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Title: GitHub - PlayingNumbers/ML_Process_Course: Public repo for the 365 Data Science ML Process Course

Open Graph Title: GitHub - PlayingNumbers/ML_Process_Course: Public repo for the 365 Data Science ML Process Course

X Title: GitHub - PlayingNumbers/ML_Process_Course: Public repo for the 365 Data Science ML Process Course

Description: Public repo for the 365 Data Science ML Process Course - PlayingNumbers/ML_Process_Course

Open Graph Description: Public repo for the 365 Data Science ML Process Course - PlayingNumbers/ML_Process_Course

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READMEhttps://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#ml-process-course
ML Process Coursehttps://365datascience.com/learn-machine-learning-process-a-z/
The Machine Learning A-Z Bundlehttps://bit.ly/3NAZ5oP
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#flashcards
Ankiweb.nethttps://ankiweb.net
herehttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/365datascience_ml_process_flashcards.apkg
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#table-of-contents
Coding Workbooks for Each Coursehttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#coding-workbooks-for-each-course
Data Science Blogshttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#data-science-blogs
Applying MLhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#2-applying-ml
Problem Framinghttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#3-problem-framing
Data Collectionhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#4-data-collection
Data Preprocessinghttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#5-data-preprocessing
Exploratory Data Analysishttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#6-exploratory-data-analysis
Feature Engineeringhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#7-feature-engineering
Cross Validationhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#8-cross-validation
Feature Selectionhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#9-feature-selection
Imbalanced Datahttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#10-imbalanced-data
Modelinghttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#11-modeling
Model Evaluationhttps://github.com/PlayingNumbers/ML_Process_Course/blob/main/README.md#12-model-evaluation
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#coding-workbooks-for-each-course
5-Missing Valueshttps://www.kaggle.com/code/kenjee/dealing-with-missing-values-section-5-1
5-Outliershttps://www.kaggle.com/code/kenjee/dealing-with-outliers-section-5-2
5-Missing Valueshttps://colab.research.google.com/drive/1P-4i_T1UE8_PLZibNApGbGPDxhOJDnd8?usp=sharing
5-Outliershttps://colab.research.google.com/drive/1e_9VUn48sOebsEmDMRZ2R7OEkJLM9Zxt?usp=sharing
6-EDAhttps://www.kaggle.com/code/kenjee/basic-eda-example-section-6
6-EDAhttps://colab.research.google.com/drive/18sWPkf2o6yX2RJu0YRZRCbJqNzc5q8ZS?usp=sharing
7-Categoricalshttps://www.kaggle.com/code/kenjee/categorical-feature-engineering-section-7-1
7-Continuoushttps://www.kaggle.com/code/kenjee/numeric-feature-engineering-section-7-2
7-Categoricalshttps://colab.research.google.com/drive/1F94kWYM_GTb-Neh_jCte04BxfcUdBodn?usp=sharing
7-Continuoushttps://colab.research.google.com/drive/1SGwguOuloOG7nd3OoOGBALR9jOg26UNt?usp=sharing
8-Cross Validationhttps://www.kaggle.com/code/kenjee/cross-validation-foundations-section-8
8-Cross Validationhttps://colab.research.google.com/drive/1xsVT5MWAX1Yq8KXMPbqU6DBti-NN17YH?usp=sharing
9-Feature Selectionhttps://www.kaggle.com/code/kenjee/feature-selection-section-9
9-Feature Selectionhttps://colab.research.google.com/drive/19uXC7Cm_K1FDTjkcRVjxcAY4f7dv6LDV?usp=sharing
10-Imbalanced Datahttps://www.kaggle.com/code/kenjee/dealing-with-imbalanced-data-section-10
10-Imbalanced Datahttps://colab.research.google.com/drive/1pulqugw0V1xyoMbQrB3aTdlwO0KDXDFf?usp=sharing
11-Model Selectionhttps://www.kaggle.com/code/kenjee/model-building-example-section-11
11-Model Selectionhttps://colab.research.google.com/drive/1oV675pKGmCLIYE44a_Quw1s66c4LVcOq?usp=sharing
