Material for Week 9 of ML boot camp.
Prerequisites - you should be familiar and should at least be able to understand below, if not please refer to previous vidoes and course material.
- Data wrangling with numpy and pandas, Data viz with matplotlib and seaborn.
- Able to handle missing values, categorical data, create pipelines.
- Understand Linear regression, Logistic regression, KNN, Naive Bayes. Deploy these algorithms and measure their performance.
YOUR CHECKLIST FOR SESSION ON 17th OCT @ 13:30 PM IST
- Day1: Go through the Video on Support Vector Machines. Understand the concepts explained in the slide deck for Support Vector Machine.
- Day2: Replicate this Notebook, If you are note able to - see the video on SVM hands-on.
- Day3: Take a dataset with missing values and categrical data in it. Ensure it is a classification problem. Apply all the skills you have learnt so far on that dataset. Try clearning the dataset, encoding the labels, use pipelines, then fit these models - Logistic Regression, KNN, Naive Bayes and SVM.
- Day4: Practice on a few more datasets end to end. You can pick these datasets from previous practice notebooks I have shared.
- Day5: Read about
C- hyperparameters for SVM algorithm.