I accept the challenge of Siraj Raval of 100 days of ML. I will learn and code machine learning for at least 1 hour everyday.
Day 1 : Stock Price Predictor
Day 2 : Twitter Sentiment Analysis and Udacity Intro to Machine Learning - Lesson 11 Text Learning
Day 3 : Created labelled CSV dataset of twitter tweets and its sentiments and trained a neural network model to classify images of clothing, like sneakers and shirts.
Day 4 : Learned Bayes Theorem and Naive Bayes Classification Algorithm. Wrote a small program using sklearn to demonstrate Gaussian Naive Bayes.
Day 5 : Built a Naive Bayes Classifier from Scratch using python and numpy package. Reference: https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
Day 6 : Learned Decision Tree Classifier and implemented it using sklearn on iris dataset. Started learning Pandas library.