100 Days of Machine Learning Coding as proposed by Siraj Raval

Get the datasets from here

Check out the code from here.

Check out the code from here.

Check out the code from here.

Moving forward into #100DaysOfMLCode today I dived into the deeper depth of what actually Logistic Regression is and what is the math involved behind it. Learned how cost function is calculated and then how to apply gradient descent algorithm to cost function to minimize the error in prediction.
Due to less time I will now be posting a infographic on alternate days. Also if someone wants to help me out in documentaion of code and has already some experince in the field and knows Markdown for github please contact me on LinkedIn :) .

Check out the Code here

#100DaysOfMLCode To clear my insights on logistic regression I was searching on the internet for some resource or article and I came across this article (https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc) by Saishruthi Swaminathan.

It gives a detailed description of Logistic Regression. Do check it out.

Got an intution on what SVM is and how it is used to solve Classification problem.

Learned more about how SVM works and implementing the knn algorithm.

Implemented the K-NN algorithm for classification. #100DaysOfMLCode Support Vector Machine Infographic is halfway complete will update it tomorrow.

Continuing with #100DaysOfMLCode today I went through the Naive Bayes classifier. I am also implementing the SVM in python using scikit-learn. Will update the code soon.

Today I implemented SVM on linearly related data. Used Scikit-Learn library. In scikit-learn we have SVC classifier which we use to achieve this task. Will be using kernel-trick on next implementation. Check the code here.

Learned about diffrent types of naive bayes classifer also started the lectures by Bloomberg. first one in the playlist was Black Box Machine Learning. It gave the whole over view about prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.

Using Scikit-Learn library implemented SVM algorithm along with kernel function which maps our data points into higher dimension to find optimal hyperplane.

Completed the whole Week 1 and Week 2 on a single day. Learned Logistic regression as Neural Network.

Completed the Course 1 of the deep learning specialization. Implemented a neural net in python.

Started Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. It was basically an intoduction to the upcoming lectures. He also explained Perceptron Algorithm.

Completed the Week 1 of Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.

Watched some tutorials on how to do web scaping using Beautiful Soup in order to collect data for building a model.

Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. Learned about Hoeffding Inequality.

Lec 3 of Bloomberg ML course introduced some of the core concepts like input space, action space, outcome space, prediction functions, loss functions, and hypothesis spaces.

Check the code here.