AI Power Internship Program: Learn Artificial Intelligence Through Practical Projects

By Yuvraj Nimankar | Updated 10 October 2026

We are not connected to Saiket Systems or any government body, university or training provider.

Saiket Systems runs unpaid, task-based internships, and its official page lists AI-related work under a Machine Learning track. No track called “AI Power” appears there. This post covers what the page does say, how to judge AI tasks once you receive them, and what to ask the provider before you apply. The syllabus is not published, so the first question is which kind of AI the tasks cover.

Enrolment status

Unclear as of 10 October 2026. The official internships page (last modified 4 October 2026) shows no program start date or application deadline for any track, and it lists no track named “AI Power.” The closest listing is Machine Learning. Confirm the exact program name and the dates in writing from the provider before planning around any date you have seen elsewhere.

Quick overview

The two date rows are separate on purpose. The start date is when tasks begin, and the deadline is the last day the application form accepts submissions. Do not treat them as one date. Level, duration and eligibility come from the provider’s own wording, which is broad. Treat that wording as a claim, not as a prerequisite list.

Who is this course for?

It may suit:

• Computer science, IT and engineering students who have attended AI lectures but never shipped a working project.
• Graduates from non-technical fields who want to test whether AI work suits them before paying for a course.
• Freshers with some Python who can follow tutorials but stall when no tutorial exists for the problem.
• Learners who want someone to check whether their model results are real or accidental.

Skip it if you need a stipend, a placement route or a qualification an employer can formally verify. Skip it too if you have never written code and do not plan to learn basic Python alongside the tasks.

What you will learn

For the Machine Learning listing, the provider says only that you learn machine learning concepts through practical projects and “industry-focused AI training.” There are no modules, tools or hours, so the useful skill is judging what each task trains.

Five areas that “AI” usually covers

This is our own map, not the provider’s curriculum. Knowing the area tells you what a task builds and what it leaves out.

The official listing is for Machine Learning, so expect the first row to dominate. If your tasks sit in one row, you will learn that row well and the others not at all. That is fine as long as you know it.

Using a model is different from building one

Many “AI projects” online connect to an existing model with a few lines of code. That is a legitimate skill, but it teaches software integration, not how models learn. For each task, ask which of the two you are doing. A strong portfolio shows at least one project where you trained something yourself and measured how well it worked.

What a good AI task looks like

Take an illustrative case: sorting customer reviews into positive and negative. A weak submission reports “92 percent accuracy” and stops. A strong one states how many reviews were used, how they were split into training and test sets, what a trivial baseline scored, which reviews the model got wrong and why, and what would change the result. The code can be identical. The second version shows that you understand what the number means.

Project ideas worth doing alongside the tasks

Assigned tasks may be narrow, and under the provider’s terms everything you create during the internship, including reports and materials, belongs to Saiket Systems. Build these on public data so you can show them freely.

1. Local-language text classifier. Collect public headlines or reviews in your own language and classify them by topic. Most tutorials use English, so this is original work, and mixed scripts and spelling variants teach real preprocessing.
2. Plant or crop leaf classifier. Use a public leaf-image dataset, then test it on photos from your own phone. The gap between the dataset score and your own photos is the most instructive part.
3. Document question-answering tool. Build a small tool that answers questions from public documents, such as a government scheme guide. Record each wrong answer and its cause.
4. A model that explains itself. Take a simple classifier and write a one-page note on which inputs drive its decisions and where it should not be trusted.

Write the question and the success measure before you write any code.

Eligibility

The provider’s FAQ says students, freshers, graduates, professionals and interested individuals may apply, and that selection can depend on application details, skills, availability and program requirements. It publishes no age limit, ID rule or device specification. Answer these honestly:

• Do you meet the provider’s broad eligibility description? Yes / No
• Do you have a laptop or desktop and a stable internet connection? Yes / No
• Can you read and write basic Python, such as loops and functions? Yes / No

If the first answer is No, write to support@saiket.in and ask before applying. If the second is No, arrange access first, since most AI work is impractical on a phone. If the third is No, spend your first two weeks on Python basics, because tasks will assume it. Small models and public datasets run on an ordinary laptop. Free cloud notebooks help with heavier work, but ask your mentor whether they are allowed for assigned tasks.

Certificate and recognition

The certificate rules are the same across the provider’s tracks, so we explain them once in our [Saiket Systems Internship Program guide: add link]. In short, submitting through the official form earns a digital certificate within 10 working days of review, while submitting by email gets only a confirmation of completion.

For AI work, a project you can explain and defend usually outweighs a certificate, so treat the certificate as supporting evidence.

Check the “AICTE approved” claim yourself

The official internships page carries “AICTE Approved Internships” as its page title and search description. The provider’s terms (last updated 27 May 2026) say the program is not affiliated with, endorsed by or run in partnership with any government authority, council or third-party body unless explicitly stated in writing. We did not find written approval details on either page. Before you rely on the AICTE label in a resume or application, ask the provider for the approval reference in writing and check it on AICTE’s own website.

Career and job outcomes

The provider markets large figures for learners, institution partnerships and hiring partners, with no supporting data on the page, so we cannot verify them. The provider’s terms say completing the internship does not guarantee a job. The provider’s testimonials are also marketing, not independent evidence.

AI roles are crowded with beginners who share the same tutorial projects. What sets you apart is specific detail: the dataset you chose, the failure you found and the decision you changed because of it.

How to enrol

The Machine Learning listing has an “Apply Now” button that opens a Google Form. Use the button on the official page, not a link sent to you by someone else.

1. Open https://saiketsystems.in/internships/ and find the Machine Learning listing under Data/AI.
2. Use its Apply Now button and complete the Google Form with accurate skills and availability, since the provider says selection can depend on them.
3. Watch your email for the offer, which sets your duration, and confirm within the stated time.
4. Ask your mentor on day one how many projects there will be and whether you will train models or use existing ones.

Common mistakes and problems

• Reporting one accuracy number. A model that is 95 percent accurate on data where 95 percent of cases belong to one class has learned nothing. Always compare against a simple baseline.
• Letting test data leak into training. If the same record, or a near copy, appears on both sides, your score is inflated. Split the data before any cleaning step that learns from it.
• Copying code you cannot explain. A mentor’s first follow-up is usually why you chose a setting. If you cannot answer, the task has taught you little.
• Trusting generated output without checking. Language models state wrong answers fluently. Test any AI-generated answer, code or citation against a reliable source.
• Pasting task data into a public AI chatbot. The terms bar sharing confidential information without permission, so ask your mentor what is allowed.
• Using datasets with unclear licences. Check the licence before you reuse or publish anything.

Is it worth it?

It is worth trying if you already know basic Python, you want deadlines and review on AI work you would otherwise do alone, and you accept unpaid work. It helps most with the step tutorials skip: having someone question whether your results mean what you think they mean. It helps least if you need credentials or placement support, have no coding background, or cannot give steady weekly hours.

The unresolved program name and dates are a reason to ask questions first, not to rush. Judge the first two tasks against the map above. If they only call a ready-made model with no evaluation, ask your mentor for one task where you train and test something yourself.

How to succeed in the course

Build your base before the first task

• Learn enough Python to read a short script and change it without breaking it.
• Learn what training data, test data and a baseline are, since every AI task depends on them.
• Run one small model end to end on a public dataset, even if it performs poorly.

Habits that help during the internship

• Change one thing at a time, and note the effect of each change.
• Save every failed experiment with a line on what you learned. Mentors value these.
• Record library versions and random seeds so a mentor can reproduce your numbers.
• Ask whether AI coding assistants are allowed for assigned tasks, and disclose any use.
• Explain each result in two sentences to someone who does not code. If you cannot, you do not yet understand it.

FAQs

Is there a program called “AI Power” on the official page? We did not find one. The closest listing is Machine Learning. If you saw “AI Power” in an email, advert or message, ask the provider to confirm the name in writing.

Is this the same as the data science internship? The official page lists Machine Learning and Data Science as separate tracks, and both currently point to the same application form. For the analytics side, see [our Data Science & Analytics post: add link].

Do I need a maths background? Not stated. Basic statistics and some algebra are enough to start. Deeper maths matters later, mostly for deep learning.

Can I do this on a low-end laptop? Small models and public datasets run on ordinary hardware. Heavier tasks may need a free cloud notebook, so ask first.

Will I train models or only use existing ones? The provider mentions “practical projects” without detail, so ask your mentor on day one.

Who do I contact with questions? support@saiket.in, the address given in the provider’s current terms.

Official links

• Official internships page: https://saiket.in/internship/
• Internship terms and conditions: https://saiketsystems.in/saiket-systems-internship-terms-and-conditions/
• Contact: support@saiket.in

Last checked 10 October 2026, when we opened the official internships page and the terms. We update this post when the provider changes dates or rules.