100-Days-Of-ML-Code

GitHub describes it as 100 Days of ML Coding. The metadata lists the MIT license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as 100 Days of ML Coding. The metadata lists the MIT license. The README describes the project this way: Moving forward into 100DaysOfMLCode today I dived into the deeper depth of what Logistic Regression actually 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 an infographic on alternate days. Also if someone wants to help me out in documentaion of code and already has some experince in the field and knows Markdown for github please contact me on LinkedIn :) .

The README section "Math Behind Logistic Regression | Day 8" states: 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.

The README section "Implementation of K-NN | Day 11" states: Implemented the K-NN algorithm for classification. 100DaysOfMLCode Support Vector Machine Infographic is halfway complete. Will update it tomorrow.

The README section "Naive Bayes Classifier | Day 13" states: 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.

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README