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AI Explorer
AI Explorer
Apr 24, 2026 · Artificial Intelligence

Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)

This article introduces the open‑source "Dive into LLMs" tutorial (34k+ GitHub stars) that offers a complete, hands‑on workflow for large language models—from fine‑tuning and deployment to prompt engineering, knowledge editing, math reasoning, watermarking, and jailbreak security experiments—along with step‑by‑step Jupyter notebooks and easy setup instructions.

AI securityFine-tuningJupyter Notebook
0 likes · 6 min read
Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)
AI Explorer
AI Explorer
Mar 7, 2026 · Artificial Intelligence

Master Claude Prompt Engineering with Anthropic’s Interactive Tutorial

Anthropic’s open‑source interactive tutorial teaches developers and AI enthusiasts how to craft effective prompts for Claude 3 Haiku, offering hands‑on Jupyter notebooks, a skill‑tree from beginner to advanced, and practical examples that turn prompt engineering from mysticism into systematic practice.

AIAnthropicClaude
0 likes · 6 min read
Master Claude Prompt Engineering with Anthropic’s Interactive Tutorial
Python Programming Learning Circle
Python Programming Learning Circle
Jul 12, 2025 · Fundamentals

Unlock Powerful Python Data Analysis Tricks: Profiling, Interactive Plots, and Jupyter Magic

This article compiles ten practical Python data‑analysis tips, covering Pandas Profiling for quick exploratory reports, Cufflinks‑powered interactive visualisations, essential Jupyter magic commands, debugging shortcuts, and handy code snippets to boost productivity in data‑science workflows.

Jupyter NotebookMagic CommandsProfiling
0 likes · 9 min read
Unlock Powerful Python Data Analysis Tricks: Profiling, Interactive Plots, and Jupyter Magic
Python Programming Learning Circle
Python Programming Learning Circle
Apr 30, 2025 · Artificial Intelligence

Homemade Machine Learning: Python Implementations of Popular Machine Learning Algorithms with Jupyter Notebook Demos

This article introduces the open‑source Homemade Machine Learning project, which implements popular supervised and unsupervised algorithms from first principles in Python, provides Jupyter Notebook demos, code examples, and step‑by‑step setup instructions for learners who want to understand the mathematics and practice the models.

Jupyter NotebookUnsupervised Learninghomemade algorithms
0 likes · 7 min read
Homemade Machine Learning: Python Implementations of Popular Machine Learning Algorithms with Jupyter Notebook Demos
Python Programming Learning Circle
Python Programming Learning Circle
Aug 12, 2024 · Fundamentals

Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables

This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables within Jupyter Notebook, providing code examples, visual demos, and guidance on when to choose each tool for data exploration and lightweight analysis.

Interactive TablesJupyter NotebookPython packages
0 likes · 5 min read
Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables
Python Programming Learning Circle
Python Programming Learning Circle
Jan 16, 2024 · Fundamentals

Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables

This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables and visualizations within Jupyter Notebook, providing code examples, usage tips, and comparisons to help users choose the right tool for data exploration.

Interactive TablesJupyter NotebookPyGWalker
0 likes · 4 min read
Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables
Python Programming Learning Circle
Python Programming Learning Circle
Sep 19, 2023 · Fundamentals

Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables

This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables within Jupyter Notebook, providing features such as pivoting, drag‑and‑drop filtering, visual exploration, and lightweight data inspection for enhanced data analysis.

Data visualizationInteractive TablesJupyter Notebook
0 likes · 5 min read
Interactive DataFrame Visualization in Jupyter Notebook with Pivottablejs, PyGWalker, Qgrid, and Itables
Python Programming Learning Circle
Python Programming Learning Circle
Oct 11, 2022 · Fundamentals

Plotly Overview: Interactive Python Visualizations Made Easy

This article introduces the powerful open‑source Plotly library for Python, showing how a single line of code can create interactive charts such as bar, box, scatter, time‑series, and advanced visualizations, while also covering installation, theme customization, and integration with Jupyter Notebook and Plotly Chart Studio.

CufflinksData visualizationInteractive Charts
0 likes · 8 min read
Plotly Overview: Interactive Python Visualizations Made Easy
MaGe Linux Operations
MaGe Linux Operations
Sep 24, 2022 · Fundamentals

Boost Your Jupyter Notebook Productivity with 5 Essential Extensions

This guide walks you through installing Jupyter Notebook extensions, explains why they improve workflow, and highlights five must‑have add‑ons—including Table of Contents, Autopep8, Variable Inspector, ExecuteTime, and Hide Code—to streamline data‑science tasks.

Data ScienceExtensionsJupyter Notebook
0 likes · 7 min read
Boost Your Jupyter Notebook Productivity with 5 Essential Extensions
Model Perspective
Model Perspective
Sep 18, 2022 · Fundamentals

How to Open and Use Jupyter Notebook Files

This guide explains how to launch Jupyter Notebook, open existing notebook files, and begin working with them, providing clear step‑by‑step instructions for users new to the interactive computing environment, including tips on navigating the interface and saving your work.

