Added @pycaret related articles and tutorials to various categories

neomatrix369 · Jan 17, 2022 · 9d17796 · 9d17796
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diff --git a/README-details.md b/README-details.md
@@ -52,6 +52,24 @@
   - [Supercharge your Machine Learning Experiments with PyCaret and Gradio](https://moez-62905.medium.com/supercharge-your-machine-learning-experiments-with-pycaret-and-gradio-5932c61f80d9?utm_medium=social&utm_source=linkedin&utm_campaign=postfity&utm_content=postfityb4ae6)
   - [PyCaret and Streamlit: How to Create and Deploy Data Science Web App](https://towardsdatascience.com/pycaret-and-streamlit-how-to-create-and-deploy-data-science-web-app-273d205271a3?gi=2116c67d324d)
   - [H2O Wave is a software stack for building beautiful, low-latency, realtime, browser-based applications and dashboards entirely in Python without using HTML, Javascript, or CSS](https://www.linkedin.com/posts/philipvollet_python-datascience-gui-activity-6746425913367515136-RTBv)
+  - [Introduction to Regression in Python with PyCaret](https://towardsdatascience.com/introduction-to-regression-in-python-with-pycaret-d6150b540fc4?source=search_post)
+  - [Regression with PyCaret: A better machine learning library](https://towardsdatascience.com/regression-with-pycaret-a-better-machine-learning-library-e02762a0615c?source=search_post)
+  - [Introduction to Binary Classification with PyCaret](https://towardsdatascience.com/introduction-to-binary-classification-with-pycaret-a37b3e89ad8d?source=search_post)
+  - [Classification with PyCaret: A better machine learning library](https://towardsdatascience.com/classification-with-pycaret-a-better-machine-learning-library-cff07a10a28c?source=search_post)
+  - [Predict Customer Churn (the right way) using PyCaret](https://towardsdatascience.com/predict-customer-churn-the-right-way-using-pycaret-8ba6541608ac?source=search_post---------9)
+  - [Build and deploy machine learning web app using PyCaret and Streamlit](https://towardsdatascience.com/build-and-deploy-machine-learning-web-app-using-pycaret-and-streamlit-28883a569104?source=search_post)
+  - [Deploy Machine Learning App built using Streamlit and PyCaret on Google Kubernetes Engine](https://towardsdatascience.com/deploy-machine-learning-app-built-using-streamlit-and-pycaret-on-google-kubernetes-engine-fd7e393d99cb?source=search_post)
+  - [Build with PyCaret, Deploy with FastAPI](https://towardsdatascience.com/build-with-pycaret-deploy-with-fastapi-333c710dc786?source=search_post)
+  - [Easy MLOps with PyCaret + MLflow](https://towardsdatascience.com/easy-mlops-with-pycaret-mlflow-7fbcbf1e38c6?source=search_post)
+  - [Supercharge Your Machine Learning Experiments with PyCaret and Gradio](https://towardsdatascience.com/supercharge-your-machine-learning-experiments-with-pycaret-and-gradio-5932c61f80d9?source=search_post)
+  - [Deploy PyCaret and Streamlit app using AWS Fargate — serverless infrastructure](https://towardsdatascience.com/deploy-pycaret-and-streamlit-app-using-aws-fargate-serverless-infrastructure-8b7d7c0584c2?source=search_post)
+  - [Predict Lead Score (the Right Way) Using PyCaret](https://towardsdatascience.com/predict-lead-score-the-right-way-using-pycaret-332faa780cfc?source=search_post)
+  - [Deploy PyCaret Models on Edge Devices with ONNX Runtime](https://towardsdatascience.com/deploy-pycaret-models-on-edge-devices-with-onnx-runtime-c6d060a2e1a6?source=search_post)
+  - [Deploy Machine Learning Pipeline on cloud using Docker Container](https://towardsdatascience.com/deploy-machine-learning-pipeline-on-cloud-using-docker-container-bec64458dc01?source=search_post)
+  - [GitHub is the best AutoML you will ever need](https://towardsdatascience.com/github-is-the-best-automl-you-will-ever-need-5331f671f105?source=search_post)
+  - [Predicting Spotify Song Popularity](https://towardsdatascience.com/predicting-spotify-song-popularity-49d000f254c7?source=search_post)
+  - [Predict Crash Severity with Machine Learning?](https://medium.com/spatial-data-science/predict-crash-severity-with-machine-learning-dc9848cabcef?source=search_post)
+  - [Pycaret articles on Medium](https://medium.com/search?q=pycaret)
 + Libra • Automates the end-to-end machine learning process in just one line of code: [GitHub](https://lnkd.in/g4kYRnq) | [Notebooks with tutorials](https://lnkd.in/g95uKnR) | [Docs](https://lnkd.in/g_vF72M) | [NLP Queries](https://lnkd.in/gZhufPf) 
 - [GitHub is the best AutoML you will ever need 👇 👇 👇](https://www.linkedin.com/posts/profile-moez_github-is-the-best-automl-you-will-ever-need-activity-6696949164791652352-bleJ)
 - [AutoGOAL: an autoML framework (high & low level) by Alejandro Piad et al.](https://www.linkedin.com/posts/madewithml_machinelearning-artificialintelligence-madewithml-activity-6693165741547626496-mHhS)

diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md
@@ -27,8 +27,9 @@ aka *_Exploratory Data Analysis_*
 - [Pandas is great for most day-to-day data analysis](https://github.com/chiphuyen/just-pandas-things)
 - [The Exploratory Data Analysis (EDA) lesson is out for Made With ML's Applied ML in Production course!](https://www.linkedin.com/posts/goku_exploratory-data-analysis-applied-ml-in-activity-6734809864322871296-FDyJ)
 - [@clone95's repo with studies on Exploratory Data Analysis, Time Series forecasting, and Data Manipulation with popular Python Libraries](https://github.com/clone95/Prices-of-Avocados)
+- [A Complete Data Analysis Workflow in Python PyCaret](https://towardsdatascience.com/a-complete-data-analysis-workflow-in-python-pycaret-9a13c0fa51d4?source=search_post)
 
-
+    
 ### Tools
 
 - [Pandas Profiling](https://pandas-profiling.github.io/pandas-profiling/)
@@ -90,7 +91,8 @@ aka *_Exploratory Data Analysis_*
 - [Disadvantages of KMeans Clustering](https://www.inovex.de/blog/disadvantages-of-k-means-clustering/)
 - Clustering workshop on Uni. of Waterloo Discord channel: [Slideshow](https://bit.ly/3bDy1SW() [Google Doc](https://docs.google.com/presentation/d/1bXvU-IImwZyNGeGhXysX0q-pMrKfG7Esj7ZhhEY2d88/edit#slide=id.gc6d1cf1e32_0_147) |[Notebook](https://bit.ly/3bIaWP3 (https://colab.research.google.com/drive/11Gb-6M8DZNNp04zfyKI3thoZQWm2_1Dk#scrollTo=RqFP72NgNAq7) | [Video](https://youtu.be/127zPeHsFpU)
 - [Key Data Science Algorithms Explained: From k-means to k-medoids clustering](https://www.kdnuggets.com/2020/12/algorithms-explained-k-means-k-medoids-clustering.html#.X-tG4bEBjNg.linkedin)
-
+- [Introduction to Clustering in Python with PyCaret](https://towardsdatascience.com/introduction-to-clustering-in-python-with-pycaret-5d869b9714a3?source=search_post)
+- [Clustering Made Easy with PyCaret](https://towardsdatascience.com/clustering-made-easy-with-pycaret-656316c0b080?source=search_post)
 
 ### Outliers
 

diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md
@@ -96,6 +96,7 @@
   - [PyGLN Gated Linear Network (GLN implementations for NumPy, PyTorch, TensorFlow and JAX: A new family of neural networks introduced by DeepMind](https://www.linkedin.com/posts/philipvollet_machinelearning-neuralnetwork-network-activity-6693036479427563520-AYtm)
   - [Understand the Impact of Learning Rate on Neural Network Performance](https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/)
   - [Neural Networks are Function Approximation Algorithms](https://machinelearningmastery.com/neural-networks-are-function-approximators/)
+  - [PyCaret + SKORCH: Build PyTorch Neural Networks using Minimal Code](https://towardsdatascience.com/pycaret-skorch-build-pytorch-neural-networks-using-minimal-code-57079e197f33?source=search_post)
 
   ## Generative Adversarial Network (GAN)
 

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -187,6 +187,8 @@
 - [Bayesian Analysis](https://projecteuclid.org/euclid.ba/1516093227)
 - [Bayesian Data Analysis](https://www.stat.columbia.edu/~gelman/book/)
 - [Bayesian Basics, Explained](https://www.kdnuggets.com/2016/12/bayesian-basics-explained.html)
+- [Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret](https://medium.com/distributed-computing-with-ray/bayesian-hyperparameter-optimization-with-tune-sklearn-in-pycaret-a33b1592662f?source=search_post)
+
 - Naive Bayesian
     + [Book: Bayes Theorem: A visual intro for Beginners](https://www.amazon.com/Bayes-Theorem-Examples-Introduction-Beginners-ebook/dp/B01LZ1T9IX)
     + [Article: How to become a Bayesian in 8 steps: an annotated reading List](https://link.springer.com/article/10.3758/s13423-017-1317-5)

diff --git a/natural-language-processing/README.md b/natural-language-processing/README.md
@@ -63,7 +63,7 @@ See [Sentiment analysis](./sentiment-analysis.md)
 
