After Python for Beginners, AI advises the below COURSES, but to first become familiar with NumPy and Scikit-Learn LIBRARIES. My goal is creating production level AI Agents. (There are more courses after). -Mathematics for Machine Learning and Data Science Specialization -Machine Learning Specialization -Deep Learning Specialization

MLS and DLS will cover that you need to know about Numpy, Scikit, and TensorFlow. The MLS and DLS courses do not expect you to have studied any of those tools in detail before you enroll. Math for Machine Learning doesn’t teach much that you will need in either MLS or DLS. It’s more math than machine learning. Since MLS is a beginner level course, it covers all the basics you will need.

Thanks for replying. I lack Linear Algebra, Calculus, Probability knowledge- AI told me the these are needed for MLS, DLS. Not the case, or ???

Also, even if MLS, DLS don’t need the “Math for ML, Data Science” prior- what math knowledge do the MLS, DLS give me? Enough to create quality production level agents? Thanks!

Math skills that MLS expects: How to compute an average. What a standard deviation is. The equation of a straight line. MLS sets you up for attending the Deep Learning Specialization. DLS is where you’ll be introduced to the tech that is used for creating language models - and a whole lot more, including generative methods. If you just want to use existing agentic workflows, that’s a totally different topic. What courses to attend depends on your goals.

Hey, that’s great. You have already taken the step for your goal, and your end goal is building production-level AI agents. Here are some steps you should follow that will be efficient for you. Step 1. Python for Beginners (you have already covered the basics). Step 2. Learn Core Libraries (NumPy, Pandas, Matplotlib, Scikit-Learn). Step 3. Mathematics for Machine Learning and Data Science (linear algebra, calculus, probability and statistics, and Optimization basics). Step 4. Machine Learning Specialization. Step 5. Deep Learning Specialization. Step 6. Build Production-Level AI Agents. Hope this will help you.

[image] Guide for Machine Learning, NLP, Computer Vision - (Roadmap) Feedback AI Discussions Hello everyone, Hello everyone, I hope you’re all doing great! I’ve been working on an academic project for UX design, and as part of it, I had to create a new feature for any website. I thought DeepLearning.AI was a great place to apply my skills and knowledge. I started by focusing on a common problem in the community: “Where should I start? Is there any roadmap for learning AI?” This is a question I’ve seen many people post here on the forum. So, I did some research and analyzed different…

IHello DeepLearning.AI team, I’m currently taking Andrew Ng’s the course Learn Python with AI for Beginners. The content is excellent, but I’m running into a major issue that affects my ability to learn independently. Many of the projects only run inside the provided Jupyter notebook environment. When I try to run them locally, the code breaks due to hidden helper functions, custom utilities, and strict dependency versions that aren’t documented. This makes it difficult to understand how the projects work under the hood or how to build them myself outside the course environment. Because of this, I believe DeepLearning.AI should consider creating a complete Python course that teaches: How to set up real development environments How to build ML projects from scratch How to manage dependencies How to write training loops, data pipelines, and model classes without hidden helper files How to run everything locally, not only in controlled notebooks This would help learners become truly independent and better prepared for real-world machine learning engineering. Thank you for your time, and I hope this feedback helps improve future courses. Best regards, Jacques

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