12-Model Evaluation Classificationhttps://www.kaggle.com/code/kenjee/model-evaluation-classification-section-12
12-Model Evaluation Regressionhttps://www.kaggle.com/code/kenjee/model-evaluation-regression-12
12-Model Evaluation Classificationhttps://colab.research.google.com/drive/1FYHAL3lbv7Rdh3EV9TooMFkPNv6MEJly?usp=sharing
12-Model Evaluation Regressionhttps://colab.research.google.com/drive/1_of9a48P-rGkrS8US9YgwQm_58_1unAV?usp=sharing
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#data-science-blogs
AirBnB Engineeringhttps://medium.com/airbnb-engineering
Spotify Researchhttps://research.atspotify.com/blog/
Netflix Researchhttps://research.netflix.com/
DoorDash ML Bloghttps://doordash.engineering/category/data-science-and-machine-learning/
Uber Engineeringhttps://www.uber.com/blog/honolulu/engineering/
Lyft Engineeringhttps://eng.lyft.com/tagged/data-science
Shopify Engineeringhttps://shopify.engineering/topics/data-science-engineering
Meta Engineeringhttps://engineering.fb.com/
LinkedIn Engineeringhttps://engineering.linkedin.com/blog
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#2-applying-ml
Analytics Hierarchy of Needs by Ryan Foleyhttps://towardsdatascience.com/the-analytics-hierarchy-of-needs-6d57d0e205e2
The First Rule of ML by Eugene Yanhttps://eugeneyan.com/writing/first-rule-of-ml/
ML for Product Analytics by Ron Tidharhttps://www.youtube.com/watch?v=L8D8LEDmLyE&ab_channel=cnvrg.io
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#3-problem-framing
Problem Understanding by Stratechihttps://www.stratechi.com/problem-statement/
How to Define a Product Problemhttps://userpilot.com/blog/product-problems/
Intro to Opportunity Sizinghttps://medium.com/related-works-inc/intro-to-opportunity-sizing-ce9d7e5a29c4
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#4-data-collection
Getting Data From a Databasehttps://medium.com/swlh/searching-through-a-database-52eaaf64f89c
What is SQL - Videohttps://www.youtube.com/watch?v=27axs9dO7AE&ab_channel=DanielleTh%C3%A9
How Data Analysts Use SQL - Videohttps://www.youtube.com/watch?v=GEBzsz8ZSXs&ab_channel=LukeBarousse
Why Are APIs Important For Data Science - Videohttps://www.youtube.com/watch?v=s1gD35Z4eUc&ab_channel=KenJee
What is an API?https://www.freecodecamp.org/news/what-is-an-api-in-english-please-b880a3214a82/
How Companies Caputre Datahttps://kb.narrative.io/how-do-companies-collect-data
How Cookies Workhttps://www.acxiom.com/about-us/privacy/how-cookies-work/#:~:text=What%20Are%20Cookies%2C%20and%20How,some%20useful%20information%20about%20you.
Web Scraping Basicshttps://towardsdatascience.com/web-scraping-basics-82f8b5acd45c
Types of Survey Techniqueshttps://www.questionpro.com/blog/survey-data-collection/
365 Data Science SQL Coursehttps://365datascience.com/courses/sql/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#5-data-preprocessing
All You Need To Know About Different Types Of Missing Data Values And How To Handle Ithttps://www.analyticsvidhya.com/blog/2021/10/handling-missing-value/#:~:text=Types%20Of%20Missing%20Values,Missing%20Not%20At%20Random%20(MNAR)
7 Ways to Handle Missing Values in Machine Learninghttps://towardsdatascience.com/7-ways-to-handle-missing-values-in-machine-learning-1a6326adf79e
Null Values Imputation by Utkarsh Guptahttps://www.kaggle.com/general/248836
What are methods to make a predictive model more robust to outliers?https://www.quora.com/What-are-methods-to-make-a-predictive-model-more-robust-to-outliers
Guidelines for Removing and Handling Outliers in Data by Jim Frosthttps://statisticsbyjim.com/basics/remove-outliers
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#6-exploratory-data-analysis
10 Types of Statistical Data Distribution Modelshttps://www.analyticssteps.com/blogs/10-types-statistical-data-distribution-models
What is EDA?https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15
Is Data Visualization Important for Data Science? - Videohttps://www.youtube.com/watch?v=k8YxyrcAXJs&ab_channel=KenJee
Box Plot Detailshttps://byjus.com/maths/box-plot/
Histogram Additional Detailshttps://towardsdatascience.com/histograms-why-how-431a5cfbfcd5#:~:text=A%20histogram%20provides%20a%20visual,or%20gaps%20in%20the%20data.
