![]() ![]() ![]() Deep learning Specialization on Coursera | Day 18Ĭompleted the Course 1 of the deep learning specialization. Learned Logistic regression as Neural Network. Started Deep learning Specialization on Coursera | Day 17Ĭompleted the whole Week 1 and Week 2 on a single day. Using Scikit-Learn library implemented SVM algorithm along with kernel function which maps our data points into higher dimension to find optimal hyperplane. Implemented SVM using Kernel Trick | Day 16 It gives the whole overview about prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning. First one in the playlist was Black Box Machine Learning. Learned about different types of naive bayes classifiers. Naive Bayes Classifier and Black Box Machine Learning | Day 15 #100 day plan codeWill be using kernel-trick on next implementation.Ĭheck the code here. In Scikit-Learn we have SVC classifier which we use to achieve this task. Today I implemented SVM on linearly related data. I am also implementing the SVM in python using scikit-learn. Support Vector Machines | Day 12Ĭontinuing with #100DaysOfMLCode today I went through the Naive Bayes classifier. Support Vector Machine Infographic is halfway complete. ![]() Implemented the K-NN algorithm for classification. Learned more about how SVM works and implementing the K-NN algorithm. Got an intution on what SVM is and how it is used to solve Classification problem. It gives a detailed description of Logistic Regression. #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 ( ) by Saishruthi Swaminathan. Implementing Logistic Regression | Day 6Ĭheck out the Code here K Nearest Neighbours | Day 7 #100 day plan how toLearned how cost function is calculated and then how to apply gradient descent algorithm to cost function to minimize the error in prediction.ĭue to less time I will now be posting an infographic on alternate days.Īlso 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 :). 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. Get the datasets from here Data PreProcessing | Day 1 100 Days of Machine Learning Coding as proposed by Siraj Raval ![]()
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