machine-learning/python
In this article, I will introduce you to 10 little-known Python libraries every data scientist should know.
Popular MLOps Python tools that will make machine learning model deployment a piece of cake.
Exploring the Latest Enhancements and Features of PyCaret 3.0
There are various challenges in MLOps and model sharing, including, security and reproducibility. To tackle these for scikit-learn models, we've developed a new open-source library: skops. In this article, I will walk you through how it works and how to use it with an end-to-end example.
Become familiar with some of the most popular Python libraries available for hyperparameter optimization.
A cross-framework package for kernels and Gaussian processes on manifolds, graphs, and meshes
Python Feature Engineering Cookbook Second Edition, published by Packt - PacktPublishing/Python-Feature-Engineering-Cookbook-Second-Edition
Learn how to build MMMs for different countries the right way
The BAIR Blog
I show toy implementations of Python decorator patterns that may be useful for Data Scientists.
Apply Louvain’s Algorithm in Python for Community Detection
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
Prophet (FB time series prediction package) docs to Python code. - bjpcjp/fb-prophet
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
Here is my take on this cool Python library and why you should give it a try
Dimensionality reduction is a vital tool for data scientists across industries. Here is a guide to getting started with it.
In this first post in a series on how to build a complete machine learning product from scratch, I describe how to setup your project and tooling.
Low-code Machine Learning with a Powerful Python Library
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application.
Combining tree-boosting with Gaussian process and mixed effects models - fabsig/GPBoost
Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces. - SimonBlanke/Gradient-Free-Optimizers
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Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.
Recursive Feature Elimination, or RFE for short, is a popular feature selection algorithm. RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting the target variable. There are two important configuration options when using RFE: the choice…
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
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques - yzhao062/pyod
Recently I’ve started using PyMC3 for Bayesian modelling, and it’s an amazing piece of software! The API only exposes as much of heavy machinery of MCMC as you need — by which I mean, just the pm.sample() method (a.k.a., as Thomas Wiecki puts it, the Magic Inference Button™). This really frees up your mind to think about your data and model, which is really the heart and soul of data science! That being said however, I quickly realized that the water gets very deep very fast: I explored my data set, specified a hierarchical model that made sense to me, hit the Magic Inference Button™, and… uh, what now? I blinked at the angry red warnings the sampler spat out.
Using mlxtend to perform market basket analysis on online retail data set.
An easy-to-use library for recommender systems.
Documentation, tutorials and guides for the Gradio ecosystem..