Guidance : A NewBie trying to make his way

AI Discussions
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A huge HELLO to the WORLD of future talent!
I am a Data Analyst and I wish to transform my career in the field of AI. I have little to no knowledge about the world of AI and I feel overwhelmed with the amount of information on the internet. If you all could help me structure my approach to learning, that would mean a lot and help me move forward.
Currently, I have taken DeepLearning course on course era (called Supervised Machine Learning: Regression and Classification).
I am on week 1 currently, and I would be grateful if anyone could tell me how to go about things, how to study (for maximum productivity) so that I can achieve my end goal i.e. being ABLE to land a job as a data scientist/ML Developer/ ML Engineer etc.

Looking forward to this journey with all the bright minds. :smiley:

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I would take a look at short courses here at DeepLearning.AI, especially

Open Source Models with Hugging Face

https://learn.deeplearning.ai/courses/open-source-models-hugging-face/lesson/1/introduction

Understanding and Applying Text Embeddings

https://learn.deeplearning.ai/courses/google-cloud-vertex-ai/lesson/1/introduction

Building Generative AI Applications with Gradio

https://learn.deeplearning.ai/courses/huggingface-gradio/lesson/1/introduction

Building Applications with Vector Databases

https://learn.deeplearning.ai/courses/building-applications-vector-databases/lesson/1/introduction

after that it should be much easier.

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Thanks a ton! @C0d3-B0dy, do you advice I should start these courses along side the course I am currently doing or after I complete the course I am currently doing?

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Since the courses are short, I would suggest to do it in parallel, or make a pause with your main course and make these short courses before going on. And machine learning and deep learning are kind of two different disciplines in my opinion, what should be considered as well.
The most important thing with the mini courses while working through them, is experimentation and trying out ideas right away you get while working through them. This allows to boost and deepen then understanding.
Wish you successful learning :wink:

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Gotcha! You are suggesting I should learn Deep Learning first and then I should move to Machine Learning is it? Final question I swear :grin:

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The Machine Learning Specialization (MLS) comes first. It is a basic course on machine learning techniques.

The Deep Learning Specialization (DLS) comes next. It is an intermediate course that expands on the basic topics in MLS.

  • Attending MLS after DLS would not be beneficial.

  • Understanding what is happening in DLS is unlikely without attending MLS first.

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Hi mamba824,

I’m currently working on an academic UX project that focuses on how people structure and navigate learning paths in the field of Artificial Intelligence and Machine Learning. I came across your post on the forum, and I really resonated with the challenges you mentioned about getting started in this field and feeling overwhelmed by the information out there.

I’d love to invite you to share more about your experiences and challenges, and perhaps answer a few questions about how you’re approaching your learning journey. Your insights would be incredibly valuable to my research.
Additionally, if you’d like, I can share some suggestions on how to get started with your learning journey and structure your approach. Just let me know if that would be helpful!
If you’re interested, please let me know.

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As others mentioned, there are classic approaches like first ML, then DL and it kind of makes sense in theory. But in fact, there are many cases where you don’t need deep ML understanding to be able to use and apply DL. It is kind of specific to your goals.
I did not mean to generalize on the learning order of ML and DL, but actually doing some AI stuff using DL to get a better feeling how to use it programmatically which will of course benefit ML too. It would allow you to do AI stuff without deep dive of math understanding for example. Which does not mean you will not learn it at all, you will, but at the point when it gets relevant or when you get a more complete understanding around it (this is just an example, since you are data analyst i expect you to know the math already :wink: )
Feel free to ask anything related, this is what the platform has been made for.
And others seem to be interested too.

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Hi @TMosh, thanks for taking out the time to reply. Understood, and after I get a hang of these two, I think I can start NLP?
if I’m not wrong

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Hi @MarcoATL , thank you for writing back. Sure, even though I’ve just started learning, I would love to share my experience so far and pick a little of your brain as well :grin: Looking forward!

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Makes sense @C0d3-B0dy, I understand what you mean. I can even approach both of them simultaneously and in the long term it won’t matter as I would be covering the “left” topics no matter what.
This really helps, thank you! You’ve been really helpful :smiley: Appreciate it

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Yes, that is what I recommend.

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I would like to hear, if it actually worked out for you to approach it this way.
When I started, I really struggled with ML but DL was much easier. But i didn’t have a solid math background, and maybe this was the problem. And my initial approach was actually to learn it completely from the low level and it did not work out too good. So I would really appreciate some feedback after you moved forward in these.

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Perfect, thank you so much :smiley:

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Sure thing! @C0d3-B0dy will keep you updated as I move forward with my learnings. Thank you

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Hi, I hope you’re doing well.

I’m glad you accepted my proposal! First, I’ll explain a bit about the path you can follow. If you already have a solid understanding of university-level mathematics (probability, statistics, and linear algebra), you can jump straight into the Machine Learning course. If not, I recommend starting with the Mathematics for Machine Learning specialization. After completing the Machine Learning course, you can either move on to the Deep Learning specialization or the MLOps course.

Next, it’s important to explore your interests. If you enjoy working with text, creating translators, or analyzing sentiment, I suggest taking the Natural Language Processing (NLP) course followed by TensorFlow. If you’re more interested in analyzing objects and images, start with TensorFlow and then take the AI for Medicine course. If you choose the computer vision path, you might also find the GANs (Generative Adversarial Networks) course interesting, as it focuses on generative AI for images. If you take NLP, you can explore various generative AI courses focused on text, like the LLMs course from AWS or OpenAI’s courses.

I recommend working on a personal project after completing the Machine Learning or Deep Learning courses. You can find interesting datasets on Kaggle and implement an algorithm to predict missing data or classify data points.

Take your time; learning AI is challenging, so give yourself the time you need to absorb the material. Another thing that helped me was looking up YouTube videos when I didn’t understand a specific topic or asking ChatGPT for theoretical explanations.

Now, for my research, I would appreciate it if you could share a bit about yourself, what you do, and the challenges you’ve faced in finding an AI learning path. Specifically, have you looked for guidance on DeepLearning.AI or elsewhere? Would you find a roadmap on DeepLearning.AI for their courses helpful?

Looking forward to hearing from you!

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That makes sense @MarcoATL, I will keep it in mind. Since I am quite familiar with mathematics (university level), I think I will dive straight into ML and DL courses.

Now, talking about my background, I did my B.Tech in Computer Science Engineering, after which I was recruited by an analytics firm where I worked as a data analyst. After working there for a year I switched my job and worked as a Data analyst there. My work mostly revolved around SQL, Power BI, Excel VBA, Python (intermediate level) and Azure Services in both these firms.

The previous year, I started pursuing Msc. in Business Analytics. This in brief is my background. Now coming to the challenges I faced, initially when I decided to pursue my career in AI (1 month ago), the information on the internet was overwhelming to look at. I didn’t know how ML, DL, AI, NLP etc were related to each other. I wanted to know about the basics of these things, for example, what do these term mean, when to use what etc before starting to learn. After researching and talking to people, I got an overall understanding of how these things work together, when to use what for what purpose or application etc. Now, recently I took a course of DeepLearing.ai where I found this community, where I got to know more about this field by mentors such as yourself and many more.
To answer your last question, definitely, it would be really helpful if I get a roadmap on how to go about things and which course to do first etc.

Hope this helps, and if there are any further questions I would be happy to answer them. Thank you :smiley:

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Thank you so much for sharing your experience! I’m currently working on a learning path design in Figma, and I’d love for you to try it out and share your feedback if you’re interested. I’ll reach out to you in 1-2 weeks once it’s ready.

Thanks again!

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My pleasure :D, sure would love to fill it.