#other #computer_vision #deep_learning #e_books #ebook #machine_learning #math #mathematics #nlp #numpy #opencv #opencv_computer_vision #pandas #pdf #python #reinforcement_learning #scikit_learn #scipy #tensorflow

https://github.com/changwookjun/StudyBook

#jupyter_notebook #jupyter #jupyter_notebook #jupyter_notebooks #jupyter_tutorial #numpy #numpy_arrays #numpy_tutorial #pandas #pandas_dataframe #pandas_tutorial #python #python_pandas #python_tutorial #python_tutorials

https://github.com/codebasics/py

#python #bounding_boxes #computer_vision #data_augmentation #data_visualization #deep_learning #deep_neural_networks #image_processing #k_means #mean_average_precision #numpy #object_detection #pandas #performance_visualization #pretrained_weights #python3 #random_weights #tensorflow2 #train #yolo #yolov3

https://github.com/emadboctorx/yolov3-keras-tf2

#python #datasets #evaluation #metrics #natural_language_processing #nlp #numpy #pandas #pytorch #tensorflow

https://github.com/huggingface/nlp

#jupyter_notebook #data_science #keras #machine_learning #nlp #pandas #portfolio #python #scikit_learn

https://github.com/sajal2692/data-science-portfolio

#jupyter_notebook #data_analysis #exercise #pandas #practice #tutorial

https://github.com/guipsamora/pandas_exercises

#python #dataframe #gui #hacktoberfest #pandas #viewer

https://github.com/adamerose/pandasgui

#python #data_science #exploratory_data_analysis #jupyter #pandas #visualization #visualization_tools

https://github.com/lux-org/lux

#jupyter_notebook #data_analysis #data_science #data_visualization #pandas #python

https://github.com/microsoft/Data-Science-For-Beginners

#python #financial_data #fix_yahoo_finance #market_data #pandas #stock_data #yahoo_finance #yahoo_finance_api

https://github.com/ranaroussi/yfinance

#jupyter_notebook #andrew_ng #andrew_ng_course #andrew_ng_machine_learning #andrewng #coursera #coursera_machine_learning #data_science #deep_learning #deep_neural_networks #dl #machine_learning #ml #neural_network #neural_networks #numpy #pandas #python #pytorch #reinforcement_learning

https://github.com/ashishpatel26/Andrew-NG-Notes

#jupyter_notebook #data_analysis #data_science #data_science_tips #data_visualization #jupyter #jupyter_notebook #jupyter_tips #matplotlib #matplotlib_tips #numpy #pandas #pandas_tips #python #python_tips #sklearn

https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science

#python #data_drift #data_science #hacktoberfest #html_report #jupyter_notebook #machine_learning #machine_learning_operations #mlops #model_monitoring #pandas_dataframe #production_machine_learning

https://github.com/evidentlyai/evidently

#python #data_drift #data_science #data_validation #deep_learning #html_report #jupyter_notebook #machine_learning #ml #mlops #model_monitoring #model_validation #pandas_dataframe #pytorch

https://github.com/deepchecks/deepchecks

#python #bigquery #clickhouse #dask #database #datafusion #duckdb #impala #mssql #mysql #pandas #polars #postgresql #pyarrow #pyspark #snowflake #sql #sqlalchemy #sqlite #trino

https://github.com/ibis-project/ibis

#jupyter_notebook #data_analysis #data_science #data_visualization #pandas #python

This curriculum is designed to help beginners learn data science over 10 weeks with 20 detailed lessons. Each lesson includes pre- and post-lesson quizzes, step-by-step guides, knowledge checks, and assignments to ensure you retain the information. You'll learn about data ethics, statistics, working with different types of data, data visualization, and the entire data science lifecycle. The project-based approach helps you build practical skills while learning. Additionally, there are resources for students and teachers to make the learning process flexible and engaging. This curriculum is beneficial because it provides a structured and interactive way to gain hands-on experience in data science, making it easier to understand and apply these skills in real-world scenarios.

https://github.com/microsoft/Data-Science-For-Beginners

#other #matplotlib #numpy #pandas

The book "利用Python进行数据分析" (Using Python for Data Analysis) has a new third edition with several improvements. It includes updated versions of Python (3.10) and Pandas (1.4.0), adding new methods and features. The book is more user-friendly for beginners, simplifying code readability by avoiding confusing shortcuts. There are also additional resources like video guides, study notes, and online versions available. This makes it easier for users to learn and apply data analysis techniques effectively.

For advanced users, the book "极速Python" (Fast Python) focuses on high-performance techniques for large datasets, covering topics like data structure optimization, high concurrency, and distributed data processing. It integrates technologies like Arrow and Ray, which are crucial for efficient data handling and analysis in modern applications. This helps users handle big data more efficiently and stay updated with the latest technological advancements.

https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version

#python #a_shares #akshare #dataframe #pandas #python #ta_lib #turtle_trade #tushare

This program, called Sequoia, helps you choose stocks using different strategies. It uses data from East Money and allows you to select or combine multiple strategies. Here’s how it benefits you You can choose from various stock selection strategies or even create your own.
- **Easy Setup** You can set up the program to run automatically at scheduled times, and it can even send you notifications via WeChat.
- **Backtesting**: You can test your strategies on historical data by setting a specific end date.

Overall, Sequoia makes it easier to manage and automate your stock selection process.

https://github.com/sngyai/Sequoia

#python #financial_data #fix_yahoo_finance #market_data #pandas #python #stock_data #yahoo_finance #yahoo_finance_api

You can use `yfinance` to easily download financial and market data from Yahoo Finance using Python. This tool is great for research and educational purposes, allowing you to fetch data for single or multiple stock tickers, sectors, industries, and more. It's free, open-source, and easy to install with `pip install yfinance`. However, remember to check Yahoo!'s terms of use to understand your rights to the data. This helps you get valuable market information quickly and efficiently.

https://github.com/ranaroussi/yfinance

#python #ai #csv #data #data_analysis #data_science #data_visualization #database #datalake #gpt_4 #llm #pandas #sql #text_to_sql

PandaAI is a tool that lets you ask questions about your data using natural language. It's helpful for both non-technical and technical users. Non-technical users can interact with data more easily, while technical users can save time and effort. You can load your data, save it as a dataframe, and then ask questions like "Which are the top 5 countries by sales?" or "What is the total sales for the top 3 countries?" PandaAI also allows you to visualize charts and work with multiple datasets. It's easy to install using pip or poetry and can be used in Jupyter notebooks, Streamlit apps, or even a secure Docker sandbox. This makes it simpler and more efficient to analyze your data.

https://github.com/sinaptik-ai/pandas-ai