Flagship project
Image Classification with CNNs
Build and train a CNN to classify images from a real dataset with documented accuracy.
- Computer Vision
- CNN
- TensorFlow
Artificial IntelligenceHigh-Growth Skill
Deep Learning Crash Course in Amritsar with Real Models & Placement Support
Deep Learning Crash Course In Amritsar course in AmritsarJob-oriented deep learning training built on real datasets — small batches, daily hands-on coding and 100% placement assistance.
3 / 6 MonthsBeginner to advancedHybrid
3 / 6 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
Deep Learning Crash Course In Amritsar · 3 / 6 Months
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Deep Learning Crash Course in Amritsar is a practical, code-first introduction to deep learning for beginners and career switchers.
It opens with Python, math for ML, and neural network basics, then moves into CNNs, RNNs, transformers, computer vision, NLP, and model deployment on real projects.
You finish with a documented deep learning portfolio, a capstone project, CV preparation and interview coaching.
Every tech company in Amritsar — from the IT parks on Airport Road to the startups in Ranjit Avenue, from data teams in local firms to students preparing for AI roles — is waking up to the same reality: deep learning is no longer a research lab concept. It is how apps recognize faces, how chatbots answer questions, and how businesses predict what customers will buy next. Companies that once relied on basic Excel sheets now need people who can build and train neural networks. This is one of the fastest-growing tech skill demands in the city, and it shows no sign of slowing down.
techcadd's Deep Learning Crash Course in Amritsar is built on the tools you actually see in job listings — Python, TensorFlow, Keras, PyTorch, Google Colab, and real datasets — not abstract theory or endless math proofs. You build neural networks and train real models from the very first module and finish with a documented deep learning project portfolio, not just a certificate. The programme runs in a 3-month crash track, with an optional 6-month extended track for deeper mastery. Batches are capped at 10–12 students, and every model you build is reviewed weekly by mentors who still work on live AI projects for real clients.
Placement assistanceLive coding labsInternship certificate
- Duration
- 3 / 6 Months
- Level
- Beginner to advanced
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Deep Learning Crash Course is a nested ladder — the longer track includes every module from the shorter track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 08
Python & Math for Deep Learning
Python programming and the math foundations every deep learning professional assumes you already know.
Topics covered
- Python basics
- NumPy
- Pandas
- Matplotlib
- linear algebra
- calculus
- probability
- gradient descent
Tools in this module
PythonJupyter NotebookGoogle ColabNumPyPandasMatplotlibYou finish withA documented Python and math notebook for a real dataset
- Module 02 of 08
Neural Networks & Deep Learning Basics
Neural network fundamentals — building, training and evaluating your first deep learning models.
Topics covered
- Perceptrons
- activation functions
- backpropagation
- loss functions
- optimizers
- overfitting
- regularization
Tools in this module
TensorFlowKerasPyTorchScikit-learnGoogle ColabYou finish withA trained neural network model with documented accuracy and loss curves
- Module 03 of 08
Computer Vision & CNNs
Computer vision — building CNNs for image recognition, classification and detection tasks.
Topics covered
- Convolutional layers
- pooling
- image classification
- data augmentation
- transfer learning
- object detection
Tools in this module
TensorFlowKerasPyTorchOpenCVKaggle DatasetsYou finish withA trained CNN model for image classification with a documented accuracy report
- Module 04 of 08
NLP & Sequence Models
Natural language processing — building sequence models for text classification and prediction.
Topics covered
- RNNs
- LSTMs
- GRUs
- word embeddings
- text preprocessing
- sentiment analysis
- sequence prediction
Tools in this module
TensorFlowKerasPyTorchNLTKSpaCyHugging FaceYou finish withA trained NLP model for sentiment analysis with a documented performance report
- Module 05 of 08
Transformers & Attention Mechanisms
Transformers and attention — building and fine-tuning state-of-the-art NLP models.
Topics covered
- Attention
- self-attention
- transformer architecture
- BERT
- GPT
- fine-tuning
- tokenization
Tools in this module
Hugging Face TransformersPyTorchTensorFlowGoogle ColabYou finish withA fine-tuned transformer model for a real text classification task with documented results
- Module 06 of 08
Advanced Computer Vision & GANs
Advanced computer vision — detection, segmentation and generative models.
Topics covered
- Object detection
- image segmentation
- YOLO
- GANs
- style transfer
- image generation
Tools in this module
PyTorchTensorFlowOpenCVYOLOKaggle DatasetsYou finish withA trained object detection or GAN model with a documented performance report
- Module 07 of 08
Model Deployment & MLOps Basics
Model deployment — taking your trained models from notebook to production-ready applications.
Topics covered
- Model saving
- Flask APIs
- Streamlit apps
- Docker basics
- cloud deployment
- model monitoring
Tools in this module
FlaskStreamlitDockerGoogle CloudAWSHerokuYou finish withA deployed deep learning model with a working API and a live demo link
- Module 08 of 08
Capstone Project, Career & Interview Readiness
A capstone project combining computer vision, NLP and deployment, plus career readiness for AI/ML roles.
