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 forma parte de Programa Especializado - Data Science at Scale
Practical Predictive Analytics: Models and Methods
ofrecido por
Acerca de este Curso
Learner Career Outcomes
25%
20%
Habilidades que obtendrás
Learner Career Outcomes
25%
20%
100 % en línea
Curso 2 de 4 en
Fechas límite flexibles
Aprox. 13 horas para completar
Inglés (English)
ofrecido por
Universidad 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 - Qué aprenderás en este curso
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.
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.
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.
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
Principales revisiones sobre Practical Predictive Analytics: Models and Methods
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 .
Acerca de Programa especializado Data Science at Scale
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