KIndly Guide Me

I am a new student of Machine Learning course i have prior knowledge of Python but not any of its libraries kindly suggest me which libraries and other stuff should i learn and from where to do this better because i am unable to understand course programming labs. Thanks in advance

Please provide details about the course / assignment you’re referring to.
Here’s the community user guide to get started on using this forum.


Machine Learning Course

1 Like

Adding @rmwkwok, @gent.spah, @saifkhanengr, @TMosh


If you have knowledge, understanding of python and know how to get around it, then you should be able to successfully complete that course. The libraries used will be learned as part of the course, no need to worry about it, but if you lack in python then you should take some online course about it and there are plenty in coursera.


Means we will learn matplotlib and other libraries later . Thanks

Hi @Talha_Rashid

Welcome to the community.

Congratulations on starting your journey in Machine Learning! Python is an excellent language for ML, and there are several libraries that are commonly used in the field. Here’s a list of essential libraries and resources to get you started:

  • NumPy: This fundamental library provides support for large, multi-dimensional arrays and matrices, along with an extensive collection of mathematical functions to operate on these arrays. It’s the foundation for many other libraries in Python’s data ecosystem.

  • Pandas: Pandas is a powerful library for data manipulation and analysis. It offers data structures like DataFrames that make handling structured data much easier.

  • Matplotlib and Seaborn: These libraries are essential for data visualization. Matplotlib is a versatile plotting library, while Seaborn builds on top of Matplotlib and provides an interface for creating attractive statistical graphics.

  • Scikit-learn: This is a widely-used library for machine learning tasks. It provides a simple and efficient API for various ML algorithms like classification, regression, clustering, and more.

  • TensorFlow or PyTorch: These are popular deep learning libraries used to build and train neural networks. Choose one of them to get started with deep learning.

  • Jupyter Notebooks: Jupyter Notebooks allow you to create interactive and shareable code notebooks. They are great for experimenting, visualizing data, and documenting your ML projects.

Remember, learning ML takes time and patience. Don’t get discouraged by challenges; keep practicing and seeking help when needed. Best of luck with your learning journey!

Count on us to help with your journey!

Best regards