Об этом курсе
4.1
Оценки: 275
Рецензии: 50
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...
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 4 weeks of study, 6-8 hours/week

Прибл. 12 ч. на завершение
Comment Dots

English

Субтитры: English, Korean

Приобретаемые навыки

Random ForestPredictive AnalyticsMachine LearningR Programming
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 4 weeks of study, 6-8 hours/week

Прибл. 12 ч. на завершение
Comment Dots

English

Субтитры: English, Korean

Программа курса: что вы изучите

1

Раздел
Clock
2 ч. на завершение

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....
Reading
28 видео (всего 121 мин.)
Video28 видео
Hypothesis Testing5мин
Significance Tests and P-Values3мин
Example: Difference of Means4мин
Deriving the Sampling Distribution6мин
Shuffle Test for Significance4мин
Comparing Classical and Resampling Methods3мин
Bootstrap6мин
Resampling Caveats6мин
Outliers and Rank Transformation3мин
Example: Chi-Squared Test3мин
Bad Science Revisited: Publication Bias4мин
Effect Size4мин
Meta-analysis5мин
Fraud and Benford's Law4мин
Intuition for Benford's Law2мин
Benford's Law Explained Visually3мин
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3мин
Multiple Hypothesis Testing: False Discovery Rate4мин
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3мин
Big Data and Spurious Correlations4мин
Spurious Correlations: Stock Price Example3мин
How is Big Data Different?3мин
Bayesian vs. Frequentist4мин
Motivation for Bayesian Approaches3мин
Bayes' Theorem2мин
Applying Bayes' Theorem4мин
Naive Bayes: Spam Filtering4мин

2

Раздел
Clock
2 ч. на завершение

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....
Reading
26 видео (всего 111 мин.), 1 материал для самостоятельного изучения, 1 тест
Video26 видео
Simple Examples3мин
Structure of a Machine Learning Problem5мин
Classification with Simple Rules5мин
Learning Rules4мин
Rules: Sequential Covering3мин
Rules Recap2мин
From Rules to Trees2мин
Entropy4мин
Measuring Entropy4мин
Using Information Gain to Build Trees6мин
Building Trees: ID3 Algorithm2мин
Building Trees: C.45 Algorithm4мин
Rules and Trees Recap3мин
Overfitting7мин
Evaluation: Leave One Out Cross Validation5мин
Evaluation: Accuracy and ROC Curves5мин
Bootstrap Revisited4мин
Ensembles, Bagging, Boosting4мин
Boosting Walkthrough5мин
Random Forests3мин
Random Forests: Variable Importance5мин
Summary: Trees and Forests2мин
Nearest Neighbor4мин
Nearest Neighbor: Similarity Functions4мин
Nearest Neighbor: Curse of Dimensionality3мин
Reading1 материал для самостоятельного изучения
R Assignment: Classification of Ocean Microbes10мин
Quiz1 практическое упражнение
R Assignment: Classification of Ocean Microbes28мин

3

Раздел
Clock
1 ч. на завершение

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. ...
Reading
11 видео (всего 41 мин.)
Video11 видео
Gradient Descent Visually4мин
Gradient Descent in Detail2мин
Gradient Descent: Questions to Consider3мин
Intuition for Logistic Regression4мин
Intuition for Support Vector Machines3мин
Support Vector Machine Example3мин
Intuition for Regularization3мин
Intuition for LASSO and Ridge Regression3мин
Stochastic and Batched Gradient Descent5мин
Parallelizing Gradient Descent3мин

4

Раздел
Clock
2 ч. на завершение

Unsupervised Learning

A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem....
Reading
4 видео (всего 21 мин.), 1 тест
Video4 видео
K-means5мин
DBSCAN4мин
DBSCAN Variable Density and Parallel Algorithms4мин
4.1
Direction Signs

33%

начал новую карьеру, пройдя эти курсы
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: SPDec 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.

автор: KPFeb 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 .

Преподаватель

Bill Howe

Director of Research
Scalable Data Analytics

О 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....

О специализации ''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....
Data Science at Scale

Часто задаваемые вопросы

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

Остались вопросы? Посетите Центр поддержки учащихся.