prakhar21 / 50 Days Of Ml
A day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects
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50-Days-of-ML
A day to day plan for this challenge. Covers both theoritical and practical aspects.
I have build Docker Image with all the required dependencies till Day 21. Feel free to use it by pulling it using -> docker pull prakhar21/ml-utilities
Please see Deep Work 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
Day-19 (21st August, 2018)
- Glance through Sklearn Docs on Feature Selection
- Read Feature Selection - Analytics Vidhya
- See C2W1L4 and C2W1L5
- Implementing Feature Selection Methods
Day-20 (22nd August, 2018)
- Explore A fast and simple progress bar
- Casual read on Pandas - Tips/Tricks - 1 and Pandas - Tips/Tricks - 2
- See Day 35
- Implement data resampling techniques
Day-21 (23rd August, 2018)
- See all videos under C2W2
- Implement saving/loading of ML models
- Write Dockerfile
Day-22 (24th August, 2018)
- See and follow along Introduction to PyTorch
- Push Dockerfile and update Docker Readme.
Day-23 (25th August, 2018)
- Read Chapter 6 (till 6.1.2) from the book Mining Massive Datasets
- Read/Practice Day-26
Day-24 (26th August, 2018)
- Read Chapter 6 (till 6.1) from the book Mining Massive Datasets
Day-25 (27th August, 2018)
Day-26 (28th August, 2018)
- See 1, 2, 3 videos from Calculus
- See Week-1 (Video by David Silver)
Day-27 (29th August, 2018)
- Read about article on RL 1, 2, 3, 4
- Implement randomised cartpole balancer
Day-28 (30th August, 2018)
- Read paper
- Implement neural network in PyTorch
- PyTorch + TensorBoard
- Update Docker File/Image
Day-29 (31st August, 2018)
- See 4, 5, 6 videos from Calculus
- See 1, 2, 3, 4 videos from Linear Algebra
Day-30 (1st September, 2018)
- Implementing NN from scratch
- See 5, 6 videos from Linear Algebra
Day-31 (3rd September, 2018)
- Implement Cartpole using Cross Entropy method
Day-32 (4th September, 2018)
- Read about Q-Learning.
- See 7, 8, 9 videos from Linear Algebra
- See 7, 8 videos from Calculus
Day-33 (5th September, 2018)
- Read/Practice Day 51
- See But what is a Neural Network?
- Read Grammar correction in text usecase
Day-34 (6th September, 2018)
- See How Neural Networks learn
- Read Text Summarization
- See 10, 11 videos from Linear Algebra
- Read Neural Networks, Manifolds, and Topology
Day-35 (7th September, 2018)
- Implement Q-Learning
Day-36 (10th September, 2018)
- Complete Equations/Graphs/Functions
- See 9, 10 videos from Calculus
- See What does Backpropagation really do ?
Day-37 (11th September, 2018)
- See Backpropagation Calculus
- See 1, 2, 3 from Statistics - Khan Academy
Day-38 (12th September, 2018)
- Read 7 in Assignments
- See 4, 5, 6 from Statistics - Khan Academy
Day-39 (13th September, 2018)
- Read about Agglomerative Clustering
Day-40 (14th September, 2018)
- Read about Deep-Q-Networks and understand epsilon-greedy, replay buffer and target network in the same context.
- See 7, 8 from Statistics - Khan Academy
Day-41 (15th September, 2018)
- Read about Spectral Clustering
- See 9, 10, 11, 12 Statistics - Khan Academy
- Complete Finance and Python
Day-42 (17th September, 2018)
- Read Autoencoders Notebook
- Complete Week-1
Day-43 (18th September, 2018)
- See Neural Voice Cloning
- Complete Week-2
- Read Autoencoder in Text
Day-44 (19th September, 2018)
- Read 1-10 pages of A Primer on Neural Network Modelsfor Natural Language Processing
Day-45 (20th Spetember, 2018)
- Read 11-20 pages of A Primer on Neural Network Models for Natural Language Processing
Day-46 (21st September, 2018)
- Read 21-30 pages of A Primer on Neural Network Models for Natural Language Processing
Day-47 (22nd Spetember, 2018)
- Read 31-40 pages of A Primer on Neural Network Models for Natural Language Processing
Day-48 (22nd Spetember, 2018)
- Read 41-50 pages of A Primer on Neural Network Models for Natural Language Processing
Day-49 (23rd September, 2018)
- Read 51-60 pages of A Primer on Neural Network Models for Natural Language Processing
Day-50 (24th Spetember, 2018)
- Read 61-76 pages of A Primer on Neural Network Models for Natural Language Processing
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