This repository is aimed to help Coursera learners who have difficulties in their learning process.
The quiz and programming homework is belong to coursera.Please Do Not use them for any other purposes.
Please feel free to contact me if you have any problem,my email is wcshen1994@163.com.

• Bayesian Statistics From Concept to Data Analysis

• Learn to Program: Crafting Quality Code

• Neural Networks for Machine Learning-University of Toronto

• Specialization Advanced Machine Learning Higher School of Economics

  • Introduction to Deep Learning

• Specialization Applied Data Science with Python

  • Introduction to Data Science in Python
  • Applied Machine Learning in Python

• Specialization Big Data-UCSD

  • Introduction to Big Data
  • Big Data Modeling and Management Systems
  • Big Data Interation and Processing

• Specialization Data Mining-UIUC

  • Text Retrieval and Search Engines
  • Text Mining and Analytics
  • Pattern Discovery in Data Mining
  • Cluster Analysis in Data Mining

• Specialization Data Science-Johns Hopkins University

  • The Data Scientist’s Toolbox
  • R Programming
  • Getting and Cleaning Data

• Specialization Data Structures & Algorithms-UC San Diego

  • Algorithmic Toolbox
  • Data Structures
  • Algorithms on Graphs
  • Algorithms on Strings

• Specialization Deep Learning

  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models

• Specialization Functional Programming in Scala

  • Functional Programming Principles in Scala

• Specialization Fundamentals of Computing-Rice University

  • Principles of Computing 1

• Specialization Meachine Learning-University of Washington

  • Machine Learning Foundations: A Case Study Approach
  • Machine Learning: Regression
  • Machine Learning: Classification
  • Machine Learning: Clustering & Retrieval

• Specialization Probabilistic Graphical Models-Stanford University

  • Probabilistic Graphical Models 1: Representation
  • Probabilistic Graphical Models 2: Inference
  • Probabilistic Graphical Models 3: Learning

• Specialization 程序设计与算法-Peking University

  • 计算导论与C语言基础
  • C程序设计进阶
  • C++程序设计
  • 算法基础
  • 数据结构基础

• Specialization Recommender System-University of Minnesota

  • Introduction to Recommender Systems: Non-Personalized and Content-Based
  • Nearest Neighbor Collaborative Filtering
  • Recommender Systems:Evaluation and Metrics
  • Matrix Factorization and Advanced Techniques

• Specialization Statistics with R-Duke University

  • Introduction to Probability and Data
  • Inferential Statistics

• The Unix Workbench-Johns Hopkins University