Appendix: Additional Resources to be reviewed and read

Free books!:

• https://openstax.org/subjects
• https://www.openintro.org/stat/
• http://www.nltk.org/book/

Visualization: https://towardsdatascience.com/february-edition-data-visualization-ee524ca64416

NLP: https://towardsdatascience.com/intro-to-nlp-using-inaugural-speeches-of-presidents-8c7ca32cbdfe

Self tracking: https://towardsdatascience.com/what-should-you-be-tracking-in-2019-a9415a700897

Docker: https://towardsdatascience.com/learn-enough-docker-to-be-useful-b7ba70caeb4b

Interpretable Machine Learning:

• https://christophm.github.io/interpretable-ml-book/

• Logistic Regression Model: http://blog.yhat.com/posts/logistic-regression-python-rodeo.html

• Another Log Regression but Using Heatmap: http://www.data-mania.com/blog/logistic-regression-example-in-python/

• Strings as features: https://datascience.stackexchange.com/questions/5226/strings-as-features-in-decision-tree-random-forest

• LabelEncoder vs. Dictionary Vectorizer: https://stackoverflow.com/questions/33976845/using-categorical-data-as-features-in-sklean-logisticregression

• Why NA indicator instead of value imputation? : https://stats.stackexchange.com/questions/28860/why-adding-an-na-indicator-column-instead-of-value-imputation-for-randomforest

• Pandas - Get Dummies: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html

• Partial Dependence Plot: https://christophm.github.io/interpretable-ml-book/pdp.html

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• Capstone 1 - All the things that should have been done on the model around data cleaning, ecnoding, etc:

• • https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/
• • https://www.analyticsvidhya.com/blog/2015/11/easy-methods-deal-categorical-variables-predictive-modeling/
• • https://www.kdnuggets.com/2015/12/beyond-one-hot-exploration-categorical-variables.html
• • https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/
• • https://medium.com/hugo-ferreiras-blog/dealing-with-categorical-features-in-machine-learning-1bb70f07262d
• • https://hackernoon.com/my-self-created-ai-masters-degree-ddc7aae92d0e
• • Feature Hashing: https://blog.myyellowroad.com/using-categorical-data-in-machine-learning-with-python-from-dummy-variables-to-deep-category-66041f734512
• • Encoding & PySpark: https://www.datacamp.com/community/tutorials/categorical-data

Label Encoder, 1 Hot Encoding, Scaling on Continuous features, Imputation w/ flags, Using frequency or response rate to combine levels, Find & Replace, Using CategoryEncoders package for the library

https://hackernoon.com/my-self-created-ai-masters-degree-ddc7aae92d0e

Advanced Pipelines tutorial:

• https://www.kaggle.com/aashita/advanced-pipelines-tutorial

Rajiv Shah Video: https://twitter.com/srikanth_vikas/status/1092585613314772992

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Tableau:

• https://onlinehelp.tableau.com/current/pro/desktop/en-us/calculations_aggregation.htm
• https://onlinehelp.tableau.com/current/pro/desktop/en-us/calculations_calculatedfields_understand_types.htm
• https://www.tableau.com/learn/whitepapers/understanding-lod-expressions?signin=97b791a958d58e65907012e6cc1672a6
• https://onlinehelp.tableau.com/current/pro/desktop/en-us/calculations_calculatedfields_lod_overview.htm
• https://community.tableau.com/thread/141667
• https://kb.tableau.com/articles/howto/sorting-segments-within-stacked-bars-by-value

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Python:

• Welcome to the Everyday Python Project - http://interactivepython.org/runestone/static/everyday/index.html
• https://runestone.academy/runestone/static/thinkcspy/index.html
• https://runestone.academy/runestone/static/fopp/index.html
• CS for All - https://www.cs.hmc.edu/csforall/
• Problem Solving with Algorithms and Data Structures using Python - https://runestone.academy/runestone/static/pythonds/index.html
• How to Think like a Data Scientis - https://runestone.academy/runestone/static/httlads/index.html

