Machine Learning and Probabilistic Graphical Models: Course Materials. Online Learning Curriculum for Data Scientists. Online Learning Curriculum for Data Scientists blog, data science Online Learning Curriculum for Data Scientists “Is there any online reading or courses I can do to get into data analysis?”
At my workplace, I get asked the question above. The question is usually posed by people typically with a finance background, who’s working as a management consultant. A data scientist can be defined by Drew Conway‘s Data Science Venn diagram which suggests that data scientists must have a solid mathematical background, skills in coding and computer hacking, and a healthy mix of subject matter expertise. The courses mentioned below are by no means a “over a weekend” type of engagement – if you are serious about entering the world of data science as a profession, allow yourself at least 3-6 months to complete and study the content of the courses below.
Learn to program.R and Python are the two primary scripting languages that are taking over the world of data science. 2 Comments Nice article! Leave a comment. 2nd Lisbon Machine Learning School (2012) 1st Lisbon Machine Learning School. Past Courses in Big Data Analytics and Data Science: Content Online. Practical machine learning: methods and algorithmics. Learning From Data - Online Course.
A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.
ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. CosmoLearning Computer Science. PURDUE Machine Learning Summer School 2011. Learning From Data - The Lectures. Taught by Feynman Prize winner Professor Yaser Abu-Mostafa.
The fundamental concepts and techniques are explained in detail. The focus of the lectures is real understanding, not just "knowing. " Lectures use incremental viewgraphs (2853 in total) to simulate the pace of blackboard teaching. The 18 lectures (below) are available on different platforms: Here is the playlist on YouTube Lectures are available on iTunes U course app The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning.
Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. Training versus Testing - The difference between training and testing in mathematical terms. Theory of Generalization - How an infinite model can learn from a finite sample.
The VC Dimension - A measure of what it takes a model to learn. Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities.