u/hiddengemsofds

deeplearning.ai and edu.machinelearningplus.com are the best. Go for it.

You've started with the right things, that is getting the programming part and the maths out of the way. The next step probably would be to get into the core ML algorithms followed by Time series, Deep learning and ML Ops for deploying your solutions. Deep learning is a large world by itself make sure to cover nlp, cv and transformer based architectures.

Gen AI is also important, for which langchain, langgraph and/or llamaindex are helpful.

Build good quality industrial projects and have them committed for your profile.

There is much to learn, If you are prepared to do the hard work, only two resources I'd recommend: deeplearining.ai specializations and edu.machinelearningplus.com. While applying what you learn is important, don't neglect the math behind the algorithms. These resources are very good for that. Do more projects oriented to solving industrial problems and start building your profile. All the best!

8-10 hours per week should be good, however there is no hard commitment

edu.machinelearningplus.com - highly recommend.

Assuming your goal is to land a AI/ML Engineer job, you will need to master multiple things starting with Python programming and follow it up with data wrangling. Once you are fairly comfortable with coding, you will need a bit of math for ml which is mainly linear algebra, calculus and probstats. Then you can jump into ml, DL and time series. Don't forget to do quality projects for both practice and for your profile. The complete ds course at edu.machinelearningplus.com is great for this, alternately, check out courses at deeplearning.ai. These two are probably sufficient.

Assuming you know the programming (Python and SQL), ideally the math and stats goes first. But if you are in a hurry, you can go to the ml and come back again or learn them both in parallel.

If you are going in for a self study, Pattern Recognition by Bishop (for ML) or Basic econometrics by Gujarati (for stats) are great choices. Best wishes.

Kirill and Hadelin are great teachers and this is a great course actually. However, If you are going for a paid course, I'd recommend the complete DS course edu.machinelearningplus.com, better structured and depth.

Was not happy with the lessons there, lacking the depth needed for ML.

It's a good book, you might find their github repo particularly resourceful: https://github.com/ageron/handson-ml3

The chances are you have more value to add to the classroom from your 13 years of experience. If you need the academic acknowledgement so bad, then may be yes. I hope you don't have to leave your job for this.

Let me start by saying I’d never heard of machinelearningplus before stumbling on it through a Reddit thread. As someone skeptical of lesser-known platforms, I was surprised by how much I got out of it—but it’s not perfect. Here’s my take:

The Good:

• Structured Learning Path: The course is weirdly organized in a good way. It starts with Python basics, the math, stats, ml algos and slowly builds up to deep learning, it saved me from losing track. I actually finished the assignments instead of abandoning them halfway.
• Instructor’s Teaching Style: Selva (the instructor) has this knack for explaining math without making it feel like a lecture. His whiteboard doodles for concepts like gradient descent stuck with me better
• Real-World Projects: The hands-on tasks—like building recommendation systems, market mix models or NLP scripts—were clutch. I even recycled code from their forecasting module for a work project.
• Supportive Community: When I hit a wall with TensorFlow errors, their response had fixes within hours. Way faster than waiting on forums.

The Not-So-Good:

• Technical Hiccups: Some Google Colab examples glitched out for me. Minor issue, but debugging at midnight was not fun
• Niche Use Cases: While the daily-life examples (e.g., retail, finance) were helpful, I wish they included more niche areas like healthcare or agriculture. Had to adapt projects myself
• Pacing for Absolute Beginners: A friend with zero coding experience tried it and felt lost early on. It’s great for intermediates, but total newbies might need some supplementary resources.

Is it a hidden gem? For $250, yeah—if you’re self-motivated and want practical skills without fluff. But it’s not a magic bullet. You’ll still need to Google things and adapt projects to your field. Compared to pricier platforms like Udacity, though, it’s a steal for the hands-on coding alone

If coding is not your cup of tea, then you dont have to do it as your main work. Product management in AI / Data Science is looking bright, where you don't have to do coding as your prime role.

But I'd still recommend to learn to code. With LLMs generating most of the code, you can get things done faster if you understand the code. If you can think in terms of flowcharts, learning to code is not a lot different, more like speaking a language to implement logic.

Since you already know Java, you need to approach it from a different angle and approach coding for applying on matrices for data wrangling, feature engineering, exploratory analyses, conducting statistical tests, training ml / dl models etc.

Take it in steps, that is learn Python coding first (SQL later), then the Math required for AI such as Linear algebra, calculus, prob and stats. Then get into the ML, DL and Time series modeling, while applying the concepts on good projects. You will have to pick up MLOps as well, which you can do right after picking up ML, or do later after you covered all the concepts (ML and DL), depends on the need.

Hope that helps.

Well, I didnt complete everything, I focussed mostly the algorithms and projects.

Honestly, the Complete Data Science Course by Machine Learning Plus doesn’t get enough credit. It’s super beginner-friendly, hands-on, and covers everything from basics to advanced ML. Definitely worth a look: edu.machinelearningplus.com

Then check https://pytorch-forecasting.readthedocs.io/

Gluonts: https://ts.gluon.ai/stable/

Nixtla: https://www.nixtla.io/

Uber Orbit: https://www.uber.com/en-IN/blog/orbit/

If you wish to learn time series from Industry implementations perspective: https://edu.machinelearningplus.com/s/pages/forecasting-expert

If you are into time series checkout Rob Hyndman's blog and books:

https://otexts.com/fpp3/

https://robjhyndman.com/hyndsight/

The best option today is the complete data science pathway by ML+. https://edu.machinelearningplus.com/s/pages/roadmap

AWS Certifications have some value for ML engineer and MLOps based roles. Other than that just focus on building your profile, maintain your github, contribute to some open source project or publish your own package in PyPi or R CRAN.

Basically build your profile, there is no concept of 'Certified Data Scientist' out there as of 2024.

If you are looking for code completion, Amazon Q and Cursor are worth looking at

You might be interested in this: https://github.com/ImagineAILab/ai-by-hand-excel/ but if you prefer courses, check this out: https://edu.machinelearningplus.com/

I dont think thats the purpose of a roadmap, obv that's very time consuming. But you need to know enough so you can focus deeper on specific areas and solve problems that matter.

what do you recommend?

How about this one? https://edu.machinelearningplus.com/s/pages/roadmap

These two are the best:

deeplearning.ai - Reputed, strong on theory and concepts, Andrew Ng is great but can get dry beyond a certain point.

edu.machinelearningplus.com --> Comprehensive and in-depth, excellent teaching, industry projects, more real but some courses were incomplete.