Important Python libraries for machine learning

(A thread)

1. NumPy: This library provides support for large, multi-dimensional arrays and matrices of numerical data, as well as a large collection of mathematical functions to operate on these arrays. www.youtube.com/watch?v=QUT1VHiLmmI

2. Pandas: This library provides tools for data manipulation and analysis, including functions for reading and writing data, handling missing values, and aggregating and summarizing data. www.youtube.com/watch?v=vmEHCJofslg

3. Matplotlib: This library is a powerful tool for data visualization in Python, allowing users to create a wide range of static, animated, and interactive plots and charts.
www.youtube.com/watch?v=UO98lJQ3QGI

4. Scikit-learn: This library is a popular choice for machine learning in Python, providing a range of algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction. www.youtube.com/watch?v=r4bRUvvlaBw

5. TensorFlow: This library is a powerful tool for building and training machine learning models, particularly for deep learning. It provides a flexible and efficient platform for building and training complex models.
www.youtube.com/watch?v=2FmcHiLCwTU