Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit Learn - Episode 07

Thanks for watching, turn on notificatREACTions (🔔) to receive every new video 😍 🔰 MY INSTAGRAM 🔰   / med.max.technology   © Copyright by MED MAX TECHNOLOGY You can learn about NumPy, SciPy, Matplotlib, Pandas, and scikit-learn from a variety of online resources, including tutorials, documentation, courses, and books. Here are some recommended platforms and sources to help you get started: 1. Official Documentation: NumPy: The official NumPy documentation (https://numpy.org/doc/) provides comprehensive information about the library, including tutorials and examples. SciPy: The SciPy documentation (https://docs.scipy.org/doc/scipy/refe...) covers various scientific computing topics and includes tutorials and examples. Matplotlib: The Matplotlib documentation (https://matplotlib.org/stable/content...) offers detailed guides for creating visualizations using Matplotlib. Pandas: The Pandas documentation (https://pandas.pydata.org/docs/) is a rich resource for learning about data manipulation and analysis using Pandas. scikit-learn: The scikit-learn documentation (https://scikit-learn.org/stable/docum...) includes tutorials and user guides for machine learning with scikit-learn. 2. Online Tutorials: NumPy: You can find introductory tutorials on NumPy on platforms like GeeksforGeeks, Real Python, and DataCamp. Pandas: Similar to NumPy, Pandas tutorials are available on platforms like GeeksforGeeks, Real Python, and DataCamp. Matplotlib: Data visualization tutorials using Matplotlib can be found on websites like Real Python, Towards Data Science, and DataCamp. scikit-learn: Various tutorials on machine learning with scikit-learn are available on platforms like Kaggle, Analytics Vidhya, and Machine Learning Mastery. 3. Online Courses: Platforms like Coursera, Udemy, edX, and LinkedIn Learning offer comprehensive courses on each of these libraries. Look for courses like "Data Analysis with Python" or "Machine Learning with scikit-learn" to get started. 4. Books: Many books cover these libraries in-depth. For example, "Python for Data Analysis" by Wes McKinney is an excellent resource for learning Pandas, and "Introduction to Machine Learning with Python" by Andreas Müller and Sarah Guido covers scikit-learn. 5. Interactive Platforms: Websites like Kaggle and DataCamp offer interactive coding exercises and projects that help you learn by doing. They have specific courses on these libraries. 6. YouTube Channels and Video Tutorials: YouTube hosts a wide range of tutorials and video lectures on these libraries. Channels like "sentdex" and "Corey Schafer" offer tutorials on Python libraries, including the ones you mentioned. Remember, learning these libraries involves both theoretical understanding and hands-on practice. Start with basic concepts and gradually work your way up to more advanced topics. Don't hesitate to experiment with code and build projects to solidify your learning. © Copyright by MED MAX TECHNOLOGY YT Channel ☞ Do not Reup . Thanks !!! 🔥