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

Access provided by Barbados NTI

39,588 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

323 reviews

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

323 reviews

8 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment¹

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Taught in English

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This course is part of the Data Science at Scale Specialization

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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 minutes
Hypothesis Testing5 minutes
Significance Tests and P-Values4 minutes
Example: Difference of Means5 minutes
Deriving the Sampling Distribution7 minutes
Shuffle Test for Significance5 minutes
Comparing Classical and Resampling Methods3 minutes
Bootstrap6 minutes
Resampling Caveats6 minutes
Outliers and Rank Transformation3 minutes
Example: Chi-Squared Test4 minutes
Bad Science Revisited: Publication Bias5 minutes
Effect Size4 minutes
Meta-analysis5 minutes
Fraud and Benford's Law4 minutes
Intuition for Benford's Law3 minutes
Benford's Law Explained Visually4 minutes
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections4 minutes
Multiple Hypothesis Testing: False Discovery Rate4 minutes
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3 minutes
Big Data and Spurious Correlations5 minutes
Spurious Correlations: Stock Price Example3 minutes
How is Big Data Different?4 minutes
Bayesian vs. Frequentist4 minutes
Motivation for Bayesian Approaches4 minutes
Bayes' Theorem3 minutes
Applying Bayes' Theorem5 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 111 minutes

Statistics vs. Machine Learning4 minutes
Simple Examples4 minutes
Structure of a Machine Learning Problem5 minutes
Classification with Simple Rules5 minutes
Learning Rules4 minutes
Rules: Sequential Covering3 minutes
Rules Recap3 minutes
From Rules to Trees3 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 Recap4 minutes
Overfitting7 minutes
Evaluation: Leave One Out Cross Validation6 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 Forests3 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 minutes
Gradient Descent Visually4 minutes
Gradient Descent in Detail3 minutes
Gradient Descent: Questions to Consider4 minutes
Intuition for Logistic Regression4 minutes
Intuition for Support Vector Machines4 minutes
Support Vector Machine Example3 minutes
Intuition for Regularization3 minutes
Intuition for LASSO and Ridge Regression4 minutes
Stochastic and Batched Gradient Descent5 minutes
Parallelizing Gradient Descent4 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

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Instructor

Instructor ratings

(5 ratings)

Bill Howe

University of Washington

4 Courses92,083 learners

Offered by

University of Washington

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Reviewed on Dec 22, 2016

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

Reviewed on Aug 6, 2019

Too little people participated and long peer review time.But the course content is good.

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.

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