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.
- 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
- 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
- Implementing of Decision Trees
- See lectures from Coursera - 2nd week and Coursera - 4th week
- Khan Academy Vector's Section
- Light read on Stacking Classifier
- Implementing - Handeling missing values using pandas
- General read on EM for data imputation
- Read about Model Evaluation
- See Khan Academy Linear combinatations & span and Linear Dependence/Independence
- Explore a Helper Lib
- See Khan Academy Subspaces
- Practice Mlxtend
- Read/Practice Day-33 & Day-34
- Light read on Vector Quantization
- Reading about Boosting Algorithms
- See all videos under Ensembling
- Performance Metrics Hands-on
- Khan Academy Vector dot products
- See Metrics Optimization
- General read on Medium
- Read about Text Classification
- Read about scrape method in Pandas
- Read about FastText
- Glance through Sklearn Docs on Feature Selection
- Read Feature Selection - Analytics Vidhya
- See C2W1L4 and C2W1L5
- Implementing Feature Selection Methods
- 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
- See all videos under C2W2
- Implement saving/loading of ML models
- Write Dockerfile
- See and follow along Introduction to PyTorch
- Push Dockerfile and update Docker Readme.
- Read Chapter 6 (till 6.1.2) from the book Mining Massive Datasets
- Read/Practice Day-26
- Read Chapter 6 (till 6.1) from the book Mining Massive Datasets
- See 1, 2, 3 videos from Calculus
- See Week-1 (Video by David Silver)
- Read about article on RL 1, 2, 3, 4
- Implement randomised cartpole balancer
- Read paper
- Implement neural network in PyTorch
- PyTorch + TensorBoard
- Update Docker File/Image
- See 4, 5, 6 videos from Calculus
- See 1, 2, 3, 4 videos from Linear Algebra
- Implementing NN from scratch
- See 5, 6 videos from Linear Algebra
- Implement Cartpole using Cross Entropy method
- Read about Q-Learning.
- See 7, 8, 9 videos from Linear Algebra
- See 7, 8 videos from Calculus
- Read/Practice Day 51
- See But what is a Neural Network?
- Read Grammar correction in text usecase
- See How Neural Networks learn
- Read Text Summarization
- See 10, 11 videos from Linear Algebra
- Read Neural Networks, Manifolds, and Topology
- Implement Q-Learning
- Complete Equations/Graphs/Functions
- See 9, 10 videos from Calculus
- See What does Backpropagation really do ?
- See Backpropagation Calculus
- See 1, 2, 3 from Statistics - Khan Academy
- Read 7 in Assignments
- See 4, 5, 6 from Statistics - Khan Academy
- Read about Agglomerative Clustering
- 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
- Read about Spectral Clustering
- See 9, 10, 11, 12 Statistics - Khan Academy
- Complete Finance and Python
- Read Autoencoders Notebook
- Complete Week-1
- See Neural Voice Cloning
- Complete Week-2
- Read Autoencoder in Text
- Read 1-10 pages of A Primer on Neural Network Modelsfor Natural Language Processing
- Read 11-20 pages of A Primer on Neural Network Models for Natural Language Processing
- Read 21-30 pages of A Primer on Neural Network Models for Natural Language Processing
- Read 31-40 pages of A Primer on Neural Network Models for Natural Language Processing
- Read 41-50 pages of A Primer on Neural Network Models for Natural Language Processing
- Read 51-60 pages of A Primer on Neural Network Models for Natural Language Processing
- Read 61-76 pages of A Primer on Neural Network Models for Natural Language Processing