Types of Data Distributionshttps://www.analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science/
Understanding Skewhttps://en.wikipedia.org/wiki/Skewness
Scatterplots and Correlation Additional Detailshttps://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/introduction-to-scatterplots/a/scatterplots-and-correlation-review
Types of Correlationshttps://www.ncl.ac.uk/webtemplate/ask-assets/external/maths-resources/statistics/regression-and-correlation/types-of-correlation.html
Correlation Matrix Detailshttps://www.displayr.com/what-is-a-correlation-matrix/
Different correlation matrices in pythonhttps://medium.com/@szabo.bibor/how-to-create-a-seaborn-correlation-heatmap-in-python-834c0686b88e
Creating Pivot Tables Documentationhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html
When to use different types of chartshttps://help.flourish.studio/article/25-line-bar-and-pie-charts#:~:text=Line%20charts%20are%20ideal%20for,or%20axis%20labels%20are%20long.
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#7-feature-engineering
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#categorical-feature-engineering
All about Categorical Variable Encoding by Baijayanta Royhttps://towardsdatascience.com/all-about-categorical-variable-encoding-305f3361fd02
Ordinal and One-Hot Encodings for Categorical Data by Jason Brownleehttps://machinelearningmastery.com/one-hot-encoding-for-categorical-data/
Feature Engineering Ordinal Variables by Sheng Junhttps://towardsdatascience.com/feature-engineering-ordinal-variables-bfea697f5eee
Target Encoding by Ryan Holbrookhttps://www.kaggle.com/code/ryanholbrook/target-encoding
Weight of Evidence Coding by Bruce Lundhttps://www.mwsug.org/proceedings/2016/AA/MWSUG-2016-AA15.pdf
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#continuous-feature-engineering
About Feature Scaling and Normalization by Sebastian Raschkahttps://sebastianraschka.com/Articles/2014_about_feature_scaling.html
Feature Scaling Techniques in Python – A Complete Guide by Eddie_4072https://www.analyticsvidhya.com/blog/2021/05/feature-scaling-techniques-in-python-a-complete-guide/
Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization by Aniruddha Bhandarihttps://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/#:~:text=Normalization%20is%20a%20scaling%20technique,known%20as%20Min%2DMax%20scaling.&text=Here%2C%20Xmax%20and%20Xmin%20are,values%20of%20the%20feature%20respectively.
Robust Scaler - Sklearn Docshttps://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html
Log Transformation: Purpose and Interpretation by Kyaw Saw Htoonhttps://medium.com/@kyawsawhtoon/log-transformation-purpose-and-interpretation-9444b4b049c9
Best exponential transformation to linearize your data with Scipyhttps://towardsdatascience.com/best-exponential-transformation-to-linearize-your-data-with-scipy-cca6110313a6
Exponentially scaling your data in order to zoom in on small differenceshttps://rikunert.com/exponential_scaler
Box Cox Transformation by Ted Hessinghttps://sixsigmastudyguide.com/box-cox-transformation/
Box-Cox Transformation and Target Variable: Explainedhttps://builtin.com/data-science/box-cox-transformation-target-variable
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#8-cross-validation
Understanding 8 types of Cross-Validation by Satyam Kumarhttps://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d
7 Types of Cross Validation by Soumyaa Rawathttps://www.analyticssteps.com/blogs/7-types-cross-validation
k-fold cross-validation explained in plain English by Rukshan Pramodithahttps://towardsdatascience.com/k-fold-cross-validation-explained-in-plain-english-659e33c0bc0
Machine Learning Fundamentals: Bias and Variance by Josh Starmer/Statquesthttps://www.youtube.com/watch?v=EuBBz3bI-aA&t=4s&ab_channel=StatQuestwithJoshStarmer
A Quick Intro to Leave-One-Out Cross-Validation (LOOCV) by Statologyhttps://www.statology.org/leave-one-out-cross-validation/
Time Series Cross Validation by Robert Hyndmanhttps://otexts.com/fpp3/tscv.html
Time Based Cross Validationhttps://towardsdatascience.com/time-based-cross-validation-d259b13d42b8
KFold vs. Monte Carlo by Rebecca Patrohttps://towardsdatascience.com/cross-validation-k-fold-vs-monte-carlo-e54df2fc179b
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#9-feature-selection
