machine-learning/python
In this article, I will introduce you to 10 little-known Python libraries every data scientist should know.
In this article, I'll take you through a list of 50+ Data Analysis Projects you should try to learn Data Analysis.
In this article, I'll take you through a list of 80+ hands-on Data Science projects you should try to learn everything in Data Science.
In this article, I'll take you through a list of 50+ AI & ML projects solved & explained with Python that you should try.
This is a standalone notebook implementing the popular byte pair encoding (BPE) tokenization algorithm, which is used in models like GPT-2 to GPT-4, Llama 3,...
Popular MLOps Python tools that will make machine learning model deployment a piece of cake.
Learn which variables you should and should not take into account in your model.
Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.
In this article, I'll take you through the task of Market Basket Analysis using Python. Market Basket Analysis using Python.
Understand survival analysis, its use in the industry, and how to apply it in Python
Applying causal machine learning to trim the campaign target audience
Master Sklearn pipelines for effortless and efficient machine learning. Discover the art of building, optimizing, and scaling models with ease. Level up your data preprocessing skills and supercharge your ML workflow today
Create insights from frequent patterns using market basket analysis with Python
Exploring the Latest Enhancements and Features of PyCaret 3.0
A quick guide on how to make clean-looking, interactive Python plots to validate your data and model
Use natural language to test the behavior of your ML models
Discover how to effectively detect multivariate outliers in machine learning with PyOD in Python. Learn to convert anomaly scores to probability confidence, choose the best outlier classifier and determine the right probability threshold for improved model accuracy.
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.
Circular data can present unique challenges when it comes to analysis and modeling
Tips for taking full advantage of this machine learning package
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
Mathematical Modeling, Solution, and Visualization Using PuLP and VeRoViz
How to compress and fit a humongous set of vectors in memory for similarity search with asymmetric distance computation (ADC)
Learn how to build MMMs for different countries the right way
Creating eye-catching graphs with Python to use instead of bar charts.
Graph partitioning has been a long-lasting problem and has a wide range of applications. This post shares the methodology for graph…
Reduce time in your data science workflow with these libraries.
Capturing non-linear advertising saturation and diminishing returns without explicitly transforming media variables
How to forecast with scikit-learn and XGBoost models with sktime
Brain-inspired unsupervised machine learning through competition, cooperation and adaptation
Use linear programming to minimize the difference between required and scheduled resources
The BAIR Blog
Using the Folium Package to Create Stunning Choropleths
How to use Python libraries like Open3D, PyVista, and Vedo for neighborhood analysis of point clouds and meshes through KD-Trees/Octrees
I show toy implementations of Python decorator patterns that may be useful for Data Scientists.
The introduction of the intel sklearn extension. Make your Random Forest even faster than XGBoost.
Apply Louvain’s Algorithm in Python for Community Detection
As a data analyst at Microsoft, I must investigate and understand time-series data every day. Besides looking at some key performance…
Topic modeling can bring NLP to the next level. Here’s how.
Because Graph Analytics is the future
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
A Quick Guide to The Weibull Analysis
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
Easily and efficiently optimize your model’s hyperparameters with Optuna with a mini project
Master usecols, chunksize, parse_dates in pandas read_csv().
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
Streamlit releases v1.0 of its DataOps platform for data science apps to make it easier for data scientists to share code and components.
Hands-on tutorial to effectively use different Regression Algorithms
OpenCV is not the only one
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application.
What companies can learn from employee turnover data
In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
Combining tree-boosting with Gaussian process and mixed effects models - fabsig/GPBoost
Scroll down to see how to interpret a plot created by a great tool for comparing two classes and their corpora.
Word on the street is that PyTorch lightning is a much better version of normal PyTorch. But what could it possibly have that it brought such consensus in our world? Well, it helps researchers scale…
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.
As Data Science continues to grow and develop, it’s only natural for new tools to emerge, especially considering the fact that data…
If you are dealing with a classification task, I recommend the modAL. As for the sequence labeling task, the AlpacaTag is the only choice for you. Active learning could decrease the number of labels…
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.
XGBoost explained as well as gradient boosting method and HP tuning by building your own gradient boosting library for decision trees.
GPU vs CPU training speed comparison for xgboost
for beginners as well as advanced users
Train, visualize, evaluate, interpret, and deploy models with minimal code.
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.
A comprehensive guide on standard generative graph approaches with implementation in NetworkX
How to identify and segregate specific blobs in your image
A complete explanation of the inner workings of Support Vector Machines (SVM) and Radial Basis Function (RBF) kernel
An Overview of the Most Important Features in Version 0.24
Demystifying the inner workings of BFGS optimization
A simple introduction to matching in bipartite graphs with Python code examples
Learn which of the 9 most prominent automatic speech recognition engines is best for your needs, and how to use it in Python programs.
Explained with examples
A step-by-step guide to apply perspective transformation on images
I come from the world of MATLAB and numerical computing, where for loops are shorn and vectors are king. During my PhD at UVM, Professor…
A tour of one of the most popular topic modelling techniques and a guide to implementing and visualising it using pyLDAvis
Python 3.9 New Feature Guide
Overview of the latest developments in version 0.23
Do you know about these packages?
Not enough data for Deep Learning? Try Eigenfaces.
Introduction
Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.
Building up the intuition for how matrices help to solve a system of linear equations and thus regressions problems
Explaining outlier detection with PyCaret library in python
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…
This new Python package accelerates notebook-based machine learning experimentation
Using q-learning for sequential decision making and therefore learning to play a simple game.
I came across Pycaret while I was browsing on a slack for data scientists. It's a versatile library in which you can apply/evaluate/tune…
Learn matrix multiplication for machine learning by following along with Python examples
How does pivot work? What is the main pandas building block? And more …
5 lesser-known pandas tricks that help you be more productive
https://github.com/sepandhaghighi/pycm https://www.pycm.ir custom_rounder function added #279 complement function added sparse_matrix attribute added…
Extract data from different sources
Expedite your data analysis process
Why and How to use with examples of Keras/XGBoost
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 the FeatureSelector for efficient machine learning workflows
Using mlxtend to perform market basket analysis on online retail data set.
An easy-to-use library for recommender systems.
In this article, I'll take you through a list of guided projects to master AI & ML with Python. AI & ML Projects with Python.
Documentation, tutorials and guides for the Gradio ecosystem..