Artificial Intelligence Trending Internship
Artificial Intelligence
Understand core AI concepts, techniques and responsible use.
Pricing to be announced
View detailsArtificial Intelligence & Generative AI Internship
An advanced self-paced virtual internship covering machine learning foundations, neural networks, transformers, large language models, prompt engineering and retrieval-augmented generation, ending with a capstone GenAI application project.
Course duration3 months – 6 months
Modules4 module exams
Learning hours80 hours
Certificate at70% average
CertificateQR-verifiable
Overview
The Artificial Intelligence & Generative AI Internship is an advanced, self-paced virtual internship for learners who want a solid technical understanding of how modern AI systems work and how generative AI applications are built. The program moves from core machine learning ideas to the transformer architecture behind today's large language models (LLMs), and then to the practical techniques used to build reliable applications on top of them.
Across four modules you will study supervised and unsupervised learning, model evaluation, neural networks and backpropagation, embeddings and attention, and the way LLMs are pre-trained, fine-tuned and aligned. You will then learn prompt engineering patterns, retrieval-augmented generation (RAG) with vector databases, tool use, and the evaluation and responsible-AI practices needed to ship GenAI features safely. The study notes include short Python examples and clear definitions so you can practise on your own machine or a free cloud notebook.
Each module ends with a timed online multiple-choice exam. After the final module you will complete a capstone project brief to build a document question-answering assistant using RAG. Learners who meet the exam criteria receive a QR-verifiable internship certificate showing the internship period, and the capstone gives you a portfolio-ready project that demonstrates end-to-end GenAI skills.
What this internship covers
- Machine learning foundations, model evaluation and overfitting
- Neural networks, embeddings, attention and the transformer architecture
- How LLMs are trained, fine-tuned and aligned
- Prompt engineering, RAG, vector search and tool use
- Evaluating GenAI systems and responsible AI practices
- A capstone RAG-based document assistant project
Who is this for?
This is an advanced program for learners who already have programming experience and some exposure to data or machine learning concepts.
- Final-year undergraduate and postgraduate students in computer science, IT, data science or related engineering fields
- Software developers and data analysts who want to build generative AI applications
- Prerequisite: good working knowledge of Python, including functions, lists, dictionaries and installing packages with pip
- Recommended: basic linear algebra, probability and familiarity with NumPy or pandas
Course duration: 3 months – 6 months · 80 hours of learning
What you’ll learn
Explain the differences between supervised, unsupervised and generative models
Evaluate machine learning models using appropriate metrics and validation methods
Describe how embeddings, self-attention and transformers process text
Apply prompt engineering patterns to obtain reliable, structured LLM outputs
Build a retrieval-augmented generation pipeline using embeddings and a vector store
Assess GenAI applications for accuracy, hallucination, bias and safety risks
Benefits
- QR-verifiable internship certificate showing the internship period
- Portfolio-ready capstone RAG application project
- Module-wise study material with Python examples
- Self-paced online learning that fits around study or work
- Instant results after each module exam
Course modules
Modules unlock one after another. Pass each module exam to open the next.
Build a strong base in how machine learning models learn from data and how they are evaluated. These ideas underpin every generative AI system.
- AI, ML, deep learning and generative AI
- Supervised and unsupervised learning
- Loss functions and gradient descent
- Evaluation, overfitting and regularisation
Outcomes: Differentiate types of machine learning; Explain loss, gradient descent and generalisation; Choose suitable evaluation metrics
Understand how neural networks learn and how the transformer architecture uses attention to model language. This is the technical core behind modern LLMs.
- Neural networks and backpropagation
- Tokenisation and embeddings
- Self-attention mechanism
- Transformer architecture variants
Outcomes: Explain neurons, activations and backpropagation; Describe tokens and embeddings; Explain self-attention and the transformer block
Learn how LLMs are trained and controlled, and how to ground them in your own data with retrieval-augmented generation.
- LLM training: pre-training, fine-tuning, RLHF
- Prompt engineering patterns
- Decoding parameters and hallucination
- Retrieval-augmented generation and vector databases
Outcomes: Explain pre-training, fine-tuning and alignment; Apply prompt engineering patterns and sampling settings; Design a basic RAG pipeline
Put LLMs to work in real applications using APIs, tool use and agents, and learn how to evaluate, secure and deploy them responsibly. Includes the capstone project brief.
