Acerca de este curso: 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
Creada por: University of Washington
Enseñado por: Bill Howe, Director of Research
Scalable Data Analytics
| Información básica | Curso 2 de 4 en Data Science at Scale Specialization |
| Compromiso | 4 weeks of study, 6-8 hours/week |
| Idioma | English, Subtítulos: Korean |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
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 videos
- Vídeo: Hypothesis Testing
- Vídeo: Significance Tests and P-Values
- Vídeo: Example: Difference of Means
- Vídeo: Deriving the Sampling Distribution
- Vídeo: Shuffle Test for Significance
- Vídeo: Comparing Classical and Resampling Methods
- Vídeo: Bootstrap
- Vídeo: Resampling Caveats
- Vídeo: Outliers and Rank Transformation
- Vídeo: Example: Chi-Squared Test
- Vídeo: Bad Science Revisited: Publication Bias
- Vídeo: Effect Size
- Vídeo: Meta-analysis
- Vídeo: Fraud and Benford's Law
- Vídeo: Intuition for Benford's Law
- Vídeo: Benford's Law Explained Visually
- Vídeo: Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
- Vídeo: Multiple Hypothesis Testing: False Discovery Rate
- Vídeo: Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
- Vídeo: Big Data and Spurious Correlations
- Vídeo: Spurious Correlations: Stock Price Example
- Vídeo: How is Big Data Different?
- Vídeo: Bayesian vs. Frequentist
- Vídeo: Motivation for Bayesian Approaches
- Vídeo: Bayes' Theorem
- Vídeo: Applying Bayes' Theorem
- Vídeo: Naive Bayes: Spam Filtering
SEMANA 2
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 videos, 1 lecturas
- Vídeo: Simple Examples
- Vídeo: Structure of a Machine Learning Problem
- Vídeo: Classification with Simple Rules
- Vídeo: Learning Rules
- Vídeo: Rules: Sequential Covering
- Vídeo: Rules Recap
- Vídeo: From Rules to Trees
- Vídeo: Entropy
- Vídeo: Measuring Entropy
- Vídeo: Using Information Gain to Build Trees
- Vídeo: Building Trees: ID3 Algorithm
- Vídeo: Building Trees: C.45 Algorithm
- Vídeo: Rules and Trees Recap
- Vídeo: Overfitting
- Vídeo: Evaluation: Leave One Out Cross Validation
- Vídeo: Evaluation: Accuracy and ROC Curves
- Vídeo: Bootstrap Revisited
- Vídeo: Ensembles, Bagging, Boosting
- Vídeo: Boosting Walkthrough
- Vídeo: Random Forests
- Vídeo: Random Forests: Variable Importance
- Vídeo: Summary: Trees and Forests
- Vídeo: Nearest Neighbor
- Vídeo: Nearest Neighbor: Similarity Functions
- Vídeo: Nearest Neighbor: Curse of Dimensionality
- 阅读: R Assignment: Classification of Ocean Microbes
Calificado: R Assignment: Classification of Ocean Microbes
SEMANA 3
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 videos
- Vídeo: Gradient Descent Visually
- Vídeo: Gradient Descent in Detail
- Vídeo: Gradient Descent: Questions to Consider
- Vídeo: Intuition for Logistic Regression
- Vídeo: Intuition for Support Vector Machines
- Vídeo: Support Vector Machine Example
- Vídeo: Intuition for Regularization
- Vídeo: Intuition for LASSO and Ridge Regression
- Vídeo: Stochastic and Batched Gradient Descent
- Vídeo: Parallelizing Gradient Descent
SEMANA 4
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 videos
- Vídeo: K-means
- Vídeo: DBSCAN
- Vídeo: DBSCAN Variable Density and Parallel Algorithms
Calificado: Kaggle Competition Peer Review
Preguntas Frecuentes
Cómo funciona
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Excellent!!
Very good approach to each method; the assignments are a good test for the topics.
great for learner
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rather nice course. learn R before joining