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
Este curso faz parte do Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga Escala
Análise preditiva prática: modelos e métodos
oferecido por
Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga EscalaUniversidade de Washington
Informações sobre o curso
4.1
287 classificações
Habilidades que você terá
Random ForestPredictive AnalyticsMachine LearningR Programming
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
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 vídeos (total de (Total 121 mín.) min)
28 videos
Hypothesis Testing5min
Significance Tests and P-Values3min
Example: Difference of Means4min
Deriving the Sampling Distribution6min
Shuffle Test for Significance4min
Comparing Classical and Resampling Methods3min
Bootstrap6min
Resampling Caveats6min
Outliers and Rank Transformation3min
Example: Chi-Squared Test3min
Bad Science Revisited: Publication Bias4min
Effect Size4min
Meta-analysis5min
Fraud and Benford's Law4min
Intuition for Benford's Law2min
Benford's Law Explained Visually3min
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3min
Multiple Hypothesis Testing: False Discovery Rate4min
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3min
Big Data and Spurious Correlations4min
Spurious Correlations: Stock Price Example3min
How is Big Data Different?3min
Bayesian vs. Frequentist4min
Motivation for Bayesian Approaches3min
Bayes' Theorem2min
Applying Bayes' Theorem4min
Naive Bayes: Spam Filtering4min
Semana
22 horas para concluir
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 vídeos (total de (Total 111 mín.) min), 1 leitura, 1 teste
26 videos
Simple Examples3min
Structure of a Machine Learning Problem5min
Classification with Simple Rules5min
Learning Rules4min
Rules: Sequential Covering3min
Rules Recap2min
From Rules to Trees2min
Entropy4min
Measuring Entropy4min
Using Information Gain to Build Trees6min
Building Trees: ID3 Algorithm2min
Building Trees: C.45 Algorithm4min
Rules and Trees Recap3min
Overfitting7min
Evaluation: Leave One Out Cross Validation5min
Evaluation: Accuracy and ROC Curves5min
Bootstrap Revisited4min
Ensembles, Bagging, Boosting4min
Boosting Walkthrough5min
Random Forests3min
Random Forests: Variable Importance5min
Summary: Trees and Forests2min
Nearest Neighbor4min
Nearest Neighbor: Similarity Functions4min
Nearest Neighbor: Curse of Dimensionality3min
1 leituras
R Assignment: Classification of Ocean Microbes10min
1 exercício prático
R Assignment: Classification of Ocean Microbes28min
Semana
31 hora para concluir
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 vídeos (total de (Total 41 mín.) min)
11 videos
Gradient Descent Visually4min
Gradient Descent in Detail2min
Gradient Descent: Questions to Consider3min
Intuition for Logistic Regression4min
Intuition for Support Vector Machines3min
Support Vector Machine Example3min
Intuition for Regularization3min
Intuition for LASSO and Ridge Regression3min
Stochastic and Batched Gradient Descent5min
Parallelizing Gradient Descent3min
Semana
42 horas para concluir
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 vídeos (total de (Total 21 mín.) min), 1 teste
4 videos
K-means5min
DBSCAN4min
DBSCAN Variable Density and Parallel Algorithms4min
25%
comecei uma nova carreira após concluir estes cursos
20%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
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.
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 .
Instrutores
Bill Howe
Director of ResearchScalable Data Analytics
Sobre Universidade de 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.
Sobre o Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga Escala
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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