Need Guidance on Implementing Machine Learning Concepts in Python

Hi everyone,

I’ve recently completed the first course in Andrew Ng’s Machine Learning specialization on Coursera, and I have a solid grasp of the theory. I also have basic Python programming skills.

However, I’m facing difficulties in implementing the concepts in code. I plan to learn key Python packages like NumPy, Pandas, and Matplotlib from YouTube and other sources to improve my coding skills.

Should I:

  1. Focus on learning these packages first?
  2. Revisit the labs and assignments from the first course to implement the concepts in code?
  3. Work on some small projects to solidify my understanding before moving on to the second course in the specialization?

Any advice or suggestions would be greatly appreciated!

Thanks in advance for your help!

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Hi @omunaman

The order you mentioned is good:

  1. Focus on learning NumPy, Pandas, and Matplotlib first. These packages are important for working with data but do not waste too much time on them.

  2. After getting comfortable with the packages, revisit the labs and assignments from the first course. Try to underatand what each piece of code does and then implement them by yourself.

  3. After the first 2 steps, start working on small projects. You can find example projects on GitHub, Kaggle, etc.

Don’t worry about impostor syndrome, everyone feels that way at times. Just keep learning and enjoying the process and if you ever feel stuck feel free to ask us. Good luck!


@Alireza_Saei Thank You, So Much For Your Insight.

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You’re welcome! Happy to help :raised_hands:

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In addition to your self-study, I recommend you attend the 2nd and 3rd courses in the Machine Learning Specialization.

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@TMosh Thank You Sir!! :raised_hands: