|over 1 year ago||4 days ago|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ESL vs ISLR books?
2 projects | /r/datascience | 12 Oct 2021
Here or here for the Python versions of ISLR.
The Hundred-Page Machine Learning Book
2 projects | news.ycombinator.com | 25 Jan 2021
I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.
In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.
If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.
If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.
If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.
If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.
If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)
For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."
Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.
Simulating sales data
2 projects | /r/datascience | 12 Jun 2023
If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.
I recorded a Data Science Project using Python and uploaded it on Youtube
2 projects | /r/datascienceproject | 1 Jun 2023
Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!
pandas-profiling VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
The Data-Centric AI Community is on Discord 👾
2 projects | dev.to | 20 Dec 2022
Alternatively, if you found DCAI through pandas-profiling or ydata-synthetic you can find support for your troubleshooting and provide feedback on interesting features!
[Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
4 projects | /r/MachineLearning | 3 Dec 2022
Data profiling as part of a data reliability strategy?
2 projects | /r/dataengineering | 15 Sep 2022
GitHub repository with helpful python programs to quickly run through datasets and give a brief summary of it's statistics.
2 projects | /r/datasets | 26 Mar 2022
As a learning project, this is nice, but for standard use, what would be the advantage of this over just loading a program into Pandas and calling df.describe()? And if you need more complete details on a data set, using the pandas-profiling package?
[P] You Only Plot Once (YOPO) -> Simple low code visualization library
2 projects | /r/MachineLearning | 27 Feb 2022
Nice try making it clickable to generate different charts based on loaded data, but I can't help but notice that YOPO's functionality overlaps with another quite big tool called pandas-profiling. It automatically creates report in html or json format to explore dataset and has been used quite successfully in many production solutions.
Visions – User defined data type systems
3 projects | /r/Python | 4 Feb 2022
Visions is a python library for working with user defined data type systems. Out of the box, it provides type inference and automated data cleaning of sequence data with backend specific implementations for pandas, spark, python, and numpy. We often use it as a first pass cleaning step when working with tabular data and to simplify the backend logic of both pandas-profiling and our tabular data compression library compressio.3 projects | /r/Python | 4 Feb 2022
What are some alternatives?
dtale - Visualizer for pandas data structures
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
jupyterlab-lsp - Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol
lux - 👾 Fast and simple video download library and CLI tool written in Go
awesome-python - A curated list of awesome Python frameworks, libraries, software and resources