Everything You Need to Know About Feature Selection In Machine Learning by Kartik Menonhttps://www.simplilearn.com/tutorials/machine-learning-tutorial/feature-selection-in-machine-learning
A comprehensive guide to Feature Selection using Wrapper methods in Pythonhttps://www.analyticsvidhya.com/blog/2020/10/a-comprehensive-guide-to-feature-selection-using-wrapper-methods-in-python/
What is the difference between filter, wrapper, and embedded methods for feature selection? by Sebastian Raschkahttps://sebastianraschka.com/faq/docs/feature_sele_categories.html
Introduction to Feature Selection methods with an example (or how to select the right variables?)https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/
Feature selection in Python using the Filter method by Renu Khandelwalhttps://towardsdatascience.com/feature-selection-in-python-using-filter-method-7ae5cbc4ee05
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#10-imbalanced-data
10 Techniques to deal with Imbalanced Classes in Machine Learning by Analytics Vidhyahttps://www.analyticsvidhya.com/blog/2020/07/10-techniques-to-deal-with-class-imbalance-in-machine-learning/
Oversampling and Undersampling by Kurtis Pykeshttps://towardsdatascience.com/oversampling-and-undersampling-5e2bbaf56dcf
Overcoming Class Imbalance using SMOTE Techniqueshttps://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/
SMOTE explained for noobs – Synthetic Minority Over-sampling TEchnique line by line by Rich Datahttps://rikunert.com/smote_explained
SMOTE by Joos Korstanjehttps://towardsdatascience.com/smote-fdce2f605729
5 SMOTE Techniques for Oversampling your Imbalance Data by Cornellius Yudha Wijayahttps://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5
Fixing Imbalanced Datasets: An Introduction to ADASYN by Rui Nianhttps://medium.com/@ruinian/an-introduction-to-adasyn-with-code-1383a5ece7aa
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#11-modeling
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#hyperparameter-tuning
What does "baseline" mean in the context of machine learning?https://datascience.stackexchange.com/questions/30912/what-does-baseline-mean-in-the-context-of-machine-learning
Sklearn's Dummy Estimatorshttps://scikit-learn.org/stable/modules/model_evaluation.html#dummy-estimators
7 Hyperparameter Optimization Techniques Every Data Scientist Should Knowhttps://towardsdatascience.com/7-hyperparameter-optimization-techniques-every-data-scientist-should-know-12cdebe713da
A Comprehensive Guide on Hyperparameter Tuning and its Techniqueshttps://www.analyticsvidhya.com/blog/2022/02/a-comprehensive-guide-on-hyperparameter-tuning-and-its-techniques/
Hyperparameter tuning in Python by Tooba Jamalhttps://towardsdatascience.com/hyperparameter-tuning-in-python-21a76794a1f7
Random Search for Hyper-Parameter Optimization by James Bergestra and Yoshua Bengiohttps://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning by Will Koehrsenhttps://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f
Bayesian Optimization Primer by SigOpthttps://static.sigopt.com/b/20a144d208ef255d3b981ce419667ec25d8412e2/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf
Genetic Algorithms by Marcos Del Cuetohttps://towardsdatascience.com/genetic-algorithm-to-optimize-machine-learning-hyperparameters-72bd6e2596fc
Simulated Annealing From Scratch in Python by Jason Brownleehttps://machinelearningmastery.com/simulated-annealing-from-scratch-in-python/#:~:text=Simulated%20Annealing-,Simulated%20Annealing%20is%20a%20stochastic%20global%20search%20optimization%20algorithm.,it%20easier%20to%20work%20with.
Optimization Techniques — Simulated Annealing by Frank Lianghttps://towardsdatascience.com/optimization-techniques-simulated-annealing-d6a4785a1de7
Hyperparameter optimization for Neural Networkshttp://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html#id13
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#ensembling
Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Resultshttps://www.toptal.com/machine-learning/ensemble-methods-machine-learning#:~:text=Ensemble%20methods%20are%20techniques%20that,than%20a%20single%20model%20would
A Gentle Introduction to Ensemble Learning Algorithms by Jason Brownleehttps://machinelearningmastery.com/tour-of-ensemble-learning-algorithms/#:~:text=The%20three%20main%20classes%20of,on%20your%20predictive%20modeling%20project.
