IDEAS 2018 deck: DIY Applied Machine Learning: Practical and Collaborative Method to Jump Start into Machine Learning with Jupyter Notebooks and Google Collab

click here for the deck that I used for the IDEAS – Int’l Data Engineering and Science Association conference in Dallas.

Also here is the abstract that I wrote when I was preparing the material:

Practical and collaborative method to jump start into machine learning projects using open source with Jupyter Notebooks and Google Collab

Not at all machine learning enthusiasts are alike, and, hence, setting up code environment or training the data model can many times be overwhelming for newcomers in the field of machine learning and deep learning. Data scientists may not have the necessary skills in setting up development environments, and, programmers, may not necessarily have the data scientists skills for preparing data sets and evaluating machine learning models. Furthermore, data scientists and programmers together in some enterprise may lack the collaboration platforms to work together on such projects. Even though most of the books on machine learning using some programming language X provide readers with instructions for setting up the coding environment, such chapters can derail the process of getting started if one gets stuck in the setup. Alternatively, collaborative platforms using Jupyter Ipython Notebooks and Google Collab provide a quick starter for programmers and data scientists alike to develop and collaborate on machine learning algorithms through open source without getting stuck with the nuances of setting up their environments or having to depend on commercial products. The talk revisits the value of Jupyter notebooks for newcomers to the field of AI by showcasing live examples and sharing sources of machine learning algorithms running online using Jupyter notebooks and providing a set of guidelines for implementing such technology in the workplace or leveraging existing ones in the market such as Google Collab. The talk is intended to encourage machine learning enthusiasts to enter the field through a practical method that minimizes the stress from the overwhelming material available on the Internet on how to get started with machine learning.

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