University of Washington - Practical Predictive Analytics: Models and Methods
- Offered byCoursera
Practical Predictive Analytics: Models and Methods at Coursera Overview
Duration | 7 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Practical Predictive Analytics: Models and Methods at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Data Science at Scale Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 7 hours to complete
- English Subtitles: French, Portuguese (European), Korean, Russian, English, Spanish
Practical Predictive Analytics: Models and Methods at Coursera Course details
- 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
Practical Predictive Analytics: Models and Methods at Coursera Curriculum
Practical Statistical Inference
Appetite Whetting: Bad Science
Hypothesis Testing
Significance Tests and P-Values
Example: Difference of Means
Deriving the Sampling Distribution
Shuffle Test for Significance
Comparing Classical and Resampling Methods
Bootstrap
Resampling Caveats
Outliers and Rank Transformation
Example: Chi-Squared Test
Bad Science Revisited: Publication Bias
Effect Size
Meta-analysis
Fraud and Benford's Law
Intuition for Benford's Law
Benford's Law Explained Visually
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
Multiple Hypothesis Testing: False Discovery Rate
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
Big Data and Spurious Correlations
Spurious Correlations: Stock Price Example
How is Big Data Different?
Bayesian vs. Frequentist
Motivation for Bayesian Approaches
Bayes' Theorem
Applying Bayes' Theorem
Naive Bayes: Spam Filtering
Supervised Learning
Statistics vs. Machine Learning
Simple Examples
Structure of a Machine Learning Problem
Classification with Simple Rules
Learning Rules
Rules: Sequential Covering
Rules Recap
From Rules to Trees
Entropy
Measuring Entropy
Using Information Gain to Build Trees
Building Trees: ID3 Algorithm
Building Trees: C.45 Algorithm
Rules and Trees Recap
Overfitting
Evaluation: Leave One Out Cross Validation
Evaluation: Accuracy and ROC Curves
Bootstrap Revisited
Ensembles, Bagging, Boosting
Boosting Walkthrough
Random Forests
Random Forests: Variable Importance
Summary: Trees and Forests
Nearest Neighbor
Nearest Neighbor: Similarity Functions
Nearest Neighbor: Curse of Dimensionality
R Assignment: Classification of Ocean Microbes
R Assignment: Classification of Ocean Microbes
Optimization
Optimization by Gradient Descent
Gradient Descent Visually
Gradient Descent in Detail
Gradient Descent: Questions to Consider
Intuition for Logistic Regression
Intuition for Support Vector Machines
Support Vector Machine Example
Intuition for Regularization
Intuition for LASSO and Ridge Regression
Stochastic and Batched Gradient Descent
Parallelizing Gradient Descent
Unsupervised Learning
Introduction to Unsupervised Learning
K-means
DBSCAN
DBSCAN Variable Density and Parallel Algorithms
Practical Predictive Analytics: Models and Methods at Coursera Admission Process
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