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• http://www.cs.colorado.edu/~martin/slp.html#Chapter2

• http://www.cs.colorado.edu/~martin/slp.html#Chapter2

• https://cs.nyu.edu/courses/spring18/CSCI-UA.0480-009/

• https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-863j-natural-language-and-the-computer-representation-of-knowledge-spring-2003/index.htm

• https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/lecture-notes/

• https://ocw.mit.edu/courses/find-by-topic/#cat=engineering&subcat=computerscience&spec=artificialintelligence

• https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/

• https://www.aclweb.org/anthology/W14-5206

• https://towardsdatascience.com/a-comprehensive-guide-to-the-grammar-of-graphics-for-effective-visualization-of-multi-dimensional-1f92b4ed4149

• https://towardsdatascience.com/understanding-feature-engineering-part-3-traditional-methods-for-text-data-f6f7d70acd41

• https://spacy.io/usage/spacy-101

• https://towardsdatascience.com/using-what-if-tool-to-investigate-machine-learning-models-913c7d4118f

• https://towardsdatascience.com/pyviz-simplifying-the-data-visualisation-process-in-python-1b6d2cb728f1

• https://towardsdatascience.com/interpretable-machine-learning-1dec0f2f3e6b

• https://towardsdatascience.com/animations-with-matplotlib-d96375c5442c

• https://towardsdatascience.com/democratising-machine-learning-with-h2o-7f2f79e10e3f

• https://towardsdatascience.com/interactive-data-visualization-with-d3-js-43fc3428a27e

• https://www.oreilly.com/library/view/applied-text-analysis/9781491963036/ch04.html

NLP:

• https://www.youtube.com/watch?v=gJJQs47aUQ0
• https://www.youtube.com/watch?v=jB1-NukGZm0
• https://medium.com/@pemagrg/nlp-for-beninners-using-spacy-6161cf48a229

Fleshed out pythone notebook in Kaggle:

• https://www.kaggle.com/rahullalu/home-credit-default-risk-eda

• Better ways to ecnode: https://towardsdatascience.com/smarter-ways-to-encode-categorical-data-for-machine-learning-part-1-of-3-6dca2f71b159

Dealing with Categorical Variables:

• https://medium.com/hugo-ferreiras-blog/dealing-with-categorical-features-in-machine-learning-1bb70f07262d
• https://www.kaggle.com/rahullalu/home-credit-default-risk-eda
• https://www.kaggle.com/questions-and-answers/61528
• https://stackoverflow.com/questions/25239958/impute-categorical-missing-values-in-scikit-learn
• https://towardsdatascience.com/the-tale-of-missing-values-in-python-c96beb0e8a9d
• https://www.datacamp.com/community/tutorials/categorical-data

Multi Level Classification:

• https://towardsdatascience.com/multi-label-text-classification-with-scikit-learn-30714b7819c5

• https://towardsdatascience.com/smarter-ways-to-encode-categorical-data-for-machine-learning-part-1-of-3-6dca2f71b159

• https://www.kaggle.com/dansbecker/using-categorical-data-with-one-hot-encoding

• https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer

• https://scikit-learn.org/stable/modules/impute.html

• https://stackoverflow.com/questions/42204250/how-to-do-onehotencoding-in-sklearn-pipeline

• https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html

• https://www.kaggle.com/learn/embeddings

• https://www.kaggle.com/alexisbcook/xgboost

• https://www.kaggle.com/alexisbcook/cross-validation

• https://www.kaggle.com/alexisbcook/pipelines

• https://www.kaggle.com/alexisbcook/categorical-variables

• https://www.kaggle.com/learn/intermediate-machine-learning

• https://www.kaggle.com/learn/pandas

• https://www.kaggle.com/learn/overview

• https://www.datacamp.com/community/tutorials/pickle-python-tutorial

• https://stackoverflow.com/questions/41335718/keep-same-dummy-variable-in-training-and-testing-data

• https://jorisvandenbossche.github.io/blog/2018/05/28/scikit-learn-columntransformer/

• https://stackoverflow.com/questions/33091376/python-what-is-exactly-sklearn-pipeline-pipeline

• https://stackoverflow.com/questions/39001956/sklearn-pipeline-how-to-apply-different-transformations-on-different-columns

• https://towardsdatascience.com/preprocessing-with-sklearn-a-complete-and-comprehensive-guide-670cb98fcfb9

• https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e

• https://medium.com/vickdata/a-simple-guide-to-scikit-learn-pipelines-4ac0d974bdcf

• https://medium.com/vickdata/programming-skills-a-complete-roadmap-for-learning-data-science-part-1-7913b289751b

• https://www.kaggle.com/sermakarevich/sklearn-pipelines-tutorial

• https://medium.com/@chrisfotache/text-classification-in-python-pipelines-nlp-nltk-tf-idf-xgboost-and-more-b83451a327e0

• https://medium.com/dunder-data/from-pandas-to-scikit-learn-a-new-exciting-workflow-e88e2271ef62

• https://github.com/scikit-learn/scikit-learn/issues/12494

• https://medium.com/hugo-ferreiras-blog/dealing-with-categorical-features-in-machine-learning-1bb70f07262d

• https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html#pipelining

• https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html

• https://medium.com/@chrisfotache/text-classification-in-python-pipelines-nlp-nltk-tf-idf-xgboost-and-more-b83451a327e0

• https://www.datacamp.com/community/tutorials/naive-bayes-scikit-learn

• https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html

• http://shop.oreilly.com/product/0636920034919.do

• http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html

• https://stackoverflow.com/questions/28384680/scikit-learns-pipeline-a-sparse-matrix-was-passed-but-dense-data-is-required

• http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html

• https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html

• https://www.datacamp.com/community/tutorials/naive-bayes-scikit-learn

• https://www.quora.com/What-is-the-difference-between-tfidfVectorizer-and-TfidfTransformer-And-what-about-their-output

• https://www.tensorscience.com/nlp/sentiment-analysis-tutorial-in-python-classifying-reviews-on-movies-and-products

• SMOTE: https://towardsdatascience.com/yet-another-twitter-sentiment-analysis-part-1-tackling-class-imbalance-4d7a7f717d44

• Wining Data Science Competitions: https://medium.com/manomano-tech/how-i-won-the-data-science-olympics-2019-with-code-550a8d9c8228

• DS Projects Wiki: https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/

• Dataops: https://retina.ai/blog/dataops-principles/?utm_campaign=Data_Elixir&utm_medium=email&utm_source=Data_Elixir_236

https://www.dataquest.io/blog/free-books-learn-data-science/

• https://jalammar.github.io/visual-numpy/

• https://eng.uber.com/neural-networks-uncertainty-estimation/

• https://medium.com/manomano-tech/how-i-won-the-data-science-olympics-2019-with-code-550a8d9c8228

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

• http://statweb.stanford.edu/~jtaylo/courses/stats203/notes/penalized.pdf

• https://medium.com/microsoftazure/credit-card-fraud-detection-with-azure-data-science-virtual-machine-a121dc2fd1b5

• https://tim.blog/2019/07/03/the-tim-ferriss-show-transcripts-josh-waitzkin-how-to-cram-2-months-of-learning-into-1-day-375/

• https://blog.floydhub.com/localize-and-detect-corrosion-with-tensorflow-object-detection-api/

• https://www.forbes.com/sites/louiscolumbus/2018/06/11/10-ways-machine-learning-is-revolutionizing-supply-chain-management/#c328c8f3e370

• https://towardsdatascience.com/data-driven-growth-with-python-part-2-customer-segmentation-5c019d150444

• https://docs.python-guide.org/

• https://github.com/smit5490/CustomerChurn/blob/master/ChurnRateAnalysis.ipynb

When to use which visualization packages?:

• https://pbpython.com/python-vis-flowchart.html

Understanding a grammar of graphics:

• http://vita.had.co.nz/papers/layered-grammar.pdf

Installing from Jupyter notebook:

• https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/

Code Review & Linters:

• Essential Tools for Python: https://buildmedia.readthedocs.org/media/pdf/essential-python-tools/latest/essential-python-tools.pdf

• Using Pylint in Jupyter notebooks: https://stackoverflow.com/questions/50358327/using-pylint-in-ipython-jupyter-notebook

• Other code checkers: https://stackoverflow.com/questions/26126853/verifying-pep8-in-ipython-notebook-code

• Which linter to use: https://www.reddit.com/r/Python/comments/82hgzm/any_advantages_of_flake8_over_pylint/

• Bandit (Linter): https://developer.rackspace.com/blog/getting-started-with-bandit/

• Writing good unit tests: https://dev.to/itnext/writing-good-unit-tests-a-step-by-step-tutorial-1l4f

• Pair Programming Patterns: https://dev.to/rfornal/pair-programming-patterns-384o

• Building Profitable Side Projects: https://dev.to/csallen/three-tips-for-building-profitable-side-projects-3lmf

• Are you a mediocre developer: https://dev.to/yaser/are-you-a-mediocre-developer-me-too-34ed

• Interview Study Guide for software Engineers: https://dev.to/seattledataguy/the-interview-study-guide-for-software-engineers-764

• IndieHackers: https://www.indiehackers.com/

• https://towardsdatascience.com/why-youre-not-a-job-ready-data-scientist-yet-1a0d73f15012

• https://towardsdatascience.com/what-70-of-data-science-learners-do-wrong-ac35326219e4

• https://towardsdatascience.com/10-simple-hacks-to-speed-up-your-data-analysis-in-python-ec18c6396e6b

• https://towardsdatascience.com/top-10-statistics-mistakes-made-by-data-scientists-2d58ccf12ab1

• https://www.udemy.com/master-the-coding-interview-data-structures-algorithms/learn/lecture/12202018#overview

• https://realpython.com/python-testing/

• https://www.kaggle.com/tammyrotem/ab-tests-with-python/data

• https://measuringu.com/statistically-significant/

• https://github.com/dwyl/learn-ab-and-multivariate-testing

• http://www.pythonlikeyoumeanit.com/index.html

https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a

https://blog.usejournal.com/how-to-get-your-product-and-engineering-teams-running-like-clockwork-8c5c342721df

https://towardsdatascience.com/data-science-project-flow-for-startups-282a93d4508d

https://www.dataengineeringpodcast.com/

https://www.youtube.com/watch?v=8jIgng-ViPU

https://www.youtube.com/watch?v=0cEfe9mSatM

http://www.pythontutor.com/

Regularization:

• https://www.quora.com/What-is-regularization-in-machine-learning
• https://towardsdatascience.com/regularization-in-machine-learning-76441ddcf99a
• https://en.wikipedia.org/wiki/Regularization_(mathematics)#Regularization_in_statistics_and_machine_learning
• https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization-How-does-it-solve-the-problem-of-overfitting-Which-regularizer-to-use-and-when
• https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c

Tutorials on various items:

• https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook
• https://www.datacamp.com/community/tutorials/machine-learning-python
• https://www.datacamp.com/community/tutorials/social-network-analysis-python
• https://www.datacamp.com/community/tutorials/facebook-chatbot-python-deploy
• https://www.datacamp.com/community/tutorials/machine-deep-learning
• https://www.datacamp.com/community/tutorials/deep-learning-python
• https://www.datacamp.com/community/tutorials/time-series-analysis-tutorial

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• Understanding Monte Carlo Simulation: https://towardsdatascience.com/understanding-monte-carlo-simulation-eceb4c9cad4
• Makinf Predictions Using PySpark: https://www.analyticsvidhya.com/blog/2019/12/streaming-data-pyspark-machine-learning-model/

