DURGESH716/DataScienceCoursera

Data Science - Coursera

Data Science Specialization provided by Johns Hopkins University

Includes 9 courses and the capstone project

• The Data Scientist's Toolbox (4 weeks)
• R Programming (4 weeks)
• Getting and Cleaning Data (4 weeks)
• Exploratory Data Analysis (4 weeks)
• Reproducible Research (4 weeks)
• Statistical Inference (4 weeks) pending
• Regression Models (4 weeks) pending
• Practical Machine Learning (4 weeks) pending
• Developing Data Products (4 weeks) pending
• Data Science Capstone (8 weeks) pending

Machine Learning - Coursera

Specialization provided by Stanford University

• Linear Regression with one variable
• Linear Algebra review
• Linear Regression with multiple variables
• [Octave] no information
• Logistic Regression
• Regularization
• Neural Networks Representation
• Neural Networks Learning
• Advice for Applying Machine Learning
• Machine Learning System Design
• Support Vector Machines
• Clustering
• Dimensionality Reduction (PCA)
• Anomaly Detection
• Recommender System
• Large Scale Machine Learning
• Application Example OCR

Master Statistics with R - Coursera

Specialization provided by Duke University

Includes 4 courses and the capstone project

• Introduction to Probability and Data
• Inferential Statistics
• Linear Regression and Modeling
• Bayesian Statistics
• Statistics Capstone Project

Machine Learning - Coursera

Specialization provided by Washington University

Includes 4 courses

• Machine Learning Foundations
• Machine Learning Regression
• Machine Learning Classification
• Machine Learning Clustering & Retrieval

Deep Learning - Coursera

Specialization provided by DeepLearning.ai

Includes 5 courses

• Neural Networks and Deep Learning
• Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects no programming assignments
• Convolutional Neural Networks
• Sequence Models

Reinforcement Learning - Coursera

Specialization provided by University of Alberta

Includes 4 Courses

• Fundamentals of Reinforcement Learning
• Sample-based Learning Methods
• Prediction and Control with Function Approximation
• A Complete Reinforcement Learning System (Capstone) pending