I am learning AI/ML engineering along with full stack web development internship

Unlike to programming, carrier questions are off-topic on Stack Overflow. The same is about "How to learn" questions.

You can start by doing some basic python projects if not proficient in python.
Or if you are proficient in python then you can straight up start with basics like classification and regression type models.
I suggest you start with diabetes classification using a csv dataset to understand how pandas,numpy and scikit-learn are used to train and predict models.
For regression you can start with something like the house price prediction which is also teaches common basic things which are needed.

You could start with basic python concepts, like, variables, loops, data types, oop, algorithims, etc

then practice with simple projects like a tic-tac-toe game, calculator, ai chatbot using api, password generator, etc

Now, coming to data analytics,

learn the basics of numpy and pandas and do these

https://machinelearningplus.com/python/101-numpy-exercises-python/

deep learning and https://github.com/ashishpatel26/Andrew-NG-Notes

now, after you did all this

There is a computer science course called CS50, taught by Harvard, that course will take you from here

Its a 3 month course (approx)

https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python

You can read the details on the link and you can enroll in it, if conviced

after the course, you can build a Image Classifier using opencv in python, and build an ai calorie tracker web app using this open source ai model

https://huggingface.co/ardaocak/llava-1.5-7b-food-calorie-estimator
https://huggingface.co/datasets/aryachakraborty/FoodCalorieDataset

Now for the technologies,

Tensorflow or Pytorch
Numpy, Pandas
C++, trust me, if you planning to build ai projects with embedded systems, this will help you

I just told a roadmap that will take hours off the internship for you, but computer science is all about dedication and practice.