 ## Topic modelling
 
-- [Topic Modeling in Python using PyCaret ☟](https://www.linkedin.com/feed/update/urn:li:activity:6768428905800982528/)
+- [Topic Modeling in Python using PyCaret ☟](https://www.linkedin.com/feed/update/urn:li:activity:6768428905800982528/) | [Topic Modeling on PyCaret](https://towardsdatascience.com/topic-modeling-on-pycaret-2ce0c65ba3ff?source=search_post) (Medium post)
 - [Topic modeling helps discover abstract topics](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_machinelearning-datascience-ml-activity-6744246884703059968-DyNX)
 
 ## Presentations

diff --git a/time-series_anomaly-detection/README.md b/time-series_anomaly-detection/README.md
@@ -67,6 +67,10 @@ See [Notebooks under more time-series resources](./time-series.md#notebooks)
   - [AnomalyDetection R package by @Twitter](https://github.com/twitter/AnomalyDetection)
   - [Pycularity - Python port of @Twitter's AnomalyDetection](https://github.com/zrnsm/pyculiarity)
   - [Accurate anomaly detection](https://www.linkedin.com/posts/data-science-central_accurate-anomaly-detection-with-machine-learning-activity-6621433059633958912-2Ccp)
+  - [Introduction to Anomaly Detection in Python with PyCaret](https://towardsdatascience.com/introduction-to-anomaly-detection-in-python-with-pycaret-2fecd7144f87?source=search_post)
+  - [Automated Anomaly Detection Using PyCaret](https://towardsdatascience.com/automated-anomaly-detection-using-pycaret-5e40df75fe36?source=search_post)
+  - [A Simplified approach using PyCaret for Anomaly Detection](https://towardsdatascience.com/a-simplified-approach-using-pycaret-for-anomaly-detection-7d33aca3f066?source=search_post)
+  - [Anomaly Detection Using PyCaret!!!](https://medium.com/@insaid/anomaly-detection-using-pycaret-38b267ed638b?source=search_post)
 
 ### [Tools, libraries, frameworks](#tools,-libraries,-frameworks)
 

diff --git a/time-series_anomaly-detection/time-series.md b/time-series_anomaly-detection/time-series.md
@@ -45,6 +45,14 @@
 - [Presentation: An Introduction to Time Series Forecasting with Python](https://www.researchgate.net/publication/324889271_An_Introduction_to_Time_Series_Forecasting_with_Python)
 - Time Series Forecasting with PyCaret Regression Module: [Medium](https://towardsdatascience.com/time-series-forecasting-with-pycaret-regression-module-237b703a0c63) | [Linkedin](https://www.linkedin.com/posts/profile-moez_time-series-forecasting-with-pycaret-regression-activity-6792794575539879936-Qv6E)
 - [Multiple Time Series Forecasting with PyCaret](https://towardsdatascience.com/multiple-time-series-forecasting-with-pycaret-bc0a779a22fe)
+- [📢 Announcing PyCaret’s New Time Series Module](https://towardsdatascience.com/announcing-pycarets-new-time-series-module-b6e724d4636c?source=search_post---------8)
+- [New Time Series with PyCaret](https://towardsdatascience.com/new-time-series-with-pycaret-4e8ce347556a?source=search_post)
+- [Understanding ARIMA Models using PyCaret’s Time Series Module — Part 1](https://towardsdatascience.com/understanding-arima-models-using-pycarets-time-series-module-part-1-692e10ca02f2?source=search_post)
+- [Understanding ARIMA Models using PyCaret’s Time Series Module — Part2](https://towardsdatascience.com/understanding-arima-models-using-pycarets-time-series-module-part2-308ea7bfecf6?source=search_post)
+- [A Practical Guide to ARIMA Models using PyCaret — Part 3](https://towardsdatascience.com/a-practical-guide-to-arima-models-using-pycaret-part-3-823abb5359a7?source=search_post)
+- [PyCaret Time Series Module Architecture Overview](https://towardsdatascience.com/pycaret-time-series-module-architecture-overview-57336a2f39c7?source=search_post)
+- [Combining PyCaret and TimeMachines for Time-Series Prediction](https://medium.com/@microprediction/combining-pycaret-and-timemachines-for-time-series-prediction-a4d456e47cd9?source=search_post)
+- [Pycaret + Timeseries related articles on Medium](https://medium.com/search?q=pycaret%20timeseries)
 - [One of the strength of Deep Auto-Regressive (DeepAR) models is handling of Multi Time Series in a simple and efficient way](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-ml-machinelearning-activity-6730464577127092224-T8-Q)
 - [Time series analysis discussion on Analytics Vidhya](https://discuss.analyticsvidhya.com/t/time-series-analysis/67474)
 - [In Time series model one has to master lot of concepts Stationary, Trend, Trend Stationary, Additive and Multiplicative seasonality, detrending among others](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-machinelearning-ml-activity-6684306319253626880-R2Wm)