A day to day plan for this challenge. Covers both theoritical and practical aspects.
Please see [Deep Work](https://www.quora.com/What-is-the-one-skill-that-if-you-have-it-will-completely-change-your-life/answer/Shashank-Shekhar-221) which compliments our challenge and increases productivity
You can follow me on @Medium for interesting blog articles.
- Learn about Pandas. See Videos(1-5)
- Learn in general about ML See Video (Blackbox Machine Learning)
- Read/Practice Day-1 and Day-2
- See Intro to Linear Regression
- Read LR Docs
- Learn about Pandas. See Videos(6-10)
- Learn in general about ML See Video (Case Study: Churn Prediction)
- Read/Practice Day-3
- See Data Spread
- Andrew Ng See Videos (1-3)
- Learn about Pandas. See Videos(11-15)
- Learn in general about ML See Video (Statistical Learning Theory)
- Read/Practice Day-4 and Day-8
- Visualization in Python See Official Docs
- Learn about Pandas. See Videos(16-18)
- Read KNN-1
- Read KNN-2
- Learn about Pandas. See Videos(19-22)
- Read/Practice Day-7
- General read on Medium
- Learn about Pandas. See Videos(23-26)
- Implementing KNN
- Read/Practice Day-12
- KNN-Sklearn See Official Docs
- Learn about Numpy. Read this
- Naive Bayes - 1
- Naive Bayes - 2
- Naive Bayes - 3
- Naive Bayes - 4
- Lime
- Building Trust in ML models
- Interpretable ML models
- Implementing Naive Bayes
- Learn in general about ML See Video (Stochastic Gradient Descent) - 10 mins onwards
- Lime hands-on news dataset
- Light read about Averaging Ensemble Techniques for more accurate predictions.
- Light reading on Ensemble Techniques
- Implementing Support Vector Machines
- See Ensemble learners
- Implement Average Voting Ensemble Meta Model
- Read about Stacking Ensemble Technique
- Read Stacking from scratch
- Read Stacking-concept-pictures-code