Hey everyone! I’m a student currently learning Machine Learning through Stanford’s ML course on Coursera. I’m excited to explore topics like supervised learning, neural networks, and deep learning. Looking forward to learning from this community and working on beginner-friendly projects. Any tips or resources you’d recommend alongside the course?
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Are you attending the original “Stanford ML” course on Coursera? Or are you attending the “Machine Learning Specialization”(MLS)?
The first uses Matlab or Octave. This course is no longer supported. It was originally one 11-week course (but was then re-formatted into “Modules”).
The second uses Python. It is a series of three separate courses.
They cover most of the same topics (though MLS includes some additional methods and tools).
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@ML_Explorer That’s awesome! Stanford’s ML course is a solid foundation. Since you’re interested in supervised learning, neural networks, and deep learning, You can go with the courses
Supervised Machine Learning: Regression and Classification
Neural Networks and Deep Learning
Also you can explore the short courses from Deeplearning.AI
Also, to explore more Practice & Projects: You should start working on Kaggle and Google Colab .
Happy Learning.
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