Types of Ensemble methods in Machine learning by Anju Tajbangshihttps://towardsdatascience.com/types-of-ensemble-methods-in-machine-learning-4ddaf73879db
Introduction to Ensembling/Stacking in Python by Anisotropichttps://www.kaggle.com/code/arthurtok/introduction-to-ensembling-stacking-in-python
Ensembles and Model Stacking by Eshaan Kirpalhttps://www.kaggle.com/code/eshaan90/ensembles-and-model-stacking/notebook
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#12-model-evaluation
Model Evaluation Metrics in Machine Learning by Nagesh Singh Chauhanhttps://www.kdnuggets.com/2020/05/model-evaluation-metrics-machine-learning.html
11 Important Model Evaluation Metrics for Machine Learning Everyone should knowhttps://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/
How To Interpret R-squared in Regression Analysis by Jim Frosthttps://statisticsbyjim.com/regression/interpret-r-squared-regression/
Know The Best Evaluation Metrics for Your Regression Model by Raghav Agrawalhttps://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/
Recall, Precision, F1, ROC, AUC, and everything by Ofir Shalevhttps://medium.com/swlh/recall-precision-f1-roc-auc-and-everything-542aedf322b9
F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose? by Jakub Czakonhttps://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
Intuition behind Log Loss Score by Gaurav Demblahttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a#:~:text=is%20dependent%20on.-,What%20does%20log%2Dloss%20conceptually%20mean%3F,is%20the%20log%2Dloss%20value.
Why is ROC AUC equivalent to the probability that two randomly-selected samples are correctly ranked?https://stats.stackexchange.com/questions/190216/why-is-roc-auc-equivalent-to-the-probability-that-two-randomly-selected-samples
Man U Whitney Testhttps://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Area-under-curve_(AUC)_statistic_for_ROC_curves
Essential Things You Need to Know About F1-Scorehttps://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Area-under-curve_(AUC)_statistic_for_ROC_curves
ROC, AUC, precision, and recall visually explained by Paul Vanderlakenhttps://paulvanderlaken.com/2019/08/16/roc-auc-precision-and-recall-visually-explained/
R-squared Is Not Valid for Nonlinear Regression by Jim Frosthttps://statisticsbyjim.com/regression/r-squared-invalid-nonlinear-regression/#:~:text=Nonlinear%20regression%20is%20an%20extremely,just%20don%27t%20go%20together.
3 Best metrics to evaluate Regression Model? by Songhao Wuhttps://towardsdatascience.com/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Process_Course#12-model-productionization
Git For Data Scientists - Videohttps://www.youtube.com/watch?v=_0rHU6qAQe0&ab_channel=KenJee
Git Documentationhttps://git-scm.com/doc
Git Basics in 10 minuteshttps://www.freecodecamp.org/news/learn-the-basics-of-git-in-under-10-minutes-da548267cc91/
365 Data Science Git & Github Coursehttps://365datascience.com/courses/git-and-github/
Save and load ml modelshttps://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/
5 Different Ways to Save your ML Modelhttps://towardsdatascience.com/5-different-ways-to-save-your-machine-learning-model-b7996489d433
Improve analytics slide deckshttps://towardsdatascience.com/5-tips-to-improve-your-analytics-slide-decks-c5d0559259c0
Streamlit Galleryhttps://streamlit.io/gallery
Build 12 streamlit apps - Videohttps://www.youtube.com/watch?v=JwSS70SZdyM&ab_channel=freeCodeCamp.org
Streamlit Project Example - Videohttps://www.youtube.com/watch?v=Yk-unX4KnV4&ab_channel=KenJee
Build an api in python - Videohttps://www.youtube.com/watch?v=5ZMpbdK0uqU&ab_channel=Indently
How to create an API in pythonhttps://anderfernandez.com/en/blog/how-to-create-api-python/#:~:text=To%20create%20an%20API%20in%20Python%20with%20Flask%2C%20we%20have,app%20%3D%20Flask()%20%40app.
How to create a python libraryhttps://medium.com/analytics-vidhya/how-to-create-a-python-library-7d5aea80cc3f
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