- LLM APIs, structured output and tool calling
- Agents and orchestration
- Evaluating GenAI systems
- Responsible AI and LLM security
Outcomes: Integrate LLMs via APIs with structured outputs and tools; Evaluate GenAI outputs systematically; Apply responsible AI and security best practices
How it works
Choose & enroll
Pick your course duration, register, verify your email and pay securely online.
Study each module
Your course dates are set automatically. Learn the topics and study material of each module.
Pass module exams
Take the module exams in order — pass one to unlock the next. 2 attempts per module.
Get your e-Certificate
Reach a 70% average across all modules to unlock your verifiable e-Certificate.
Assessment & certification rules
Four module exams of 10 multiple-choice questions each (15 minutes per module). Pass each module with 60% and reach a 70% overall average to earn your internship certificate.
| Module exams | 4, taken in order (each module unlocks after the previous one is passed) |
|---|---|
| Questions per module | 10 multiple-choice |
| Pass mark per module | 60% (unless stated on the module) |
| Needed for the e-Certificate | All modules passed and an average of 70% or more (best score of each module, weighted by marks) |
| Attempts | 2 free attempts per module; an extra attempt costs ₹199 |
| Completion deadline | All module exams must be completed within your course duration (extension of 30 days available for ₹299) |
| Certificate release | On the end date of your course, after all requirements are met |
| Grading | Automatic, immediately after submission |
Your e-Certificate
On completion you receive a QR-verifiable digital internship certificate showing your name, the program, your internship period and learning hours. Anyone can verify it online using the certificate number.
CERTIFICATE
OF ACHIEVEMENT
This is to certify that
Your Name
has successfully completed all module examinations of the professional programme
Artificial Intelligence & Generative AI Internship
3 months programme · 4 modules · 80 hours
COURSE PERIODStart date – End dateISSUE DATEDD Month YYYY
ISSUED BYMr. Ajay NautiyalManaging Director
DS-INAI-2026-000000
This is a digitally generated e-Certificate and does not require a physical signature.
View sample certificate (PDF)
Sample for illustration — your certificate carries your name, dates, a unique number and a QR code.
- Unique certificate number and verification ID
- QR code linking to the public verification page — employers can check it in seconds
- Shows your course period and learning hours
- Validity: Lifetime
Internship program
This is a self-paced virtual internship. You study online at your own pace, practise the examples in Python on your own computer or a free cloud notebook, and take each module exam when you are ready.
How the internship works
- Enrol and get access to all four modules and their study material.
- Study the modules in order and try the code examples yourself.
- Take the timed online MCQ exam for each module and see your result instantly.
- Pass every module with at least 60% while keeping an overall average of 70% or more.
- Complete the capstone project brief from Module 4 and download your certificate.
Capstone project
The capstone asks you to build a document question-answering assistant that uses retrieval-augmented generation: chunk and embed a set of documents, store them in a vector index, retrieve relevant passages and generate grounded answers with citations. You also write a short evaluation of its accuracy and limitations.
Internship certificate
On meeting the exam criteria you receive a digital internship certificate showing your name, the program name and the internship period. A QR code on the certificate lets anyone verify it on the DataSquare Solutions website. The internship does not include mentoring sessions or placement guarantees.
Ratings & reviews
Frequently asked questions
The program contains about 80 hours of learning content. As it is self-paced, most learners complete it in six to ten weeks, and your certificate shows the actual internship period.
You should be comfortable writing Python code and installing packages. Basic knowledge of linear algebra, probability and libraries such as NumPy or pandas will make the material easier to follow.
Each module exam is a timed online MCQ test with instant results. You get two free attempts per module, and additional attempts can be purchased if needed.
If you pass every module with at least 60% and reach a 70% overall average, you receive a digital internship certificate showing the internship period. It includes a QR code that can be scanned to verify it online.
The capstone brief in Module 4 asks you to build a RAG-based document question-answering assistant and write a short evaluation of it. You complete it independently as a portfolio project.
Related certifications
From₹2,219.26
Enroll now