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Practical Predictive Analytics: Models and Methods
  • Level Foundation
  • Duration 7 hours
  • Course by University of Washington
  • Offered by

About

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

Modules

Lesson 1: Basics of Statistical Inference

    • 3 Videos
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3 Videos

  • Appetite Whetting: Bad Science
  • Hypothesis Testing
  • Significance Tests and P-Values

Lesson 2: Resampling Methods

    • 6 Videos
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6 Videos

  • Example: Difference of Means
  • Deriving the Sampling Distribution
  • Shuffle Test for Significance
  • Comparing Classical and Resampling Methods
  • Bootstrap
  • Resampling Caveats

Lesson 3: Practical Issues

    • 8 Videos
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8 Videos

  • Outliers and Rank Transformation
  • Example: Chi-Squared Test
  • Bad Science Revisited: Publication Bias
  • Effect Size
  • Meta-analysis
  • Fraud and Benford's Law
  • Intuition for Benford's Law
  • Benford's Law Explained Visually

Lesson 4: What Can Go Wrong

    • 6 Videos
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6 Videos

  • Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
  • Multiple Hypothesis Testing: False Discovery Rate
  • Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
  • Big Data and Spurious Correlations
  • Spurious Correlations: Stock Price Example
  • How is Big Data Different?

Lesson 5: Introduction to Bayesian Approaches

    • 5 Videos
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5 Videos

  • Bayesian vs. Frequentist
  • Motivation for Bayesian Approaches
  • Bayes' Theorem
  • Applying Bayes' Theorem
  • Naive Bayes: Spam Filtering

Lesson 6: Learning with Rules

    • 7 Videos
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7 Videos

  • Statistics vs. Machine Learning
  • Simple Examples
  • Structure of a Machine Learning Problem
  • Classification with Simple Rules
  • Learning Rules
  • Rules: Sequential Covering
  • Rules Recap

Lesson 7: Learning with Trees

    • 7 Videos
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7 Videos

  • From Rules to Trees
  • Entropy
  • Measuring Entropy
  • Using Information Gain to Build Trees
  • Building Trees: ID3 Algorithm
  • Building Trees: C.45 Algorithm
  • Rules and Trees Recap

Lesson 8: Evaluation

    • 3 Videos
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3 Videos

  • Overfitting
  • Evaluation: Leave One Out Cross Validation
  • Evaluation: Accuracy and ROC Curves

Lesson 9: Ensemble Learning

    • 6 Videos
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6 Videos

  • Bootstrap Revisited
  • Ensembles, Bagging, Boosting
  • Boosting Walkthrough
  • Random Forests
  • Random Forests: Variable Importance
  • Summary: Trees and Forests

Lesson 10: Nearest Neighbor

    • 3 Videos
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3 Videos

  • Nearest Neighbor
  • Nearest Neighbor: Similarity Functions
  • Nearest Neighbor: Curse of Dimensionality

Assignment: Supervised Learning

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • R Assignment: Classification of Ocean Microbes

1 Readings

  • R Assignment: Classification of Ocean Microbes

Lesson 11: Gradient Descent

    • 4 Videos
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4 Videos

  • Optimization by Gradient Descent
  • Gradient Descent Visually
  • Gradient Descent in Detail
  • Gradient Descent: Questions to Consider

Lesson 12: Generalizing the Cost Function

    • 5 Videos
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5 Videos

  • Intuition for Logistic Regression
  • Intuition for Support Vector Machines
  • Support Vector Machine Example
  • Intuition for Regularization
  • Intuition for LASSO and Ridge Regression

Lesson 13: Algorithmic Considerations

    • 2 Videos
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2 Videos

  • Stochastic and Batched Gradient Descent
  • Parallelizing Gradient Descent

Lesson 14: Selected Algorithms

    • 4 Videos
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4 Videos

  • Introduction to Unsupervised Learning
  • K-means
  • DBSCAN
  • DBSCAN Variable Density and Parallel Algorithms

Kaggle Competition Peer Review

    • 1 PeerReview
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1 Peer Review

  • Kaggle Competition Peer Review

Auto Summary

"Practical Predictive Analytics: Models and Methods" is a foundational Coursera course in Data Science & AI, led by expert instructors. It covers statistical experiment design, modern analysis methods, and machine learning techniques like classification, optimization, and unsupervised learning. The course spans 420 minutes and offers Starter and Professional subscription options. Ideal for those looking to solve real-world problems through effective predictive analytics.

Bill Howe
Instructor

Bill Howe