Moving forward from Machine Learning Specialization!

Hey Learners,
If you are checking this thread out, then I am assuming, you are looking for some suggestions and advice as to either how to improve upon your AI/ML concepts or how to write better code or both.

The first track requires learning theoretical foundations including concepts, ideas and algorithms and the second track requires implementing all these in a programming language of your choice, perhaps Python (which is considered in the Machine Learning Specialization). Some learners prefer to advance in these 2 tracks side-by-side while some learners tend to prefer advancing in 1 track first, followed by another. But don’t worry, the suggestions here will work all the same for you :nerd_face:

Theoretical Foundations

  • J.K. Rowling (author of the much-loved series Harry Potter) very beautifully said that “If you don’t like to read, you haven’t found the right book”. That being said, books are one of the best sources for building upon theoretical foundations. You can find some great references in the below threads:

  • Remember that no single book works for all, so find the one that works for you. Perhaps you may find it from the above threads, and if not, then you have the entire Internet with you. So, go and find your own match!

  • Another great way is to read articles & blogs. Most of the brilliant researchers in the field of AI love to write blogs and disseminate what they have learnt over their decades of experience. There are also some great communities which single-handedly focus on AI. Here are some great references for you:

  • Another great resource could be newsletters. They will help you stay up-to-date with the recent breakthroughs in the field of AI. Here are some great ones for you:

  • Participating in forums’ discussions and helping out your fellow learners can also help you to advance your own understanding of the core aspects of AI. Kaggle Discussions and DeepLearning.AI’s Discourse are 2 such forums that I am aware of.

  • Pursuing courses & specializations are another great way to advance your understanding of the theoretical foundations. Some of these tend to push you in the second track as well alongside the first track. There are some great platforms where you can pursue these courses. Here are some great references for you:

Programming Skills

  • Undoubtedly, building projects is perhaps one of the best ways in which you can improve upon your programming skills, write better code, curate better documentations, etc. Andrew has talked about project work in this issue of the Batch. Do check it out!

  • However, it’s quite common to not have an idea and still wanting to contribute to a project. Open-source projects are a great way to go in this case. You can find many such projects on Github.

  • You could also contribute to some of the most widely used libraries & frameworks in AI, such as Numpy, Pandas, Matplotlib, Tensorflow, PyTorch, etc. All of these accept open-source contributions, and they will expose you to “behind-the-scenes” of these libraries and frameworks.

  • If you want to start small, Kaggle competitions are another great way to go. You can check them out here. A kaggle competition will push you to expand your horizon to deal with some of the foremost problems in the field of AI. They also consist of a dedicated forum in each of the competition for healthy discussions regarding the problem at hand.

  • And last, but not the least, you can implement research papers that don’t have any public implementations available yet. You can find such research papers here.

Conclusion

I hope this post has helped you to decide your next steps in the field of AI. But if you still feel confused, feel free to reach out to the DeepLearning.AI team, and we will make sure to help you push your boundaries further! Happy Learning!

Cheers,
Elemento

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