Data ScienceJupyter NotebookPython
0 likes · 1 min read
How to Open and Use Jupyter Notebook Files
Python Programming Learning Circle
Python Programming Learning Circle
Aug 25, 2022 · Fundamentals

Plotly Basics: Offline Plotting, Traces, Layout, and Customization in Jupyter Notebook

This article introduces Plotly's offline plotting in Jupyter Notebook, explains the key parameters of plotly.offline.iplot, demonstrates how to create and combine traces, and shows how to customize layout elements such as fonts, titles, axes, legends, grids, and other figure properties using Python code.

Data visualizationJupyter NotebookOffline Plotting
0 likes · 15 min read
Plotly Basics: Offline Plotting, Traces, Layout, and Customization in Jupyter Notebook
Python Programming Learning Circle
Python Programming Learning Circle
Aug 10, 2022 · Fundamentals

Python and Jupyter Notebook Tips: Data Profiling, Interactive Plotting, and Handy Magic Commands

This article presents a collection of practical Python and Jupyter Notebook tips—including pandas‑profiling for quick data exploration, interactive plotting with Cufflinks/Plotly, useful magic commands, debugging shortcuts, and styled alert boxes—to boost productivity and streamline data analysis workflows.

DebuggingJupyter NotebookMagic Commands
0 likes · 9 min read
Python and Jupyter Notebook Tips: Data Profiling, Interactive Plotting, and Handy Magic Commands
Python Crawling & Data Mining
Python Crawling & Data Mining
Mar 27, 2022 · Big Data

Master Pandas in Python: Install, Explore, Analyze, and Visualize Data Quickly

This article introduces Python and the Pandas library, walks through installation, loading Excel data, core DataFrame operations such as selection, sorting, grouping, aggregation, transformation, adding columns, statistical analysis, visualization with Matplotlib, and finally exporting results, providing a comprehensive beginner‑to‑intermediate guide.

Jupyter Notebook
0 likes · 17 min read
Master Pandas in Python: Install, Explore, Analyze, and Visualize Data Quickly
Python Crawling & Data Mining
Python Crawling & Data Mining
Nov 11, 2021 · Fundamentals

Master Jupyter Notebook: Tips, Extensions, and Pro Shortcuts

This comprehensive guide walks you through Jupyter Notebook’s web‑based interactive computing features, major users, installation via Anaconda, launching, dual command/edit modes, core functionalities, useful extensions, keyboard shortcuts, magic commands, and theme customization, providing practical code snippets and visual examples for Python developers.

AnacondaData ScienceExtensions
0 likes · 13 min read
Master Jupyter Notebook: Tips, Extensions, and Pro Shortcuts
Python Programming Learning Circle
Python Programming Learning Circle
Sep 19, 2021 · Big Data

Visualizing Historical National Games Medal Rankings with Python

This tutorial demonstrates how to collect, clean, and visualize historical Chinese National Games medal data using Python, covering data extraction with regular expressions, organization into pandas DataFrames, and creating both static line charts with Matplotlib and interactive charts with PyEcharts, complete with code snippets and practical tips.

Data visualizationJupyter NotebookMatplotlib
0 likes · 6 min read
Visualizing Historical National Games Medal Rankings with Python
Python Programming Learning Circle
Python Programming Learning Circle
Jul 31, 2021 · Fundamentals

Using Plotly and Cufflinks for Powerful Interactive Data Visualizations in Python

This article introduces the open‑source Plotly library (with Cufflinks) for Python, explains how to install it, and demonstrates with concise code examples how to create a wide range of interactive charts—including bar, box, scatter, time‑series, 3D, and custom‑themed visualizations—directly in Jupyter notebooks.

CufflinksData visualizationInteractive Charts
0 likes · 8 min read
Using Plotly and Cufflinks for Powerful Interactive Data Visualizations in Python
Python Programming Learning Circle
Python Programming Learning Circle
Jul 26, 2021 · Fundamentals

Step-by-Step Guide to Data Analysis with Python: Import, Clean, Visualize, and Merge Using Pandas

This tutorial walks data analysts through setting up a Python environment with Jupyter and Anaconda, importing diverse datasets via Pandas, cleaning and reshaping data, performing calculations, filtering, visualizing results, and finally merging and grouping data to produce insightful analyses comparable to SQL and Excel workflows.

Data visualizationExcelJupyter Notebook
0 likes · 12 min read
Step-by-Step Guide to Data Analysis with Python: Import, Clean, Visualize, and Merge Using Pandas
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 24, 2021 · Fundamentals

Master Pandas: A Step‑by‑Step Guide to Data Analysis with Python

This comprehensive tutorial introduces Pandas—the powerful Python library for data manipulation and analysis—covers installation, data import, inspection, cleaning, indexing, selection, sorting, grouping, transformation, statistical functions, visualization, and exporting, all illustrated with clear code examples and visual outputs.

