100-Days-of-ML

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.

Day-1 (31st July, 2018)

• 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

Day-2 (1st August, 2018)

• 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)

Day-3 (2nd August, 2018)

• 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

Day-4 (3rd August, 2018)

• Learn about Pandas. See Videos(16-18)
• Read KNN-1
• Read KNN-2

Day-5 (4th August, 2018)

• Learn about Pandas. See Videos(19-22)
• Read/Practice Day-7
• General read on Medium

Day-6 (5th August, 2018)

• Learn about Pandas. See Videos(23-26)
• Implementing KNN
• Read/Practice Day-12
• KNN-Sklearn See Official Docs

Day-7 (6th August, 2018)

• Learn about Numpy. Read this
• Naive Bayes - 1
• Naive Bayes - 2
• Naive Bayes - 3
• Naive Bayes - 4

Day-8 (7th August, 2018)

• 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

Day-9 (8th August, 2018)

• 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

Day-10 (9th August, 2018)

• Implement Average Voting Ensemble Meta Model
• Read about Stacking Ensemble Technique
• Read Stacking from scratch
• Read Stacking-concept-pictures-code

Day-11 (10th August, 2018)

• Read/Practice Day-25
• Read about Feature Scaling
• Read Why, How and When to Scale
• Implementation of Feature scaling techniques
• See Decision Trees - MMDS
• Glance through Decision Trees - Coursera

Day-12 (11th August, 2018)

• Implementing of Decision Trees
• See lectures from Coursera - 2nd week and Coursera - 4th week

Day-13 (12th August, 2018)

• Khan Academy Vector's Section
• Light read on Stacking Classifier
• Implementing - Handeling missing values using pandas
• General read on EM for data imputation

Day-14 (13th August, 2018)

• Read about Model Evaluation
• See Khan Academy Linear combinatations & span and Linear Dependence/Independence
• Explore a Helper Lib

Day-15 (14th August, 2018)

• See Khan Academy Subspaces
• Practice Mlxtend
• Read/Practice Day-33 & Day-34

Day-16 (15th August, 2018)

• Light read on Vector Quantization
• Reading about Boosting Algorithms
• See all videos under Ensembling

Day-17 (16th August, 2018)

• Performance Metrics Hands-on
• Khan Academy Vector dot products
• See Metrics Optimization

Day-18 (20th August, 2018)

• General read on Medium
• Read about Text Classification
• Read about scrape method in Pandas
• Read about FastText