木子-周

湖北 武汉

感兴趣的主题: IT数码 体育

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木子-周 关注的课程

Machine Learning (Coursera) 28 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/ml

简介: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Natural Language Processing (Coursera) 7 个评论 关注

开始时间: 02/24/2013 持续时间: 10 weeks

主页: https://www.coursera.org/course/nlangp

简介: Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.

Natural Language Processing (Coursera) 3 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/nlp

简介: In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems.

An Introduction to Interactive Programming in Python (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/interactivepython

简介: This course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

Machine Learning (Coursera) 7 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/machlearning

简介: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

StatLearning: Statistical Learning (Stanford Online) 3 个评论 关注

开始时间: 01/20/2014 持续时间: 未知

主页: https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about

简介: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

CVX101: Convex Optimization (Stanford Online) 1 个评论 关注

开始时间: 01/20/2014 持续时间: 未知

主页: https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about

简介: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

Introduction to Data Science (Coursera) 5 个评论 关注

开始时间: 06/30/2014 持续时间: 8 weeks

主页: https://www.coursera.org/course/datasci

简介: Join the data revolution. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula. Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data.

Introduction to Statistics: Probability (edX) 1 个评论 关注

开始时间: 04/12/2013 持续时间: 5 weeks

主页: https://www.edx.org/course/uc-berkeley/stat2-2x/introduction-statistics/685

简介: An introduction to probability, with the aim of developing probabilistic intuition as well as techniques needed to analyze simple random samples.

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