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100-Days-Of-ML-Code

100 Days of Machine Learning Coding as proposed by Siraj Raval

Get the datasets from here

 Data PreProcessing | Day 1
Check out the code from here.


  


 Simple Linear Regression | Day 2
Check out the code from here.


  


 Multiple Linear Regression | Day 3
Check out the code from here.


  


 Logistic Regression | Day 4


  


 Logistic Regression | Day 5
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 :) .

 Implementing Logistic Regression | Day 6
Check out the Code here

 K Nearest Neighbours | Day 7

  


 Math Behind Logistic Regression | Day 8 

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.

 Support Vector Machines | Day 9
Got an intution on what SVM is and how it is used to solve Classification problem.

 SVM and KNN | Day 10
Learned more about how SVM works and implementing the K-NN algorithm.

 Implementation of K-NN | Day 11  

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

 Support Vector Machines | Day 12

  


 Naive Bayes Classifier | Day 13

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.

 Implementation of SVM | Day 14
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.

 Naive Bayes Classifier and Black Box Machine Learning | Day 15
Learned about different types of naive bayes classifiers. Also started the lectures by Bloomberg. First one in the playlist was Black Box Machine Learning. It gives the whole overview about prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.

 Implemented SVM using Kernel Trick | Day 16
Using Scikit-Learn library implemented SVM algorithm along with kernel function which maps our data points into higher dimension to find optimal hyperplane. 

 Started Deep learning Specialization on Coursera | Day