Having a portfolio of public evidence of your data science skills can be decisive at some point. It goes without saying that, even if you have a referral, the ability to show potential employers what you can do instead of just telling them you can do something is important. Following is a list of projects I completed (the links are redirecting to my GitHub projects portfolio repository). 

Note: Should you experience loading issues with the GitHub links, I provided an alternate link to each notebook using nbviewer.

Machine learning:

• Bias-variance tradeoff [GitHub], [nbviewer]
• K-nearest neighbors [GitHub], [nbviewer]

• Linear regression [GitHub], [nbviewer]
• Logistic regression [GitHub], [nbviewer]
• Decision trees [GitHub], [nbviewer]
• Ensembling [GitHub], [nbviewer]
• Clustering [GitHub], [nbviewer]
• Regularization [GitHub], [nbviewer]

Text-mining & natural language processing:

• Naive Bayes text classifier [GitHub], [nbviewer]
• Basic natural language processing [GitHub], [nbviewer]
  
• TF-IDF and sentiment analysis [GitHub], [nbviewer]
• Basis regular expression (RegEx) [GitHub], [nbviewer]
• Intermediate RegEx [GitHub], [nbviewer]
• Working a text-based DS problem[GitHub], [nbviewer]
• Advanced ML techniques [GitHub], [nbviewer]

Deep learning:

• Multi-layer perceptron in Keras [in progress]

New projects will be added to the above list on regular basis. I’ll focus primarily on Machine Learning using unstructured text data and Deep Learning projects since these are the subjects I’m most interested in.