Best of Data Science — April 2025

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    Article
    Avatar of freecodecampfreeCodeCamp·1y

    Essential Machine Learning Concepts Animated

    Understanding AI and machine learning is essential for developers. This visually engaging course on freeCodeCamp.org's YouTube channel by Vladimirs from Turing Time Machine simplifies over 100 core ML and AI concepts with animations and real-world analogies. It covers foundational terms, statistical methods, optimization techniques, evaluation metrics, various model types, practical workflow elements, and related disciplines like NLP and object detection.

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    Video
    Avatar of TechWithTimTech With Tim·1y

    How I'd Learn ML/AI FAST If I Had to Start Over

    Advocates a strategic approach to learning AI and ML swiftly in the rapidly evolving landscape of 2025. Emphasizes the importance of critical thinking and practical coding skills, particularly in Python, for effective AI/ML projects. Encourages data literacy as foundational and promotes hands-on experience with AI models, APIs, and machine learning techniques before transitioning into advanced concepts like LLMs and AI agents.

  3. 3
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    25 Most Important Mathematical Definitions in DS

    A visual presentation of crucial mathematical definitions used in Data Science and Statistics, such as Gradient Descent, Normal Distribution, MLE, Z-score, and SVD. The post explains these terms and their significance in various applications like dimensionality reduction, optimization, and data modeling.

  4. 4
    Article
    Avatar of hnHacker News·1y

    HandsOnLLM/Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book

    The Hands-On Large Language Models repository provides code examples from the book by Jay Alammar and Maarten Grootendorst. The book, known for its visual educational approach with almost 300 custom-made figures, covers practical tools and concepts needed to use Large Language Models. The authors recommend using Google Colab for running examples, but any cloud provider should work. Additional visual guides related to LLMs are also available. The book is a valuable resource for understanding and working with state-of-the-art language models.

  5. 5
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    MCP-powered RAG Over Complex Docs

    Learn how to use MCP to power an RAG application for processing and retrieving information from complex documents. The post details the setup of an MCP server, the creation of GroundX clients and tools, and how to implement these within the Cursor IDE. A comprehensive video walkthrough and a GitHub repository link are also provided for hands-on implementation.

  6. 6
    Video
    Avatar of freecodecampfreeCodeCamp·1y

    Essential Machine Learning and AI Concepts Animated

    Learn essential machine learning and AI concepts in an easy and visual way with this course from Vladimir of Touring Time Machine. Key topics covered include variance, unsupervised learning, time series analysis, transfer learning, gradient descent, logistic regression, and neural networks, among others. The focus is on simplifying complex ideas with animations, avoiding jargon, and making learning accessible and engaging.

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    Article
    Avatar of planetpythonPlanet Python·1y

    Create your first app with Streamlit

    Learn the basics of Streamlit, an open-source Python library for building web apps for machine learning and data science. The guide covers installation, setting up a simple app, and adding interactive features like titles, headings, paragraphs, buttons, sliders, radio buttons, dropdown menus, and a sidebar. It concludes with creating a basic web app that filters the Iris dataset using sidebar controls.

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    Article
    Avatar of motherduckMotherDuck·1y

    Instant SQL is here: Speedrun ad-hoc queries as you type

    Instant SQL is a new feature available in MotherDuck and DuckDB Local UI that provides real-time query result previews as you type, expediting the process of building and debugging SQL queries. This innovative tool is designed to maintain an analytical flow state, allowing for immediate visualization and modification of data which helps reduce the tedious cycle of drafting and debugging queries. Instant SQL supports various data sources and includes AI-powered inline edit suggestions for an enhanced user experience.

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    MCP-powered Agentic RAG

    A demonstration of an MCP-powered Agentic RAG system shows how to use an MCP-driven workflow for searching a vector database and falling back to web search when necessary. The system employs tools such as Qdrant for the vector database, Bright Data for web scraping, and Cursor as the MCP client. The post includes a detailed guide on setting up the MCP server, integrates it with Cursor, and addresses common challenges like IP blocks and bots using Bright Data.

  10. 10
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    NumPy Cheat Sheet for Data Scientists

    This post provides a cheat sheet for data scientists, highlighting the 40 most commonly used methods in NumPy. It emphasizes that mastering every method is unnecessary and suggests applying Pareto’s principle to focus on the most impactful methods.

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    Article
    Avatar of huggingfaceHugging Face·1y

    Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

    AutoRound is Intel's advanced post-training quantization tool for large language and vision-language models, designed to reduce model size and inference latency while maintaining high accuracy. It utilizes signed gradient descent to optimize weight rounding and clipping ranges for low-bit quantization (e.g., INT2 - INT8) with minimal accuracy loss. The tool supports a variety of model architectures and devices, and offers fast quantization processes with just a small calibration dataset needed. AutoRound is compatible with popular export formats and provides flexibility in quantization configurations.

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    11 Most Important DS Plots

    This post provides an overview of the 11 most important and essential plots in data science, highlighting their uses and significance. It covers plots like the KS Plot, SHAP Plot, ROC Curve, Precision-Recall Curve, QQ Plot, and others, explaining their role in data analysis, model performance evaluation, and interpretability.

  13. 13
    Article
    Avatar of planetpythonPlanet Python·1y

    Matplotlib Practice Online: Free Exercises

    Explore a comprehensive set of Matplotlib exercises and practice using an online Matplotlib compiler. Learn how to analyze trends, build machine learning models, and explore datasets with various plot types such as bar charts, scatter plots, histograms, and more. The exercises include practical examples to help you understand and visualize data using Python, making it easier for you to focus on mastering data visualization.

  14. 14
    Article
    Avatar of watercoolerWatercooler·1y

    Algorithms everywhere 🥷

  15. 15
    Article
    Avatar of gopenaiGoPenAI·1y

    ✅ A Beginner’s Guide to Time Series Forecasting in Python

    Time series forecasting is a method to predict future values based on historical data. This guide introduces beginners to the key concepts, popular Python libraries (like ARIMA, Prophet), and real-world applications (finance, weather, healthcare). It also discusses common mistakes to avoid, such as ignoring time dependency and not checking for stationarity.

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