#ScikitLearn

[alaskaenmelena]

Four giant pandas on loan to a zoo in western Japan will return to China before their lease expires

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Machine Learning for Beginners: Build Your First AI Model!

Welcome to your complete beginner's guide to machine learning! Ever wondered how artificial intelligence really works? This video is your first step into that exciting world. No PhD or complex math required – just your curiosity and computer!

In this interactive tutorial, we break down the basics of machine learning, showing you that it's not magic, but smart logic, data, and pattern recognition. Whether you have some Python skills or are an absolute beginner, you'll learn everything you need to create your first AI model. We'll walk you through each step, from understanding supervised, unsupervised, and reinforcement learning to building a real-world model that predicts house prices!

Discover how to handle data, make it useful, and check how well your model performs. We'll use easy-to-understand tools like Google Colab, scikit-learn, pandas, and matplotlib, turning you from a data explorer into a confident model creator. By the end of this course, you won't just understand machine learning for beginners; you'll be able to apply it. Ready to start your AI adventure and build your first AI model? Let's begin!

[damilola-doodles]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[dammyanimation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-ai-automation]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-warrior-mindset]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[damilola-moyo]

Project Title: Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipelinewith Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization - Scikit-Learn-Exercise-008.

#!/usr/bin/env python3 """ Integrated Precision Agriculture Yield Forecasting and Pest Detection Pipeline with Multimodal Data Fusion, Ensemble Learning, and Distributed Optimization Project Reference: ai-ml-ds-AgrYieldXyz File: integrated_precision_agriculture_yield_and_pest_detection_pipeline.py Timestamp:…

[daviddavi09]

Machine Learning Basics: Start Building Models Today #shorts

Welcome to your complete beginner's guide to machine learning — no PhD, no spotless lab, just curiosity, coffee, and your own computer. In this interactive video, we dissect what machine learning actually is: not magic, but reason, data, and pattern recognition. Whether you are a beginner with some Python skills or an absolute beginner, this book takes you through each step — from familiarizing yourself with the basics of supervised, unsupervised, and reinforcement learning to creating your first real-world model predicting house prices. Discover how to import and clean data, engineer features that have real value, and measure the performance of your model with real metrics. We dispel the myth that machine learning is reserved for math whizzes and demonstrate how attitude trumps math. With tools such as Google Colab, scikit-learn, pandas, and matplotlib, you'll be transformed from data sleuth to fearless model creator. By the end of this course, you won't only know machine learning — you'll be applying it. Are you ready to begin your ML adventure? Let's begin!

[nschool]

The Best Open-Source Tools for Data Science in 2025

Data science in 2025 is thriving, driven by a robust ecosystem of open-source tools that empower professionals to extract insights, build predictive models, and deploy data-driven solutions at scale. This year, the landscape is more dynamic than ever, with established favorites and emerging contenders shaping how data scientists work. Here’s an in-depth look at the best open-source tools that are defining data science in 2025.

1. Python: The Universal Language of Data Science

Python remains the cornerstone of data science. Its intuitive syntax, extensive libraries, and active community make it the go-to language for everything from data wrangling to deep learning. Libraries such as NumPy and Pandas streamline numerical computations and data manipulation, while scikit-learn is the gold standard for classical machine learning tasks.

NumPy: Efficient array operations and mathematical functions.

Pandas: Powerful data structures (DataFrames) for cleaning, transforming, and analyzing structured data.

scikit-learn: Comprehensive suite for classification, regression, clustering, and model evaluation.

Python’s popularity is reflected in the 2025 Stack Overflow Developer Survey, with 53% of developers using it for data projects.

2. R and RStudio: Statistical Powerhouses

R continues to shine in academia and industries where statistical rigor is paramount. The RStudio IDE enhances productivity with features for scripting, debugging, and visualization. R’s package ecosystem—especially tidyverse for data manipulation and ggplot2 for visualization—remains unmatched for statistical analysis and custom plotting.

Shiny: Build interactive web applications directly from R.

CRAN: Over 18,000 packages for every conceivable statistical need.

R is favored by 36% of users, especially for advanced analytics and research.

3. Jupyter Notebooks and JupyterLab: Interactive Exploration

Jupyter Notebooks are indispensable for prototyping, sharing, and documenting data science workflows. They support live code (Python, R, Julia, and more), visualizations, and narrative text in a single document. JupyterLab, the next-generation interface, offers enhanced collaboration and modularity.

Over 15 million notebooks hosted as of 2025, with 80% of data analysts using them regularly.

4. Apache Spark: Big Data at Lightning Speed

As data volumes grow, Apache Spark stands out for its ability to process massive datasets rapidly, both in batch and real-time. Spark’s distributed architecture, support for SQL, machine learning (MLlib), and compatibility with Python, R, Scala, and Java make it a staple for big data analytics.

65% increase in Spark adoption since 2023, reflecting its scalability and performance.

5. TensorFlow and PyTorch: Deep Learning Titans

For machine learning and AI, TensorFlow and PyTorch dominate. Both offer flexible APIs for building and training neural networks, with strong community support and integration with cloud platforms.

TensorFlow: Preferred for production-grade models and scalability; used by over 33% of ML professionals.

PyTorch: Valued for its dynamic computation graph and ease of experimentation, especially in research settings.

6. Data Visualization: Plotly, D3.js, and Apache Superset

Effective data storytelling relies on compelling visualizations:

Plotly: Python-based, supports interactive and publication-quality charts; easy for both static and dynamic visualizations.

