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Willing to build a chatbot, or an app that can identify images, or a tool that can answer questions from a PDF. You know what you want to build. But you have a problem. You’re not sure what software to use, or where to begin.
This is a common case for students entering AI engineering. There are plenty of tools out there, but not all of them are for someone who is just starting. Some require programming knowledge, others are for developers who already know machine learning.
The good news is that you can start with a regular laptop and internet connection. A few free platforms are enough to help you understand the basics and work on your first AI projects.
5 AI Engineering Platforms worth trying
Google Colab: A simple place to write your first AI programme
If you have never worked with Python before, Google Colab is a sensible place to start. You don’t have to install Python or spend time configuring libraries. Open a notebook in your browser, write a few lines of code and run them.
Once you’re comfortable with python, you can explore machine learning on the same platform. Try loading a small data set and training/predicting a simple model. Colab supports most popular libraries like Tensorflow and PyTorch.
The free version is restricted, especially with GPU availability and usage time. Still, those restrictions are unlikely to matter when you are learning the basics.
Kaggle: When you are ready to work with actual data
After learning a little Python, you will probably want to try something beyond simple coding exercises. Kaggle is useful at this point.
Its datasets cover areas like sports, healthcare, finance, education and entertainment. You could choose a dataset you like and try to find patterns in it. You can then use this information to train a machine learning model. For instance, consider house price prediction.
A good starting point would be to look at the relationship between the size, location and price of property. From there you can clean the data and create a model to predict prices. Kaggle also has competitions and user shared notebooks.
It can be helpful to see how someone else does it, especially if you are stuck. The important thing is to understand the solution rather than simply reproduce it.
Hugging Face: For those curious about Generative AI
You have probably used a chatbot or an AI image generator. But what happens when you want to build something similar yourself?
Hugging Face is a good place to explore that question. Developers can use pre-trained AI models in their apps. There are models for different tasks, such as generating text, translating, recognising images, and processing speech.
A good first experiment would be a model that decides if a product review is positive or negative. Once you know how it reacts to different inputs, you can try to use it in a small application.
Hugging Face also has Spaces where developers post interactive AI projects. Browsing these examples can give you ideas for projects of your own.
You don’t need to understand every feature immediately. Start with one model and one problem.
Amazon SageMaker Studio Lab: An introduction to cloud-based ML
At some point, you may want to understand how machine learning projects are developed outside a basic notebook environment. Amazon SageMaker Studio Lab offers a way to experiment with machine learning in the cloud.
It provides a notebook environment with commonly used tools and libraries. You can work with datasets, write Python code and try machine learning experiments without setting up a complete cloud infrastructure account.
This is useful for students who want to become familiar with cloud-based development. However, Studio Lab should not be confused with the full Amazon SageMaker service used for production machine learning workloads.
Microsoft AI for Beginners: An Introduction to the Basics
Not everyone learns best by opening a coding notebook and trying to figure things out. It can be helpful to get the idea first and then try the practical exercise.
That’s the premise behind Microsoft’s AI for Beginners learning resource. It covers topics like neural networks, computer vision, and other AI basics. Lessons include explanations and practical exercises that introduce learners to commonly used frameworks.
You can follow the material at your own pace and run compatible exercises in Google Colab. It is especially helpful if you want to understand what is happening inside an AI model, rather than just learn how to use one.
Which Platform should a Beginner Choose?
It depends on how much you already know. A student who has never written Python code will have different requirements from someone who has completed a few machine learning projects.
Here is a simple way to decide:
| If you want to. | Start with |
| Learn Python and run code | Google Colab |
| Practise with datasets | Kaggle |
| Experiment with existing AI models | Hugging Face |
| Explore machine learning in the cloud | Amazon SageMaker Studio Lab |
| Understand AI concepts systematically | Microsoft AI for Beginners |
You don’t have to use all five. In fact, trying to learn several platforms at the same time can become distracting. Pick one, spend time with it and move on when you have built something you understand.
What can you build as your first AI project?
You don’t need to begin with a complicated AI assistant or a large language model. A small project will be enough to get a feel for how the different parts of AI engineering fit together.
For example you can build a movie review classifier that identifies the positive and negative reviews. Another choice is a simple image classifier that recognises different objects. If Generative AI is something you’re interested in, try building a simple chatbot that can answer questions based on information in a set of documents.
The projects may look simple, but they involve several useful skills: preparing data, choosing a model, testing its output and making improvements.
Place the code in a GitHub repository. Write a short explanation of what you built, what worked, and what you found difficult. That gives you something tangible to show when you’re applying for internships or discussing your skills.
Frequently Asked Questions
Can I learn AI engineering for free in India?
Yes, there are multiple free learning platforms that offers great features. Though some features are usage-limited or on paid plans, but you don’t need a subscription to start learning.
Should AI engineers have the knowledge of Python?
It isn’t the most important language for AI Development, but the most useful. Many machine learning libraries and tutorials use Python.
Can I build AI projects without a GPU laptop?
Yes. Platforms like Colab and Kaggle have some usage limits but offer computing resources. Most beginner projects do not need a powerful personal computer.
Which platform is useful for learning Generative AI?
Hugging Face is the most preferred of all. You can play around with language models and other AI tools before you attempt to build a full application.
How many projects should a beginner build?
There is no fixed number. Start with two or three projects that you can explain properly. You should have a proper understanding of the data model and why a particular decision was made to justify your work.
