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View detailsDeep Learning & Computer Vision Internship
An advanced self-paced virtual internship in deep learning and computer vision covering neural network training, CNNs, transfer learning, object detection and segmentation, ending with a capstone image model project.
Course duration3 months – 6 months
Modules4 module exams
Learning hours80 hours
Certificate at70% average
CertificateQR-verifiable
Overview
The Deep Learning & Computer Vision Internship is an advanced, self-paced virtual internship for learners who want to understand and build neural network models that work with images. The program follows the practical path of a computer vision project: preparing image data, designing and training networks, adapting pre-trained models and evaluating them with the right metrics.
Over four modules you will study how deep networks are trained with backpropagation and modern optimisers, how convolutional neural networks (CNNs) extract visual features, and how architectures such as ResNet made very deep models practical. You will then learn transfer learning, data augmentation, object detection with models such as YOLO and Faster R-CNN, semantic and instance segmentation, and the metrics used to judge them, including IoU and mAP. The final module covers training workflows, debugging, model optimisation and deployment formats. Study notes include short PyTorch examples you can run locally or in a free cloud notebook with a GPU.
Each module ends with a timed online multiple-choice exam. After completing all modules you will work on a capstone project brief to train and evaluate an image classification or object detection model on a public dataset. Learners who meet the exam criteria receive a QR-verifiable internship certificate showing the internship period, and the capstone becomes a portfolio-ready project.
What this internship covers
- Deep learning fundamentals and training neural networks in PyTorch
- Convolutional neural networks and landmark CNN architectures
- Transfer learning and data augmentation for small datasets
- Object detection, segmentation and evaluation metrics
- Training workflows, model optimisation and deployment basics
- A capstone image classification or detection project
Who is this for?
This is an advanced program for learners with programming experience and a basic understanding of machine learning.
- Final-year undergraduate and postgraduate students in computer science, electronics, IT, data science or related fields
- Developers and researchers who want to apply deep learning to image data
- Prerequisite: good working knowledge of Python and NumPy, and familiarity with basic ML ideas such as training and test sets
- Recommended: basic linear algebra and calculus, and access to a GPU through a free cloud notebook
Course duration: 3 months – 6 months · 80 hours of learning
What you’ll learn
Explain how deep neural networks are trained using backpropagation and optimisers
Build and train convolutional neural networks for image classification in PyTorch
Apply transfer learning and data augmentation to improve results on small datasets
Describe how object detection and segmentation models produce their outputs
Evaluate vision models using accuracy, precision, recall, IoU and mAP
Diagnose training problems and prepare a trained model for deployment
Benefits
- QR-verifiable internship certificate showing the internship period
- Portfolio-ready capstone computer vision project
- Module-wise study material with PyTorch 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.
Learn how deep neural networks are built and trained, and write your first training loop in PyTorch.
- Neurons, layers and activation functions
- Loss functions and backpropagation
- Optimisers and learning rate
- PyTorch tensors and training loop
Outcomes: Explain layers, activations and loss functions; Describe backpropagation and common optimisers; Write a basic PyTorch training loop
Understand how CNNs extract visual features and study the architectures that shaped modern computer vision.
- Images as tensors and convolution
- Stride, padding and pooling
- Batch normalisation and dropout
- LeNet, VGG, ResNet and EfficientNet
Outcomes: Calculate convolution output sizes; Explain pooling, batch normalisation and dropout; Compare landmark CNN architectures
Adapt pre-trained models to new tasks and learn how object detection and segmentation models locate and outline objects.
- Transfer learning and fine-tuning
- Data augmentation
- Object detection: YOLO and Faster R-CNN
- Segmentation and evaluation metrics
Outcomes: Apply transfer learning and data augmentation; Explain how detectors predict boxes and classes; Evaluate detection with IoU and mAP
Learn practical techniques to train, debug, optimise and deploy vision models, then apply them in the capstone project.
- Dataset preparation and class imbalance
- Monitoring, early stopping and error analysis
- Mixed precision and model optimisation
- Exporting and deploying models
Outcomes: Organise datasets and track experiments; Diagnose training issues using curves and error analysis; Optimise and export models for deployment
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
Deep Learning & Computer Vision Internship
3 months programme · 4 modules · 80 hours
COURSE PERIODStart date – End dateISSUE DATEDD Month YYYY
ISSUED BYMr. Ajay NautiyalManaging Director
DS-INDL-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 the modules online, run the PyTorch examples on your own computer or a free cloud GPU 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 practise 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 choose a public image dataset, fine-tune a pre-trained classification or detection model, track experiments and report results with suitable metrics and error analysis. It is designed to show the complete computer vision workflow in one project.
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 has about 80 hours of learning content. As it is self-paced, most learners finish in six to ten weeks, and the certificate shows your actual internship period.
You should be comfortable with Python and NumPy and understand basic machine learning ideas such as training and test data. Some linear algebra and calculus helps, and a free cloud notebook with a GPU is recommended for training models.
Each module has a timed online MCQ exam with instant results. You get two free attempts per module, and extra attempts can be purchased if needed.
After passing every module with at least 60% and reaching a 70% overall average, you receive a digital internship certificate showing the internship period. It carries a QR code that can be scanned to verify it online.
The capstone brief in Module 4 asks you to fine-tune a pre-trained classification or detection model on a public dataset and report its results with proper metrics. You complete it independently as a portfolio project.
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