ScienceGuy

Correctly installing GraphViz for python is a painstaking task. The reason is installation and PATH related issues while using pip. The problem intensifies if you are working on your work-related device that has restrictions with respect to installation and file/folder access. Thus, despite several resources on the internet, many of them do not stand applicable for your case. I had to spend a significant amount of time searching for a solution that works. Most of the solutions I found over the internet were either not applicable to my case (owing to restricted access on my work device) or they were written in a complex way to shroud the approach to be adopted or they did not work. In a nutshell, I did not find any lucid explanation, at least at could directly be followed until resolution by someone who is not a computer scientist or very code-savvy. Hence, to save time and the frustration coupled with it, I am sharing a simple method that resolved the issue in my case.  For your refere

This is one of the most interesting courses I have had on Coursera. The reason, apart from the quality of content and exhaustive content, is actually the interest and excitement with which the prof teaches it. His lectures by far are the most fun to listen to. Given its an introductory course on MATLAB, with relatively long duration videos, the content could have been easy to doze on. However, the prof keeps the students (us) alive and riveting with frequently embedded "witty fun elements" in the lectures - cracking jokes and interesting dialogues (I did find them hilarious :D ) . Having said that, I am very thankful to Vanderbilt university for the amazing content as well, as it starts from very basics and builds on eventually! I.  Introduction     - Introduction II. MATLAB environment     - Running MATLAB      - Syntax and semantics III. Matrices and Operators     - Introduction     - Colon Operator     - Accessing parts of the matrix     - Combining and Transforming Matric

This coursera course provides a way to utilize H2O for applying Machine Learning Purposes. Note, this course has more focus on application and that on H2O. However, having said that, anyone doesn't require an in-depth pre-req of H2O. I think it is good to have a glance and to understand the basics of ML in H2O but if one is looking in theory, then this ain't the course. I. Week-1     -  Fundamentals          - Pre-req and basics II. Week-2     - Trees and Overfitting          - Tree algorithms          - Importing, generating and Over-fitting III. Week-3     - Linear Models and More          - Loading and Saving          - Linear Models and Naive Bayes          - Data Manipulation         - Grids IV. Week-4     - Deep Learning          - Early stopping          - Binning and Merging          - Deep learning V. Week-5     - Unsupervised learning          - Autoencoders