Hi @channel I am currently working on web scraping and am supposed to write a unit test for my scrapy spider. I am trying to understand how to use Mock classes for mocking the responses, but unfortunately haven't been able to get hold of how it is actually working and how can I write my unit test using it. If anyone of you have worked on this before, can you please share some resources which I can use to make sense of the concept and put in practice? Thanks

• https://stackoverflow.com/questions/6456304/scrapy-unit-testing

• https://github.com/scrapy/scrapy/blob/master/tests/test_contracts.py

• https://dzone.com/articles/properly-unit-testing-scrapy

• https://pypi.org/project/scrapy-autounit/

• https://www.quora.com/How-do-I-write-tests-for-Scrapy-project

• http://marcela-campo.blogspot.com/2015/03/unit-testing-scrapy-part-1-integrating.html

• https://www.microsoft.com/en-us/research/blog/microsoft-makes-ai-debugging-and-visualization-tool-tensorwatch-open-source/ Microsoft ResearchMicrosoft Research TensorWatch: A debugging and visualization system for machine learning New debugging and visualization tool TensorWatch allows researchers and engineers to easily implement a variety of advanced scenarios when training their machine learning models. The possibilities abound. Jun 25th

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@channel Hi! I am looking for some refences for on the following topics related to Time Series problems- contextual anomaly detection/finding irregular data patterns imputation/smoothing for the missing data forecasting techniques I have been asked to give a short presentation on these topics during the interview. I would appreciate if you could share some articles. Mainly I am looking for the first articles related to contextual anomaly detection

Jean-Sebastien - DSDJ Team 4 days ago Here are some references that could be useful: https://arxiv.org/abs/1904.00548 https://towardsdatascience.com/a-note-about-finding-anomalies-f9cedee38f0b https://era.library.ualberta.ca/items/af6513a0-e09a-47d3-8d74-2d6862f991e4 https://journalofbigdata.springeropen.com/articles/10.1186/s40537-014-0011-y arXiv.orgarXiv.org Unsupervised Contextual Anomaly Detection using Joint Deep... A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a... MediumMedium A note about finding anomalies Anomaly detection refers to the task of finding observations that do not conform to the normal, expected behaviour… Reading time 10 min read Apr 10th, 2018 (173 kB) https://miro.medium.com/max/1200/1*6_pj7EF6xY9dB2lgdhQ69g.png ERAERA Time Series Contextual Anomaly Detection... | ERA Anomaly detection in time series is one of the fundamental issues in data mining. It addresses various problems in different domains such... Keywords Market manipulation, Sentiment analysis, Time series, Anomaly detection, Stock market Rights This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law. Journal of Big Data Contextual anomaly detection framework for big sensor data The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain their real-time characteristics. The pervasiveness of data combined with the problem that many existing algorithms only consider the content of the data source; e.g. a sensor reading itself without concern for its context, leaves room for potential improvement. The proposed work defines a contextual anomaly detection framework. It is composed of two distinct steps: content detection and context detection. The content detector is used to determine anomalies in real-time, while possibly… Show more :ultra_fast_parrot: 1 :+1: 1

Chris - DSDJ Team 4 days ago For Time Series: https://dimensionless.in/beginners-guide-for-time-series-forecasting/ https://varjo.ktto.fi/files/ekonometrian-jatkokurssi-1498336030292.pdf https://www.kdnuggets.com/2018/03/time-series-dummies-3-step-process.html https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/ DIMENSIONLESS TECHNOLOGIES PVT.LTD.DIMENSIONLESS TECHNOLOGIES PVT.LTD. Beginner’s Guide for Time-Series Forecasting | Blog | Dimensionless This is a biginners guide to time series forecasting. We will solve a small time series problem & learn time series forecating along the way. Feb 13th KDnuggetsKDnuggets Time Series for Dummies – The 3 Step Process - KDnuggets Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model. Machine Learning PlusMachine Learning Plus ARIMA Model - Complete Guide to Time Series Forecasting in Python | ML+ Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend it to Seasonal ARIMA (SARIMA) and SARIMAX models. You will also see how to build autoarima models in python Feb 18th :+1: 1

