links.md

JUPYTER

https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/

https://ndres.me/post/best-jupyter-notebook-extensions/

CONCEPTS - TERMINOLOGY

https://towardsdatascience.com/machine-learning-from-scratch-part-1-76603dececa6

ML OVERVIEW

https://medium.com/cracking-the-data-science-interview/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7

https://towardsdatascience.com/ten-machine-learning-algorithms-you-should-know-to-become-a-data-scientist-8dc93d8ca52e

LINEAR ALGEBRA

https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c

https://alyssaq.github.io/2015/visualising-matrices-and-affine-transformations-with-python/

GRADIENT DESCENT

https://towardsdatascience.com/gradient-descent-algorithm-and-its-variants-10f652806a3

http://fa.bianp.net/teaching/2018/eecs227at/gradient_descent.html

ADVANCED

https://distill.pub/2017/momentum/

NUMPY

http://cs231n.github.io/python-numpy-tutorial/

PANDAS

https://jalammar.github.io/gentle-visual-intro-to-data-analysis-python-pandas/

https://towardsdatascience.com/quick-dive-into-pandas-for-data-science-cc1c1a80d9c4

https://jalammar.github.io/visualizing-pandas-pivoting-and-reshaping/

https://github.com/mm-mansour/Fast-Pandas/blob/master/README.md

SNIPPETS

https://medium.com/@msalmon00/helpful-python-code-snippets-for-data-exploration-in-pandas-b7c5aed5ecb9

https://jeffdelaney.me/blog/useful-snippets-in-pandas/

PACKAGES

https://github.com/pandas-profiling/pandas-profiling

TIMESERIES

https://medium.com/jbennetcodes/dealing-with-datetimes-like-a-pro-in-pandas-b80d3d808a7f

https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea

GUIDE

https://www.ritchieng.com/pandas-introduction/

TRAIN TEST SPLIT

https://www.fast.ai/2017/11/13/validation-sets/

https://towardsdatascience.com/how-dis-similar-are-my-train-and-test-data-56af3923de9b

https://heartbeat.fritz.ai/model-evaluation-selection-i-30d803a44ee

MISSING DATA

https://towardsdatascience.com/the-use-of-knn-for-missing-values-cf33d935c637

OUTLIER DETECTION

https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e

FEATURES

https://patsy.readthedocs.io/en/latest/overview.html

SCALING

http://sebastianraschka.com/Articles/2014_about_feature_scaling.html

SELECTION

https://towardsdatascience.com/a-feature-selection-tool-for-machine-learning-in-python-b64dd23710f0

https://github.com/DistrictDataLabs/yellowbrick/blob/develop/README.md

IMBALANCED DATASETS

https://elitedatascience.com/imbalanced-classes

https://blog.dominodatalab.com/imbalanced-datasets/

UNSUPERVISED LEARNING

DIMENSIONALITY REDUCTION

http://setosa.io/ev/principal-component-analysis/

https://towardsdatascience.com/https-medium-com-abdullatif-h-dimensionality-reduction-for-dummies-part-1-a8c9ec7b7e79

https://towardsdatascience.com/reducing-dimensionality-from-dimensionality-reduction-techniques-f658aec24dfe

CLUSTERING

https://towardsdatascience.com/unsupervised-learning-with-python-173c51dc7f03

SUPERVISED

LOSS FUNCTIONS

https://towardsdatascience.com/common-loss-functions-in-machine-learning-46af0ffc4d23

REGRESSION

LINEAR

http://setosa.io/ev/ordinary-least-squares-regression/

https://towardsdatascience.com/5-types-of-regression-and-their-properties-c5e1fa12d55e

LOGISTIC

https://towardsdatascience.com/building-a-logistic-regression-in-python-301d27367c24

PROJECT

https://hackernoon.com/i-built-a-linearregression-that-can-play-pong-with-me-7b00d73f3fcc

BIAS VARIANCE TRADE-OFF

http://www.r2d3.us/visual-intro-to-machine-learning-part-2/

https://towardsdatascience.com/polynomial-regression-bbe8b9d97491

https://utkuufuk.github.io/2018/05/04/learning-curves/

https://www.dataquest.io/blog/learning-curves-machine-learning/

REGULARIZATION

https://thomas-tanay.github.io/post--L2-regularization/

CLASSIFICATION

SVM

https://towardsdatascience.com/i-support-vector-machines-and-so-should-you-7af122b6748

https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93

https://dash-gallery.plotly.host/dash-svm

INFO THEORY

http://colah.github.io/posts/2015-09-Visual-Information/

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

ENSEMBLES

https://quantdare.com/what-is-the-difference-between-bagging-and-boosting/

RANDOM FOREST

https://towardsdatascience.com/an-implementation-and-explanation-of-the-random-forest-in-python-77bf308a9b76

https://lethalbrains.com/learn-ml-algorithms-by-coding-decision-trees-439ac503c9a4

https://towardsdatascience.com/interactive-visualization-of-decision-trees-with-jupyter-widgets-ca15dd312084

GRADIENT BOOSTING

https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d

METRICS

https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4

https://classeval.wordpress.com/simulation-analysis/roc-and-precision-recall-with-imbalanced-datasets/

CONFUSION MATRIX

https://towardsdatascience.com/confused-by-the-confusion-matrix-e26d5e1d74eb

ROC CURVE

https://towardsdatascience.com/receiver-operating-characteristic-curves-demystified-in-python-bd531a4364d0

http://www.navan.name/roc/

PRECISION-RECALL CURVE

https://acutecaretesting.org/en/articles/precision-recall-curves-what-are-they-and-how-are-they-used

HYPARAMETERS TUNING

http://sujitpal.blogspot.com/2016/12/random-vs-grid-search-which-is-better.html

https://medium.com/polyaxon/hyperparameters-tuning-with-polyaxon-9403f8ea85be

INTERPRETABILITY

https://towardsdatascience.com/understanding-model-predictions-with-lime-a582fdff3a3b

https://github.com/marcotcr/lime/blob/master/README.md

https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27

https://github.com/slundberg/shap

GUIDE

https://christophm.github.io/interpretable-ml-book/index.html

PLOTTING

MATPLOTLIB

https://realpython.com/python-matplotlib-guide/

GALLERY

https://python-graph-gallery.com/

BOXPLOT

https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51

VISUALIZATION GUIDE

https://serialmentor.com/dataviz/

ADVANCED VISUALIZATION

https://github.com/ContextLab/hypertools

DEPLOYMENT

TESTING

https://medium.com/@keeper6928/how-to-unit-test-machine-learning-code-57cf6fd81765

https://medium.com/@keeper6928/mltest-automatically-test-neural-network-models-in-one-function-call-eb6f1fa5019d

LIST OF RESOURCES

https://p.migdal.pl/interactive-machine-learning-list/

DEEP LEARNING OVERVIEW

https://medium.com/cracking-the-data-science-interview/the-10-deep-learning-methods-ai-practitioners-need-to-apply-885259f402c1

NEURAL NETWORKS

https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/

https://jalammar.github.io/feedforward-neural-networks-visual-interactive/

http://neuralnetworksanddeeplearning.com/chap4.html

VISUALIZATION

https://github.com/keplr-io/quiver

TROUBLESHOOTING

http://theorangeduck.com/page/neural-network-not-working

ADVERSARIAL

https://blog.godatadriven.com/rod-fool-neural-network