D3.js: JavaScript library for highly customizable, web-based visualizations; ideal for specialists seeking full control.

Apache Superset: Open-source dashboarding platform for interactive, scalable visual analytics; increasingly adopted for enterprise BI.

Tableau Public, though not fully open-source, is also popular for sharing interactive visualizations with a broad audience.

7. Pandas: The Data Wrangling Workhorse

Pandas remains the backbone of data manipulation in Python, powering up to 90% of data wrangling tasks. Its DataFrame structure simplifies complex operations, making it essential for cleaning, transforming, and analyzing large datasets.

8. Scikit-learn: Machine Learning Made Simple

scikit-learn is the default choice for classical machine learning. Its consistent API, extensive documentation, and wide range of algorithms make it ideal for tasks such as classification, regression, clustering, and model validation.

9. Apache Airflow: Workflow Orchestration

As data pipelines become more complex, Apache Airflow has emerged as the go-to tool for workflow automation and orchestration. Its user-friendly interface and scalability have driven a 35% surge in adoption among data engineers in the past year.

10. MLflow: Model Management and Experiment Tracking

MLflow streamlines the machine learning lifecycle, offering tools for experiment tracking, model packaging, and deployment. Over 60% of ML engineers use MLflow for its integration capabilities and ease of use in production environments.

11. Docker and Kubernetes: Reproducibility and Scalability

Containerization with Docker and orchestration via Kubernetes ensure that data science applications run consistently across environments. These tools are now standard for deploying models and scaling data-driven services in production.

12. Emerging Contenders: Streamlit and More

Streamlit: Rapidly build and deploy interactive data apps with minimal code, gaining popularity for internal dashboards and quick prototypes.

Redash: SQL-based visualization and dashboarding tool, ideal for teams needing quick insights from databases.

Kibana: Real-time data exploration and monitoring, especially for log analytics and anomaly detection.

Conclusion: The Open-Source Advantage in 2025

Open-source tools continue to drive innovation in data science, making advanced analytics accessible, scalable, and collaborative. Mastery of these tools is not just a technical advantage—it’s essential for staying competitive in a rapidly evolving field. Whether you’re a beginner or a seasoned professional, leveraging this ecosystem will unlock new possibilities and accelerate your journey from raw data to actionable insight.

The future of data science is open, and in 2025, these tools are your ticket to building smarter, faster, and more impactful solutions.

[pythonjobsupport]

Kaggle's 30 Days Of ML (Day-13 Part-1): Scikit-Learn Pipelines

This video is a walkthrough of Kaggle’s #30DaysOfML. In this video, we will learn about scikit-learn’s pipelines and use it to … source

[justisntit]

How To Install Scikit-learn In Windows

[interdatavn]

Scikit-learn là gì? Ứng dụng, xu hướng Scikit-learn trong AI/ML

Scikit-learn, với tư cách là thư viện mã nguồn mở mạnh mẽ của Python, cung cấp nhiều công cụ cho học máy (ML). Các mô hình học máy trong Scikit-learn giúp các nhà khoa học dữ liệu giải quyết bài toán phân tích dữ liệu hiệu quả. Hãy cùng tìm hiểu tại sao thư viện này lại được yêu thích và ứng dụng rộng rãi trong ngành dữ liệu.

Scikit-learn là gì?

Scikit-learn (thường được gọi tắt là sklearn) là một thư viện mã nguồn mở miễn phí được phát triển chủ yếu bằng ngôn ngữ lập trình Python phổ biến,  chuyên dùng cho các nhiệm vụ học máy (Machine Learning). Scikit-learn được xem là một trong những công cụ nền tảng và được sử dụng rộng rãi nhất trong cộng đồng khoa học dữ liệu và trí tuệ nhân tạo (AI).

Mục đích cốt lõi của Scikit-learn là cung cấp một bộ công cụ hiệu quả, toàn diện và dễ sử dụng cho các tác vụ phân tích dữ liệu và xây dựng mô hình học máy.

Các phương pháp quyết định thuật toán Scikit-learn, bao gồm:

Phân loại: xác định và phân loại dữ liệu dựa trên các mẫu.

Hồi quy: dự đoán hoặc ước tính giá trị dữ liệu dựa trên giá trị trung bình của dữ liệu hiện tại và dự kiến.

Phân nhóm: tự động nhóm các dữ liệu tương tự vào các bộ dữ liệu.

Các thuật toán hỗ trợ phân tích dự đoán, từ hồi quy tuyến tính đơn giản đến nhận dạng mẫu bằng mạng nơ-ron.

Tính tương thích với các thư viện NumPy, pandas và matplotlib.

Học máy (ML) là một công nghệ cho phép máy tính học từ dữ liệu đầu vào và xây dựng/huấn luyện mô hình dự đoán mà không cần lập trình cụ thể. Học máy là một phần của Trí tuệ nhân tạo (AI).

Xem bài viết tại: Scikit-learn là gì? Ứng dụng, xu hướng Scikit-learn trong AI/ML

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[damilola-doodles]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[dammyanimation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…

[damilola-ai-automation]

Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering - Scikit-Learn-Exercise-007

Photo by Antonio Lorenzana Bermejo on Pexels.com Project Title: ai-ml-ds-KlmNopQrSt – Advanced Urban Traffic Flow Forecasting and Incident Prediction Pipeline with Geospatial, Temporal, and Network Feature Engineering File Name: advanced_urban_traffic_flow_forecasting_and_incident_prediction_pipeline.py This project is an ultra-advanced end-to-end pipeline for predicting urban traffic…