Chris - DSDJ Team 4 days ago https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/ https://machinelearningmastery.com/lstm-model-architecture-for-rare-event-time-series-forecasting/ https://machinelearningmastery.com/findings-comparing-classical-and-machine-learning-methods-for-time-series-forecasting/ Machine Learning MasteryMachine Learning Mastery How to Develop LSTM Models for Time Series Forecasting Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time … Nov 13th, 2018 (256 kB) https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2018/11/How-to-Develop-LSTM-Models-for-Time-Series-Forecasting.jpg Machine Learning MasteryMachine Learning Mastery LSTM Model Architecture for Rare Event Time Series Forecasting Time series forecasting with LSTMs directly has shown little success. This is surprising as neural networks are known to be able to learn complex non-linear relationships and the LSTM is perhaps the most successful type of recurrent neural network that is capable of directly supporting multivariate sequence prediction problems. A recent study performed at Uber … Nov 1st, 2018 (228 kB) https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2018/11/Sliding-Window-Approach-to-Modeling-Time-Series.png Machine Learning MasteryMachine Learning Mastery Comparing Classical and Machine Learning Algorithms for Time Series Forecasting Machine learning and deep learning methods are often reported to be the key solution to all predictive modeling problems. An important recent study evaluated and compared the performance of many classical and modern machine learning and deep learning methods on a large and diverse set of more than 1,000 univariate time series forecasting problems. The … Oct 30th, 2018 (299 kB) https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2018/10/Findings-Comparing-Classical-and-Machine-Learning-Methods-for-Time-Series-Forecasting.jpg

Chris - DSDJ Team 4 days ago https://otexts.com/fpp2/ otexts.comotexts.com Forecasting: Principles and Practice 2nd edition :ultra_fast_parrot: 1

Prashant 4 days ago @Chris - DSDJ Team @Jean-Sebastien - DSDJ Team Thank you very much

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Also potentially recommend (looks interesting) -- http://www.pythonvisually.com/

pythonvisually.compythonvisually.com Learn Python VISUALLY - Learn Differently Learn Python VISUALLY

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Syed Toufiq Dec 10th at 12:56 AM Does anyone has handon with attribution modelling , can you please suggest good resources to get started and get understanding for the same 3 replies

Mikiko Bazeley - DSDJ Team 9 days ago Is this a question related to a marketing project? Attribution modeling typically is the case where marketin teams get leads and want to understand the origin or "attribute" the origin to a specific marketing channel (Facebook Ads vs. Google Search, social media vs. email marketing, etc). Does that sound like the right understanding?

Syed Toufiq 9 days ago Yes its related to Marketing project , thanks for the reply sounds right i need to dive into more details , Can you recommend any good resources?

Mikiko Bazeley - DSDJ Team 8 days ago So quick note, attribution models typically tend to be rules based approaches that aren't mathematical or rigorous and are very heuristic based. And attribution modeling isn't modeling in the sense of data science or machine learning (the reality is there's nothing really intelligent about it). I've posted some resources below: https://agencyanalytics.com/blog/marketing-attribution-models <= Different rules based approaches for creating an attribution model https://www.kaushik.net/avinash/multi-channel-attribution-modeling-good-bad-ugly-models/ <= More about attribution models https://www.referralsaasquatch.com/marketing-attribution-models/ <= Things to think about when choosing attribution models https://www.ngdata.com/marketing-attribution-models-for-success/ <= More opinions about models https://cxl.com/blog/conversion-attribution-modeling/ <= Another resource If you're using google analytics: https://support.google.com/analytics/topic/1631741?hl=en&ref_topic=3544907

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https://cedar.buffalo.edu/~srihari/CSE676/

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https://deeplearning.mit.edu/ - Deep Learning