A Summary of Active Learning Frameworks
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…
python
Geopandas Hands-on: Geospatial Relations and Operations
Part 1: Introduction to geospatial concepts (follow here) Part 2: Geospatial visualization and geometry creation (follow here) Part 3: Geospatial operations (this post) Part 4: Building geospatial…
Five Numpy Functions You Should Understand
How to stack your array horizontally and vertically, find unique values, split your array and some more tips to use Numpy effectively.
Time Series Forecasting with PyCaret Regression Module
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.
Basic Music Theory in ~200 Lines of Python
A basic introduction to Western music theory using the Python programming language to derive scales, chords, and modes in every key.
Use Python to Design Automation Tools for Excel Users
Design of Excel Automation Tools for Sales Analytics ready to be used by your colleagues without any prior knowledge of Python
Extract Tables from PDF file in a single line of Python Code
How to extract and convert tables from PDFs into Pandas Dataframe using Camelot
3 Python Tricks That Will Ease Your Life
Explained with examples
DIY XGBoost library in less than 200 lines of python
XGBoost explained as well as gradient boosting method and HP tuning by building your own gradient boosting library for decision trees.
Using PyTorch + NumPy? You're making a mistake.
A bug that plagues thousands of open-source ML projects.
How to Accelerate Signal Processing in Python
This post is the seventh installment of the series of articles on the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform…
Wicked Fast Python With Itertools
A quick look at an easy way to make Python faster and more effective for machine-learning by using the itertools module.
Data Preprocessing in Python Pandas — Part 6 Dropping Duplicates
A quick tutorial to drop duplicates using the Python Pandas library.
Building a full-text search engine in 150 lines of Python code · Bart de Goede
Full-text search is everywhere. From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like [how to do your job as a software…
Xgboost regression training on CPU and GPU in python
GPU vs CPU training speed comparison for xgboost
11 Pandas Built-in Functions You Should Know
No need to install, import and initialize — Just use them
Conda: essential concepts and tricks
for beginners as well as advanced users
How to use loc and iloc for selecting data in Pandas
Pandas tips and tricks to help you get started with data analysis
7 Must-Know Data Wrangling Operations with Python Pandas
A comprehensive practical guide
10 powerful built-in functions from the Python standard library
Photo by Divide By Zero on Unsplash There are a ton of awesome packages available in the Python ecos...
How to use PyCaret — the library for lazy data scientists
Train, visualize, evaluate, interpret, and deploy models with minimal code.
8 Things to Know to Get Started with With Pandas Groupby
Groupby is so powerful, which may sound daunting to beginners, but you don’t have to know all of its features.
Jupyter: Get ready to ditch the IPython kernel | by Dimitris Poulopoulos |
JupyterLab moves closer to becoming a full-fledged IDE with xeus-python.
Gradient-Free-Optimizers A collection of modern optimization methods in Pyt
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces. - SimonBlanke/Gradient-Free-Optimizers
Are You Still Using Pandas to Process Big Data in 2021?
Pandas doesn’t handle well Big Data. These two libraries do! Which one is better? Faster?
PyCaret — pycaret 2.2.0 documentation
Home - PyCaret
[et_pb_section fb_built=”1″ admin_label=”Header” _builder_version=”4.12.0″ background_color=”#01012C” collapsed=”on” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.12.0″ collapsed=”on” global_colors_info=”{}”][et_pb_column type=”1_2″…
A Complete Guide To Survival Analysis In Python, part 3 - KDnuggets
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/B Testing — A complete guide to statistical testing
Optimizing web marketing strategies through statistical testing
AB_Testing/AB_Testing.ipynb at main · bjpcjp/AB_Testing
A/B Testing — A complete guide to statistical testing - bjpcjp/AB_Testing
Generative Graph Models with NetworkX
A comprehensive guide on standard generative graph approaches with implementation in NetworkX
8 Must-Know File System Operations In Python
The essential for Python in tasks automation apps
Image Processing with Python — Using RG Chromaticity
How to use the Gaussian Distribution for Image Segmentation
Image Processing with Python — Template Matching with Scikit-Image
How to identify similar objects in your image
Image Processing with Python — Blob Detection using Scikit-Image
How to identify and segregate specific blobs in your image
SVM Classifier and RBF Kernel — How to Make Better Models in Python
A complete explanation of the inner workings of Support Vector Machines (SVM) and Radial Basis Function (RBF) kernel
How to Create PDF Reports with Python — The Essential Guide
Create PDF reports with beautiful visualizations in 10 minutes or less.
Python Parallelism: Essential Guide to Speeding up Your Python Code in Minu
Essential guide to multiprocessing with Python.
New Features of Scikit-Learn. An Overview of the Most Important… | by Ankit
An Overview of the Most Important Features in Version 0.24
6 Cool Python Tricks You Should Know | by Soner Yıldırım | Jan, 2021 | Towa
Go beyond the usual
Image Processing with Python — Blurring and Sharpening for Beginners
How do you apply convolution kernels to colored images?
Pylift: A Fast Python Package for Uplift Modeling – Wayfair
Uplift models seek to predict the incremental value attained in response to a treatment. For example, if we want to know the value of showing an advertisement to someone, typical response models will only tell us that a person is likely to purchase after being given an…
Introduction to Image Processing with Python — Dilation and Erosion for Beg
A deeper look into the fundamentals of image dilation and erosion with the use of kernels.
Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps | by Julia Kho
A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to…
KMP Algorithm (String Matching) Demystified
The string matching problem also known as “the needle in a haystack” is one of the classics. This simple problem has a lot of application…
BFGS in a Nutshell: An Introduction to Quasi-Newton Methods
Demystifying the inner workings of BFGS optimization
Top 10 Python libraries of 2020 you should know about | Tryolabs Blog
There are so many amazing Python libraries out there that it's hard to keep track of all of them. That's why we share with you our hand-picked selection of some top libraries.
shutil — High-level file operations
Source code: Lib/shutil.py The shutil module offers a number of high-level operations on files and collections of files. In particular, functions are provided which support file copying and removal...
Quickstart: Create a Python app - Azure App Service | Microsoft Docs
Get started with Azure App Service by deploying your first Python app to Azure App Service.
How to make an animated GIF map in Python using Folium and Imageio
A visual analysis of Brazilian Higher Education history