Data ScienceJupyter NotebookPython
0 likes · 18 min read
Master Pandas: A Step‑by‑Step Guide to Data Analysis with Python
MaGe Linux Operations
MaGe Linux Operations
Jun 25, 2021 · Fundamentals

12 Must‑Know NumPy & Pandas Functions to Supercharge Your Data Analysis

This article introduces twelve powerful NumPy and Pandas functions—six for each library—explaining their purpose, usage, and providing code snippets, enabling readers to perform efficient array manipulation, data filtering, aggregation, and I/O operations, with a link to the full Jupyter Notebook on GitHub.

Jupyter NotebookPythondata-analysis
0 likes · 11 min read
12 Must‑Know NumPy & Pandas Functions to Supercharge Your Data Analysis
Python Programming Learning Circle
Python Programming Learning Circle
May 10, 2021 · Fundamentals

A Minimalist Guide to Jupyter Notebook Extensions and the Top Five Useful Plugins

This article explains how to install Jupyter Notebook extensions, walks through a quick and a detailed tutorial, and reviews the five most popular extensions—Table of Contents, Autopep8, Variable Inspector, ExecuteTime, and Hide Code Input—highlighting their benefits for data‑science workflows.

ExtensionsJupyter Notebookproductivity
0 likes · 6 min read
A Minimalist Guide to Jupyter Notebook Extensions and the Top Five Useful Plugins
MaGe Linux Operations
MaGe Linux Operations
Apr 16, 2021 · Fundamentals

Master Data Analysis with Python: From Excel/SQL to Pandas in 10 Steps

This tutorial walks data analysts through transitioning from Excel and SQL to Python, covering environment setup, data import with pandas, web scraping, cleaning, renaming, type conversion, filtering, grouping, merging, and visualization using Jupyter Notebook and popular libraries.

Data visualizationJupyter NotebookPython
0 likes · 13 min read
Master Data Analysis with Python: From Excel/SQL to Pandas in 10 Steps
Python Programming Learning Circle
Python Programming Learning Circle
Apr 1, 2021 · Fundamentals

Building a Hangman Word‑Guessing Game in Python

This tutorial walks through the complete design and implementation of a Hangman word‑guessing game in Python, covering library imports, variable declarations, hidden‑word generation, the main game loop, input handling, guess validation, life‑count management, repeated‑guess detection, win/lose conditions, and output clearing within a Jupyter Notebook environment.

Game DevelopmentJupyter NotebookLists
0 likes · 12 min read
Building a Hangman Word‑Guessing Game in Python
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 26, 2020 · Fundamentals

Master Jupyter Notebook: Change Directory, Browser, Password, and Themes

This guide walks Jupyter Notebook beginners through essential configuration steps—including altering the default working directory, setting a preferred browser, securing the notebook with a password, installing useful extensions, and customizing the notebook’s appearance—empowering a smoother, more personalized Python workflow.

ConfigurationEnvironment setupExtensions
0 likes · 8 min read
Master Jupyter Notebook: Change Directory, Browser, Password, and Themes
MaGe Linux Operations
MaGe Linux Operations
Mar 19, 2020 · Fundamentals

Top Python Tools to Boost Your Development Efficiency

Discover a curated list of essential Python tools—including Python Tutor, IPython, Jupyter Notebook, Anaconda, and Skulpt—that streamline learning, interactive coding, data analysis, and environment management, helping developers work smarter and faster.

AnacondaJupyter Notebookdevelopment-tools
0 likes · 4 min read
Top Python Tools to Boost Your Development Efficiency
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 20, 2019 · Fundamentals

Master Jupyter Notebook: A Step‑by‑Step Data Analysis Guide for Beginners

Learn how to install Jupyter via Anaconda or pip, create and manage notebooks, understand cells and kernels, write and run Python code, explore a Fortune 500 dataset with pandas, clean missing values, and visualize profit and revenue trends using matplotlib and seaborn—all illustrated with screenshots and code snippets.

Jupyter NotebookMatplotlibPython
0 likes · 15 min read
Master Jupyter Notebook: A Step‑by‑Step Data Analysis Guide for Beginners
ITPUB
ITPUB
Jun 15, 2018 · Fundamentals

Can Python Predict the 2018 World Cup Champion? A Data‑Driven Analysis

Using a Kaggle dataset of over 40,000 matches from 1872 to 2018, this notebook demonstrates how to clean, transform, and visualize World Cup data with Python, pandas, and Matplotlib to identify top‑winning teams, total goal statistics, and forecast the most likely 2018 champion.

Jupyter NotebookPredictiondata-analysis
0 likes · 11 min read
Can Python Predict the 2018 World Cup Champion? A Data‑Driven Analysis
ITPUB
ITPUB
Jun 15, 2018 · Fundamentals

Can Python Predict the 2018 World Cup Champion? A Data‑Driven Exploration

This article walks through a Python‑based data analysis of World Cup matches from 1872 to 2018, using pandas and Jupyter Notebook to clean the data, compute win counts and total goals, visualize the top teams, and finally predict that Germany, Argentina and Brazil are the strongest contenders for the 2018 title.

Jupyter Notebookdata-analysispandas
0 likes · 11 min read
Can Python Predict the 2018 World Cup Champion? A Data‑Driven Exploration