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.
Este curso faz parte do Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga Escala
oferecido por
Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga EscalaUniversidade de WashingtonInformações sobre o curso
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Resultados de carreira do aprendiz
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20%
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 7 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Coreano, Russo, Inglês, Espanhol
Habilidades que você terá
Random ForestPredictive AnalyticsMachine LearningR Programming
Resultados de carreira do aprendiz
25%
comecei uma nova carreira após concluir estes cursos
20%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 7 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Coreano, Russo, Inglês, Espanhol
oferecido por
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.
Programa - O que você aprenderá com este curso
2 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.
2 horas para concluir
28 vídeos (Total 121 mín.)
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
3 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.
3 horas para concluir
26 vídeos (Total 111 mín.), 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 Microbes30min
1 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.
1 hora para concluir
11 vídeos (Total 41 mín.)
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
1 hora 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.
1 hora para concluir
4 vídeos (Total 21 mín.)
4 videos
K-means5min
DBSCAN4min
DBSCAN Variable Density and Parallel Algorithms4min
Avaliações
Principais avaliações do ANÁLISE PREDITIVA PRÁTICA: MODELOS E MÉTODOS
por NE7 de Jun de 2017
I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
por SP22 de Dez de 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.
por WK5 de Jun de 2017
Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.
por KP7 de Fev de 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 .
Sobre 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.
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