Practical Predictive Analytics: Models and Methods

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Practical Predictive Analytics: Models and Methods

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About 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 arg…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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

Created by:  University of Washington
  • Taught by:  Bill Howe, Director of Research

    Scalable Data Analytics
Basic Info Course 2 of 4 in the Data Science at Scale Specialization Commitment 4 weeks of study, 6-8 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Washington Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.

Syllabus


WEEK 1


Practical Statistical Inference



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.


28 videos expand


  1. Video: Appetite Whetting: Bad Science
  2. Video: Hypothesis Testing
  3. Video: Significance Tests and P-Values
  4. Video: Example: Difference of Means
  5. Video: Deriving the Sampling Distribution
  6. Video: Shuffle Test for Significance
  7. Video: Comparing Classical and Resampling Methods
  8. Video: Bootstrap
  9. Video: Resampling Caveats
  10. Video: Outliers and Rank Transformation
  11. Video: Example: Chi-Squared Test
  12. Video: Bad Science Revisited: Publication Bias
  13. Video: Effect Size
  14. Video: Meta-analysis
  15. Video: Fraud and Benford's Law
  16. Video: Intuition for Benford's Law
  17. Video: Benford's Law Explained Visually
  18. Video: Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
  19. Video: Multiple Hypothesis Testing: False Discovery Rate
  20. Video: Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
  21. Video: Big Data and Spurious Correlations
  22. Video: Spurious Correlations: Stock Price Example
  23. Video: How is Big Data Different?
  24. Video: Bayesian vs. Frequentist
  25. Video: Motivation for Bayesian Approaches
  26. Video: Bayes' Theorem
  27. Video: Applying Bayes' Theorem
  28. Video: Naive Bayes: Spam Filtering


WEEK 2


Supervised Learning



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.


26 videos, 1 reading expand


  1. Video: Statistics vs. Machine Learning
  2. Video: Simple Examples
  3. Video: Structure of a Machine Learning Problem
  4. Video: Classification with Simple Rules
  5. Video: Learning Rules
  6. Video: Rules: Sequential Covering
  7. Video: Rules Recap
  8. Video: From Rules to Trees
  9. Video: Entropy
  10. Video: Measuring Entropy
  11. Video: Using Information Gain to Build Trees
  12. Video: Building Trees: ID3 Algorithm
  13. Video: Building Trees: C.45 Algorithm
  14. Video: Rules and Trees Recap
  15. Video: Overfitting
  16. Video: Evaluation: Leave One Out Cross Validation
  17. Video: Evaluation: Accuracy and ROC Curves
  18. Video: Bootstrap Revisited
  19. Video: Ensembles, Bagging, Boosting
  20. Video: Boosting Walkthrough
  21. Video: Random Forests
  22. Video: Random Forests: Variable Importance
  23. Video: Summary: Trees and Forests
  24. Video: Nearest Neighbor
  25. Video: Nearest Neighbor: Similarity Functions
  26. Video: Nearest Neighbor: Curse of Dimensionality
  27. Reading: R Assignment: Classification of Ocean Microbes

Graded: R Assignment: Classification of Ocean Microbes

WEEK 3


Optimization



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.


11 videos expand


  1. Video: Optimization by Gradient Descent
  2. Video: Gradient Descent Visually
  3. Video: Gradient Descent in Detail
  4. Video: Gradient Descent: Questions to Consider
  5. Video: Intuition for Logistic Regression
  6. Video: Intuition for Support Vector Machines
  7. Video: Support Vector Machine Example
  8. Video: Intuition for Regularization
  9. Video: Intuition for LASSO and Ridge Regression
  10. Video: Stochastic and Batched Gradient Descent
  11. Video: Parallelizing Gradient Descent


WEEK 4


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


4 videos expand


  1. Video: Introduction to Unsupervised Learning
  2. Video: K-means
  3. Video: DBSCAN
  4. Video: DBSCAN Variable Density and Parallel Algorithms

Graded: Kaggle Competition Peer Review
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