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 SDNB College

39,563 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 videos Total 121 minutes

Appetite Whetting: Bad Science 5 minutes
Hypothesis Testing 5 minutes
Significance Tests and P-Values 4 minutes
Example: Difference of Means 5 minutes
Deriving the Sampling Distribution 7 minutes
Shuffle Test for Significance 5 minutes
Comparing Classical and Resampling Methods 3 minutes
Bootstrap 6 minutes
Resampling Caveats 6 minutes
Outliers and Rank Transformation 3 minutes
Example: Chi-Squared Test 4 minutes
Bad Science Revisited: Publication Bias 5 minutes
Effect Size 4 minutes
Meta-analysis 5 minutes
Fraud and Benford's Law 4 minutes
Intuition for Benford's Law 3 minutes
Benford's Law Explained Visually 4 minutes
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections 4 minutes
Multiple Hypothesis Testing: False Discovery Rate 4 minutes
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure 3 minutes
Big Data and Spurious Correlations 5 minutes
Spurious Correlations: Stock Price Example 3 minutes
How is Big Data Different? 4 minutes
Bayesian vs. Frequentist 4 minutes
Motivation for Bayesian Approaches 4 minutes
Bayes' Theorem 3 minutes
Applying Bayes' Theorem 5 minutes
Naive Bayes: Spam Filtering 4 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 videos Total 111 minutes

Statistics vs. Machine Learning 4 minutes
Simple Examples 4 minutes
Structure of a Machine Learning Problem 5 minutes
Classification with Simple Rules 5 minutes
Learning Rules 4 minutes
Rules: Sequential Covering 3 minutes
Rules Recap 3 minutes
From Rules to Trees 3 minutes
Entropy 4 minutes
Measuring Entropy 4 minutes
Using Information Gain to Build Trees 6 minutes
Building Trees: ID3 Algorithm 2 minutes
Building Trees: C.45 Algorithm 4 minutes
Rules and Trees Recap 4 minutes
Overfitting 7 minutes
Evaluation: Leave One Out Cross Validation 6 minutes
Evaluation: Accuracy and ROC Curves 5 minutes
Bootstrap Revisited 4 minutes
Ensembles, Bagging, Boosting 4 minutes
Boosting Walkthrough 5 minutes
Random Forests 3 minutes
Random Forests: Variable Importance 5 minutes
Summary: Trees and Forests 3 minutes
Nearest Neighbor 4 minutes
Nearest Neighbor: Similarity Functions 4 minutes
Nearest Neighbor: Curse of Dimensionality 3 minutes

1 reading Total 10 minutes

R Assignment: Classification of Ocean Microbes 10 minutes

1 assignment Total 30 minutes

R Assignment: Classification of Ocean Microbes 30 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 videos Total 41 minutes

Optimization by Gradient Descent 3 minutes
Gradient Descent Visually 4 minutes
Gradient Descent in Detail 3 minutes
Gradient Descent: Questions to Consider 4 minutes
Intuition for Logistic Regression 4 minutes
Intuition for Support Vector Machines 4 minutes
Support Vector Machine Example 3 minutes
Intuition for Regularization 3 minutes
Intuition for LASSO and Ridge Regression 4 minutes
Stochastic and Batched Gradient Descent 5 minutes
Parallelizing Gradient Descent 4 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 Courses 91,984 learners

Offered by

University of Washington

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Reviewed on Nov 10, 2015

Very nice assignments and content. You learn a lot when you complete all assignments.

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 Jun 5, 2016

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.

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