0m 48s
—Demo: Creating The Friend Matrix 1m 32s
—Overview of k-means Clustering 2m 48s
—Tanimoto Distance 1m 37s
—Demo: Implementing The Tanimoto Distance 2m 54s
—Demo: Implementing k-means 4m 59s
—Summary 1m 47s