The Machine Learning for Beginners by Microsoft Developer is an introductory course for classical machine learning. This crash course mainly focuses on regression analysis with Python 🐍, and it covers topics such as:
✅ General setup
✅ Cleaning data
✅ Data visualization
✅ Regression models
✅ Polynomial regression
✅ Logistic regression
Here is a short e-book with a sequence of tutorials on the scientific Python ecosystem for beginners. This includes topics such as:
✅ Working with numerical data using NumPy
✅ Data visualization with Matplotlib
✅ Scientific computing with SciPy
✅ Statistics with Python
✅ Machine learning with scikit-learn
(1/4) TIL about the plotnine library- the grammar of graphics in Python 🚀
I had never heard about the Plotnine library until I came across the Posit Plotnine contest (see the link below). The plotnine is a Python implementation of a grammar of graphics based on the ggplot2 library.
Learn how to handle rows in R containing specific strings using base R's grep() and dplyr's filter() with str_detect(). Select or drop rows efficiently and enhance your data manipulation skills. Give it a try with your datasets for better data cleaning and organization.
College Precalculus – Full Course with Python Code by Ed Pratowski and freeCodeCamp focus on the foundation of calculus with Python implementation. This 12 hours course covers the following topics:
✅ Core trigonometry
✅ Matrix operation
✅ Working with complex numbers
✅ Probability
FreeCodeCamp released today a new course for fine tuning LLM models. The course, by Krish Naik, focuses on different tuning methods such as QLORA, LORA, and Quantization using different models such as Llama2, Gradient, and Google Gemma model.
Here is a great resource for getting started with Observable Framework by Allison Horst. Observable Framework is an open-source JS library for creating dashboards. The sequence of videos covers how to set up a project and data loader, customize the dashboard, and deploy it.
I've been using Quarto for over a year now and I fully endorse it for technical writing, a game changer that can help get your work out there 🚀 If you're new to this space and want to get involved I highly recommend this as a starting point: https://youtu.be/_f3latmOhew?si=VxcMut5INSgK4pyP#DataScience
The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification
I'm looking for consulting work in data science/data analysis. I'm based in Christchurch, New Zealand, but I'm happy to work remote.
I have a background in media & communication, so I'm happy working with clients of any technical ability ^_^ I've worked on projects ranging from surveys of social workers to network analysis, so I'm happy to try anything!
The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models
(1/2) Hands-On Mathematical Optimization with Python 🚀
The Hands-On Mathematical Optimization with Python book by Krzysztof Postek, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor provides the foundation for mathematical optimization. As the name implies, the book is hands-on with Python examples, mainly using Pyomo.
Data Wrangler is a new Microsoft VScode extension for data exploratory analysis. It supports Python 🐍 and Pandas 🐼 DataFrame objects and is integrated into VScode Jupyter Notebooks. Here are some of the functionalities of Data Wrangler:
✅ Data review
✅ Column filtering
✅ Summary statistics
✅ Data cleaning and transformation
✅ Hadeling missing values
✅ Creating new fields