Coursera
What is Data Science? - IBM
Tools for Data Science - IBM
Data Science Methodology - IBM
Python for Data Science and AI - IBM
Databases and SQL for Data Science - IBM
Data Analysis with Python - IBM
Data Visualization with Python - IBM
Machine Learning with Python - IBM
Exploratory Data Analysis for Machine Learning - IBM
Supervised Learning: Regression - IBM
Supervised Learning: Classification - IBM
Unsupervised Learning - IBM
The Data Scientist’s Toolbox - Johns Hopkins University
R Programming - Johns Hopkins University
The R Programming Environment - Johns Hopkins University
Getting and Cleaning Data - Johns Hopkins University
Exploratory Data Analysis - Johns Hopkins University
Reproducible Research - Johns Hopkins University
Statistical Inference - Johns Hopkins University
Regression Models
Practical Machine Learning - Johns Hopkins University
Developing Data Products - Johns Hopkins University
Advanced R Programming - Johns Hopkins University
Building R Packages - Johns Hopkins University
Building Data Visualization Tools - Johns Hopkins University
Introduction to Probability and Data with R - Duke University
Inferential Statistics - Duke University
Linear Regression and Modeling - Duke University
Bayesian Statistics - Duke University
Probabilistic Graphical Models 1: Representation - Stanford
Probabilistic Graphical Models 2: Inference - Stanford
Probabilistic Graphical Models 3: Learning - Stanford
The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats - SAS
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership - SAS
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls - SAS
Natural Language Processing with Classification and Vector Spaces - deeplearning.ai
Natural Language Processing with Probabilistic Models - deeplearning.ai
Natural Language Processing with Sequence Models - deeplearning.ai
Natural Language Processing with Attention Models - deeplearning.ai
RPA Lifecycle: Introduction, Discovery and Design - Automation Anywhere
RPA Lifecycle: Development and Testing - Automation Anywhere
RPA Lifecycle: Deployment and Maintenance - Automation Anywhere
Cognitive Solutions and RPA Analytics - Automation Anywhere
Making the Case for Robotic Process Automation - AICPA
Modern Robotics, Course 1: Foundations of Robot Motion - Northwestern
Modern Robotics, Course 2: Robot Kinematics - Northwestern
Modern Robotics, Course 3: Robot Dynamics - Northwestern
Modern Robotics, Course 4: Robot Motion Planning and Control - Northwestern
Modern Robotics, Course 5: Robot Manipulation and Wheeled Mobile Robots - Northwestern
Control of Mobile Robots - Georgia Tech
Robotics: Aerial Robotics - University of Pennsylvania
Robotics: Computational Motion Planning - University of Pennsylvania
Robotics: Mobility - University of Pennsylvania
Robotics: Perception - University of Pennsylvania
Robotics: Estimation and Learning - University of Pennsylvania
EdX
Data Science: R Basics - Harvard
Data Science: Visualization - Harvard
Data Science: Probability - Harvard
Data Science: Inference and Modeling - Harvard
Data Science: Productivity Tools - Harvard
Data Science: Wrangling - Harvard
Data Science: Linear Regression - Harvard
Data Science: Machine Learning - Harvard
Data Science: Capstone - Harvard
Probability - The Science of Uncertainty and Data - MIT
Data Analysis in Social Science—Assessing Your Knowledge - MIT
Fundamentals of Statistics - MIT
Machine Learning with Python: from Linear Models to Deep Learning - MIT
Capstone Exam in Statistics and Data Science - MIT
Introduction to Data Science - Rice University
Data Science Tools - Rice University
The Data Science Method - Rice University
SQL for Data Science - Rice University
Python Basics for Data Science - Rice University
Analyzing Data with Python - Rice University
Visualizing Data with Python - Rice University
Machine Learning with Python: A Practical Introduction - Rice University
Data Science and Machine Learning Capstone Project - Rice University
Python for Data Science - UC San Diego
Probability and Statistics in Data Science using Python - UC San Diego
Machine Learning Fundamentals - UC San Diego
Big Data Analytics Using Spark - UC San Diego
Data Science: Computational Thinking with Python - UC Berkeley
Data Science: Inferential Thinking through Simulations - UC Berkeley
Data Science: Machine Learning and Predictions - UC Berkeley
Fundamentals of TinyML - Harvard University
Applications of TinyML - Harvard University
Deploying TinyML - Harvard University
Statistical Thinking for Data Science and Analytics - Columbia University
Machine Learning for Data Science and Analytics - Columbia University
Enabling Technologies for Data Science and Analytics: The Internet of Things - Columbia University
Introduction to Predictive Analytics using Python - University of Edinburgh
Successfully Evaluating Predictive Modelling - University of Edinburgh
Statistical Predictive Modelling and Applications - University of Edinburgh
Predictive Analytics using Machine Learning - University of Edinburgh
Predictive Analytics Final Project - University of Edinburgh
Artificial Intelligence (AI) - Columbia University
Machine Learning - Columbia University
Robotics - Columbia University
Animation and CGI Motion - Columbia University
AI for Everyone: Master the Basics - IBM
Introduction to Watson AI - IBM
AI Chatbots without Programming - IBM
Python Basics for Data Science - IBM
AI Applications with Watson - IBM
Computer Vision Fundamentals with Watson and OpenCV - IBM
CS50's Introduction to Artificial Intelligence with Python - Harvard
AI in Practice: Preparing for AI - TU Delft
AI in Practice: Applying AI - TU Delft
Bias and Discrimination in AI - Universite de Montréal
Ethics in AI and Big Data - The Linux Foundation
Data Ethics, AI and Responsible Innovation - The University of Edinburgh
Deep Learning Fundamentals with Keras - IBM
PyTorch Basics for Machine Learning - IBM
Deep Learning with Python and PyTorch - IBM
Deep Learning with Tensorflow - IBM
Using GPUs to Scale and Speed-up Deep Learning - IBM
Applied Deep Learning Capstone Project - IBM
Introduction to Text Analytics with Python - University of Canterbury
Visualizing Text Analytics with Python - University of Canterbury
Classical Machine Learning for Financial Engineering - NYU
Deep Learning and Neural Networks for Financial Engineering - NYU
Introduction to Data Science - IBM
FutureLearn
Data Science in the Games Industry - University of Dundee
Data Mining with Weka - University of Waikato
Advanced Data Mining with Weka - University of Waikato
AI and Big Data in Global Health Improvement - Taipei Medical University
Artificial Intelligence for Healthcare: Opportunities and Challenges - Taipei Medical University
Programming for Everybody (Getting Started with Python) - University of Michigan