Natural Language Processing (CourseraArchive) 7 个评论 关注

开始时间: 04/22/2022 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.

Machine Learning (CourseraArchive) 7 个评论 关注

开始时间: 04/22/2022 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!

Algorithms Specialization (Coursera专项课程) 0 个评论 关注

开始时间: 12/21/2023 持续时间: Approximately 4 months to complete Suggested pace of 4 hours/week

主页: https://www.coursera.org/specializations/algorithms

简介: 斯坦福大学的算法专项课程系列（Algorithms Specialization），学会像计算机科学家一样思考，掌握算法设计和分析的基础知识。这个系列包含4门子课程，涵盖基础的算法主题和高级算法主题，此前评价非常高，五颗星推荐，感兴趣的同学可以关注: Learn To Think Like A Computer Scientist-Master the fundamentals of the design and analysis of algorithms.

Algorithms: Design and Analysis, Part 1 (CourseraArchive) 5 个评论 关注

开始时间: 04/22/2022 持续时间: 6 weeks

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

简介: In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

Design of Computer Programs (Udacity) 4 个评论 关注

开始时间: 04/22/2022 持续时间: 自主

主页: https://www.udacity.com/course/cs212

简介: Learn new concepts, patterns, and methods that will expand your programming abilities, helping move you from a novice to an expert programmer.

機器學習基石 (Machine Learning Foundations) (CourseraArchive) 10 个评论 关注

开始时间: 04/22/2022 持续时间: 8 weeks

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

简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]