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
Ce cours fait partie de la Spécialisation Data Science at Scale
Practical Predictive Analytics: Models and Methods
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À propos de ce cours
4.1
280 notes
Compétences que vous acquerrez
Random ForestPredictive AnalyticsMachine LearningR Programming
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Practical 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 vidéos (Total 121 min)
28 vidéos
Hypothesis Testing5 min
Significance Tests and P-Values3 min
Example: Difference of Means4 min
Deriving the Sampling Distribution6 min
Shuffle Test for Significance4 min
Comparing Classical and Resampling Methods3 min
Bootstrap6 min
Resampling Caveats6 min
Outliers and Rank Transformation3 min
Example: Chi-Squared Test3 min
Bad Science Revisited: Publication Bias4 min
Effect Size4 min
Meta-analysis5 min
Fraud and Benford's Law4 min
Intuition for Benford's Law2 min
Benford's Law Explained Visually3 min
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3 min
Multiple Hypothesis Testing: False Discovery Rate4 min
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3 min
Big Data and Spurious Correlations4 min
Spurious Correlations: Stock Price Example3 min
How is Big Data Different?3 min
Bayesian vs. Frequentist4 min
Motivation for Bayesian Approaches3 min
Bayes' Theorem2 min
Applying Bayes' Theorem4 min
Naive Bayes: Spam Filtering4 min
Semaine
2Supervised 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 vidéos (Total 111 min), 1 lecture, 1 quiz
26 vidéos
Simple Examples3 min
Structure of a Machine Learning Problem5 min
Classification with Simple Rules5 min
Learning Rules4 min
Rules: Sequential Covering3 min
Rules Recap2 min
From Rules to Trees2 min
Entropy4 min
Measuring Entropy4 min
Using Information Gain to Build Trees6 min
Building Trees: ID3 Algorithm2 min
Building Trees: C.45 Algorithm4 min
Rules and Trees Recap3 min
Overfitting7 min
Evaluation: Leave One Out Cross Validation5 min
Evaluation: Accuracy and ROC Curves5 min
Bootstrap Revisited4 min
Ensembles, Bagging, Boosting4 min
Boosting Walkthrough5 min
Random Forests3 min
Random Forests: Variable Importance5 min
Summary: Trees and Forests2 min
Nearest Neighbor4 min
Nearest Neighbor: Similarity Functions4 min
Nearest Neighbor: Curse of Dimensionality3 min
1 lecture
R Assignment: Classification of Ocean Microbes10 min
1 exercice pour s'entraîner
R Assignment: Classification of Ocean Microbes28 min
Semaine
3Optimization
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 vidéos (Total 41 min)
11 vidéos
Gradient Descent Visually4 min
Gradient Descent in Detail2 min
Gradient Descent: Questions to Consider3 min
Intuition for Logistic Regression4 min
Intuition for Support Vector Machines3 min
Support Vector Machine Example3 min
Intuition for Regularization3 min
Intuition for LASSO and Ridge Regression3 min
Stochastic and Batched Gradient Descent5 min
Parallelizing Gradient Descent3 min
Semaine
4Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 vidéos (Total 21 min), 1 quiz
4 vidéos
K-means5 min
DBSCAN4 min
DBSCAN Variable Density and Parallel Algorithms4 min
4.1
50 avis33%
a commencé une nouvelle carrière après avoir terminé ces cours
25%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par SP•Dec 23rd 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.
par KP•Feb 8th 2016
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
Enseignant
Bill Howe
Director of ResearchScalable Data Analytics
À propos de 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.
À propos de la Spécialisation Data Science at Scale
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.
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