Practical Predictive Analytics: Models and Methods

Practical Predictive Analytics: Models and Methods

This course is part of Data Science at Scale Specialization

Instructor: Bill Howe

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Assessments

1 assignment

Taught in English

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There are 4 modules in this course

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

Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.

What's included

28 videos

28 videosTotal 121 minutes

Appetite Whetting: Bad Science5 minutesPreview module
Hypothesis Testing5 minutes
Significance Tests and P-Values3 minutes
Example: Difference of Means4 minutes
Deriving the Sampling Distribution6 minutes
Shuffle Test for Significance4 minutes
Comparing Classical and Resampling Methods3 minutes
Bootstrap6 minutes
Resampling Caveats6 minutes
Outliers and Rank Transformation3 minutes
Example: Chi-Squared Test3 minutes
Bad Science Revisited: Publication Bias4 minutes
Effect Size4 minutes
Meta-analysis5 minutes
Fraud and Benford's Law4 minutes
Intuition for Benford's Law2 minutes
Benford's Law Explained Visually3 minutes
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3 minutes
Multiple Hypothesis Testing: False Discovery Rate4 minutes
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3 minutes
Big Data and Spurious Correlations4 minutes
Spurious Correlations: Stock Price Example3 minutes
How is Big Data Different?3 minutes
Bayesian vs. Frequentist4 minutes
Motivation for Bayesian Approaches3 minutes
Bayes' Theorem2 minutes
Applying Bayes' Theorem4 minutes
Naive Bayes: Spam Filtering4 minutes

Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.

What's included

26 videos1 reading1 assignment

26 videosTotal 110 minutes

Statistics vs. Machine Learning4 minutesPreview module
Simple Examples3 minutes
Structure of a Machine Learning Problem5 minutes
Classification with Simple Rules5 minutes
Learning Rules4 minutes
Rules: Sequential Covering3 minutes
Rules Recap2 minutes
From Rules to Trees2 minutes
Entropy4 minutes
Measuring Entropy4 minutes
Using Information Gain to Build Trees6 minutes
Building Trees: ID3 Algorithm2 minutes
Building Trees: C.45 Algorithm4 minutes
Rules and Trees Recap3 minutes
Overfitting7 minutes
Evaluation: Leave One Out Cross Validation5 minutes
Evaluation: Accuracy and ROC Curves5 minutes
Bootstrap Revisited4 minutes
Ensembles, Bagging, Boosting4 minutes
Boosting Walkthrough5 minutes
Random Forests3 minutes
Random Forests: Variable Importance5 minutes
Summary: Trees and Forests2 minutes
Nearest Neighbor4 minutes
Nearest Neighbor: Similarity Functions4 minutes
Nearest Neighbor: Curse of Dimensionality3 minutes

1 readingTotal 10 minutes

R Assignment: Classification of Ocean Microbes10 minutes

1 assignmentTotal 30 minutes

R Assignment: Classification of Ocean Microbes30 minutes

You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.

What's included

11 videos

11 videosTotal 41 minutes

Optimization by Gradient Descent3 minutesPreview module
Gradient Descent Visually4 minutes
Gradient Descent in Detail2 minutes
Gradient Descent: Questions to Consider3 minutes
Intuition for Logistic Regression4 minutes
Intuition for Support Vector Machines3 minutes
Support Vector Machine Example3 minutes
Intuition for Regularization3 minutes
Intuition for LASSO and Ridge Regression3 minutes
Stochastic and Batched Gradient Descent5 minutes
Parallelizing Gradient Descent3 minutes

A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.

What's included

4 videos1 peer review

Instructor

Bill Howe

University of Washington

4 Courses89,298 learners

Offered by

University of Washington

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Reviewed on Jun 12, 2017

Very good approach to each method; the assignments are a good test for the topics.

Reviewed on Jun 7, 2017

I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.

Reviewed on Feb 3, 2017

Need some background in R or Python and the lectures are from around 2013. Most of the material is still relevant.

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