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Practical Predictive Analytics: Models and Methods

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HomeData ScienceData Analysis

Practical Predictive Analytics: Models and Methods

University of Washington

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
University of Washington

  • Bill Howe

    Taught by:  Bill Howe, Director of Research

    Scalable Data Analytics
Basic Info
Course 2 of 4 in the Data Science at Scale Specialization
Commitment4 weeks of study, 6-8 hours/week
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.1 stars
Average User Rating 4.1See what learners said
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
  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
  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. Leyendo: 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
  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
  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

FAQs
How It Works
Trabajo del curso
Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros
Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados
Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Creators
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.
Pricing
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Ratings and Reviews
Rated 4.1 out of 5 of 259 ratings
Alon Mann

rather nice course. learn R before joining

Sergio Garofoli

Excellent!!

Roberto Santamaria

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

ML

great for learner



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