Topics covered
- Deep learning portfolio
- project documentation
- resume
- LinkedIn profile
- interview readiness
- freelancing setup
Tools in this module
GitHubPortfolioresumeLinkedInGoogle SlidesYou finish withA complete capstone project with documentation, and an interview-ready deep learning portfolio and resume
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Sentiment Analysis with NLP
Train an NLP model to classify text sentiment using real-world data.
- NLP
- RNN
- PyTorch
Deployed Deep Learning API
Deploy a trained model as a Flask API with a live demo link.
- Deployment
- Flask
- Docker
Expert Capstone
Complete an end-to-end capstone combining computer vision, NLP and deployment.
- Multi-Domain
- Portfolio
- Deployment
The working loop
Learn it. Build it. Make it yours.
Learn it. Build it. Make it yours.Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.
- 01
Industry-focused curriculum
You learn it the way AI teams and startups use it, with the workflow and standards a deep learning engineer expects.
Image Classification with CNNs
- 02
Live coding labs
Every module runs on real datasets with real models and real results, not a slide or a screenshot.
Sentiment Analysis with NLP
- 03
Mentor-reviewed deliverables
Each module ends with a documented model reviewed weekly by a mentor who still works on live AI projects.
Deployed Deep Learning API
- 04
Data and models from the start
Every project is built on real data and measured through model accuracy, the way AI teams work.
Expert Capstone
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with models you can show and skills you can defend.
Project review week at the Amritsar campus
Mentors mark up work line by line
Hiring drive & mock interview day
Practitioners run the panel, not HR
Why choose Deep Learning Crash Course In Amritsar
Why students choose techcadd
Why students choose techcaddWhat makes this crash course at techcadd different from a typical alternative is that every module is built on a real dataset and deployed to live models, so you finish with an end-to-end deep learning portfolio rather than disconnected certificates.
- 01
Mentors who still work on live AI projects
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
One project environment, extended through every module
You start the project in Module 01, extend it through every module, and complete the capstone by Module 08.
- 03
Small batch sizes
10–12 students per batch ensures every student gets personalised feedback on every deliverable.
- 04
Nested, no-repeat curriculum
Longer programs include everything from shorter tracks and add more depth — you never repeat a module.
- 05
Documented internship experience
Every student receives a documented internship certificate alongside their course certificate.
- 06
Placement support
Resume and LinkedIn preparation, interview coaching, and active placement assistance for graduates.
Eligibility
Who can join the Deep Learning Crash Course In Amritsar course
Who can join the Deep Learning Crash Course In Amritsar courseThis crash course works for a specific starting point — here's who gets the most out of it.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at beginner level and builds from there across 3 / 6 months.
- AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
- EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
- Time commitmentAble to attend regularly across 3 / 6 months, in a morning, evening or weekend batch.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
Tools you will actually use
The working stack for Deep Learning Crash Course In Amritsar
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
18tools covered
Programming language
- Python
Deep learning framework
- TensorFlow
- PyTorch
Deep learning API
- Keras
Cloud notebook
- Google Colab
Development environment
- Jupyter Notebook
Numerical computing
- NumPy
Data analysis
- Pandas
Data visualization
- Matplotlib
Machine learning library
- Scikit-learn
Computer vision
- OpenCV
Transformers library
- Hugging Face
NLP library
- NLTK
- SpaCy
Web framework
- Flask
App framework
- Streamlit
Containerization
- Docker
Code hosting
- GitHub
Certification
Get certified in Deep Learning Crash Course In Amritsar
Get certified in Deep Learning Crash Course In AmritsarIssued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.
- Module-wise assessment, not attendance-based
- Project submissions logged against your certificate
- Internship letter on the industrial training track
- Verifiable directly with the campus office
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- Transformers are the new baselineEmployers increasingly assume deep learning knowledge will extend to transformers and fine-tuning.
- AI and deployment are the new layersTeams expect deep learning professionals to integrate deployment and MLOps, not just model training.
The objective is practical knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this crash course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live coding work | Every module, from week one | Varies, often only at the end |
| Learning format | One project environment extended through every module | Topics demonstrated separately, rarely combined |
| Tools used | TensorFlow, PyTorch, Hugging Face, Deployment | Usually limited to basic Python |
| Batch size | 10–12 students per batch | Often 25–40 students per batch |
| Model deployment | Live deployment, APIs, Docker | Rarely covered |
| Advanced topics | Transformers, GANs, object detection | Often only theory |
| Deployment & MLOps | Flask, Streamlit, Docker, Cloud | Rarely covered |
| Portfolio | 8 documented deep learning deliverables | Often just assignments, not portfolio-ready work |
| Certificate | Completion certificate + documented internship | Only a completion certificate |
| Placement support | Resume, LinkedIn, interview coaching, active placement | Often only a completion certificate |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
No. The programme opens at beginner level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.
