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ML dataScience Basics

Courses to learn Datascience :
- https://app.dataquest.io
- https://lambdaschool.com/courses/data-science

Prerequisites :

Important Architectures/Algorithms

Basics

Feature Scaling : (The contour analysis shows an extremely skewed figure in case the parameters are on varying scales, which causes convergence to be extremely slow and inefficient)
- https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e
Regularization :
- https://towardsdatascience.com/regularization-in-machine-learning-76441ddcf99a
Intuition behind gradient descent :
- https://towardsdatascience.com/machine-learning-bit-by-bit-multivariate-gradient-descent-e198fdd0df85
Bayes Theorm :
- https://en.wikipedia.org/wiki/Bayes%27_theorem
Decision Matrix :
- http://web.iitd.ac.in/~sumeet/mlintro_doc.pdf

Logistic regression

Line Search algorithm - *To create efficient classification models like "Conjugate gradient", "BFGS", and "L-BFGS" ...
- https://www.ii.uib.no/~lennart/drgrad/More1994.pdf

Neural Networks

Derivation of backpropagation Algorithm (Hardest math in ML):
- https://medium.com/binaryandmore/beginners-guide-to-deriving-and-implementing-backpropagation-e3c1a5a1e536
- https://sudeepraja.github.io/Neural/
Optimal brain damage algorithm (CNN compression) :
- http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf
Cascade correlation (network) learning architecture :
- http://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture.pdf
Avoiding local optima using simulated annealing :
- https://en.wikipedia.org/wiki/Simulated_annealing
- http://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/photoz/.g/web/glossary/anneal.html
Parameter Estimation (Maximum Likelihood Estimation):
- http://statweb.stanford.edu/~susan/courses/s200/lectures/lect11.pdf

Decision Trees

Regression Trees - Model Trees :
- https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205
- https://towardsdatascience.com/introduction-to-model-trees-6e396259379a
Cross Validation (Very Important) :
- https://towardsdatascience.com/cross-validation-in-machine-learning-72924a69872f
Bagging and boosting :
- https://towardsdatascience.com/decision-tree-ensembles-bagging-and-boosting-266a8ba60fd9 Boosting is a very powerful tool works really well with decision trees
- Random Forests -
  - https://towardsdatascience.com/understanding-random-forest-58381e0602d2

Evaluation/ Evaluation measures

Bootstrapping :
- https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60
Explaination to Accuracy, Recall, Precision, F-Score, Specificity and sensitivity :
- https://towardsdatascience.com/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124
Supervised-Learning : Ranking :
- https://medium.com/@nikhilbd/intuitive-explanation-of-learning-to-rank-and-ranknet-lambdarank-and-lambdamart-fe1e17fac418
- https://towardsdatascience.com/learning-to-rank-with-python-scikit-learn-327a5cfd81f

Graphical Models

Basics
- https://lili-mou.github.io/resource/MarkovNet.pdf important - contains condensed content on everything
- http://mlg.eng.cam.ac.uk/zoubin/talks/lect2gm.pdf
Markov Blanket for directed and undirected graphs
- https://library.bayesia.com/display/FAQ/Markov+Blankets
  - Markov networks can be used to factorize dependencies through which we can remove unnecessary dependencies from an equation
Hammersley–Clifford theorem
- https://en.wikipedia.org/wiki/Hammersley%E2%80%93Clifford_theorem
Bayesian Networks :
- http://www.cs.ru.nl/~peterl/teaching/CI/bbn1-new4.pdf
Representation of Undirected Graphical Model
- https://www.cs.cmu.edu/~epxing/Class/10708-17/notes-17/10708-scribe-lecture3.pdf
Hidden Markov Model - useful in NLP context

Clustering

https://towardsdatascience.com/clustering-on-mixed-type-data-8bbd0a2569c3

Partitional

Hierarchical

Density-Based

Gaussian Mixture Models

Reinforcement learning

Temporal difference learning
- https://medium.com/@violante.andre/simple-reinforcement-learning-temporal-difference-learning-e883ea0d65b0

Learning Theory

Error Generalization
- https://medium.com/@yixinsun_56102/understanding-generalization-error-in-machine-learning-e6c03b203036
Emperical Error Minimization :
- https://towardsdatascience.com/learning-theory-empirical-risk-minimization-d3573f90ff77
- (Optional) https://towardsdatascience.com/what-is-empirical-risk-minimization-erm-ef9edc76b48
Vapnik - Chenrvenenkis Dimensions :
- https://towardsdatascience.com/measuring-the-power-of-a-classifier-c765a7446c1c

Image classification Models

Image classification using Tensorflow
- https://medium.com/@tifa2up/image-classification-using-deep-neural-networks-a-beginner-friendly-approach-using-tensorflow-94b0a090ccd4
Image classification using Keras
- https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/
Machine Learning practice
- https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/beginners-guide-regression-analysis-plot-interpretations/tutorial/