In the past 2 weeks completed the course the following two courses by Andrew Ng on coursera
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
both 1, 2 coursers (2,3 of the whole specialization pack) are really imp to setup foundational base and grasping the terminology of the terms on deals with and actually plays around with while developing or working in a Machine Learning application , i didt try makin few Notes by hand in pen and paper format + few Key points in markdown editor in my github repo i liked below but , i came across these notes by Tess Ferrandez [Slides embded in the beginning of this post] for the whole courese and they are really good in terms of getting a recall and revision of the topics later . In the courses above only the 1st course had lots of coding but second one was all theory , based on my development experience so far to progress on further and deploying a ML based project to the cloud (will be jumping between multiple clouds free tier and Student Credits to saves money) will be learning Docker next to help in deploying and sharing my projects .
Notes for Course 1 : Github Link 1 , Github Link 2
Notes for Course 2 : Github Link
Link to Slides : Github Link
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