#Data Analysis

Explore cutting-edge deep learning with Python-AI's 100 practical examples, from CNNs for image recognition to GANs for image generation. Access executable code and datasets to boost your learning. Connect with a tech-savvy community via WeChat for insights and feedback. Stay informed with new articles and tutorials, published weekly. Ideal for individuals looking to deepen their AI and machine learning expertise.

LAMBDA is an innovative open-source system that simplifies data analysis by removing the need for coding and leveraging advanced model capabilities. The multi-agent framework includes roles such as the programmer and inspector, paired with a user-friendly interface and adaptable model integration for tailored data exploration. Automated report generation enhances focus on critical analysis tasks. Demonstrating strong performance on machine learning datasets, LAMBDA effectively combines human expertise with AI, revolutionizing data science practices. Explore its applications through demonstration videos.

A comprehensive guide of over 500 pages offering practical skills in Machine Learning and Deep Learning using Python. Provides hands-on experience through real-world case studies. Includes frameworks such as TensorFlow, Keras, and scikit-learn for building ML systems. Tailored for professionals aiming to understand data processing and model deployment in various industries.

Streamline Analyst utilizes AI to automate tasks such as data cleaning and model selection, offering efficient and accessible data analysis workflows. It includes features like results visualization, PCA, and balanced modeling with SMOTE and ADASYN. The tool supports classification, clustering, and regression tasks, and maintains data privacy. Future updates will introduce NLP and neural networks, expanding its analytical capabilities.

Fairness Indicators helps objectively assess model fairness for binary and multiclass classifiers with TensorFlow's support. It facilitates data distribution analysis, performance evaluation across user groups, and detailed result slice analysis. Integrating with TensorFlow Data Validation and Model Analysis, plus the What-If Tool, it provides a thorough evaluation framework without overstating capabilities. Explore fairness concerns over time with clear case studies and examples, offering insights for ethical AI integration.

Pybaseball is a Python library designed for in-depth baseball data analysis, enabling efficient data collection from sources like Baseball Reference, Baseball Savant, and FanGraphs. It offers access to detailed Statcast data, pitching and batting statistics, and team standings, available at both individual pitch and seasonal levels. Installation is simple through pip or the repository, and support is available via an active Discord community. Comprehensive documentation provides further guidance on its functionalities.

CSV-AI uses LangChain, OpenAI, and Streamlit to enhance CSV file processing by enabling seamless interaction, summarization, and analysis. Key features involve intuitive navigation, detailed data summarization, and robust analysis with filtering, sorting, and visualization options. Start by cloning the repo, installing dependencies, and using Streamlit to run the app in your browser. The platform values community insights and contributions for ongoing improvements.

Camelot is a Python library that extracts tables from PDFs with high accuracy and customizable settings, outputting to formats like CSV and Excel. Easily integrate with data workflows and install via conda or pip. Comprehensive documentation supports users in achieving precise data extraction.

Discover a rich collection of Jupyter Notebooks and code in Python, HTML5, and D3.JS, tailored for data scientists looking to explore and learn about data collection, preprocessing, analysis, visualization, and narrative techniques. Including insights on sentiment analysis, scikit-learn and PyCaret workflows, and innovative visualization methods with Altair and Plotly, this project offers comprehensive resources available across multiple platforms, backed by detailed documentation and curated tutorials on Medium.

This roadmap serves as a detailed guide for aspiring Full Stack ML engineers, highlighting essential skills such as Python programming, data analysis using libraries like NumPy and Pandas, and data visualization with Matplotlib and Seaborn. It covers statistical methods, practical machine learning applications using Scikit-Learn, and in-depth exploration of deep learning frameworks like TensorFlow and PyTorch. The roadmap also addresses natural language processing and computer vision with OpenCV, and includes insights into MLOps with AWS, as well as essential tools like Git and GitHub for collaboration and version control, providing a solid foundation in machine learning.

This project assembles a wide array of Python examples focusing on algorithms, data analysis, and machine learning, utilizing practical, small-scale cases to foster comprehension. It enables Python learners to explore common operations, modules, object-oriented programming, regular expressions, decorators, iterators, generators, and graphing methods objectively. Serving as a valuable resource, it aids both beginners and experienced programmers in expanding their Python capabilities. With additional tutorial links, enthusiasts can gain further insights into various programming techniques and methodologies.