AI5 PROFESSIONAL CERTIFICATE · BY TGC INDIA · UPDATED AUGUST 2026
Deep Learning
Build neural networks for image, text and prediction tasks through guided projects.
Online + OfflineWeekdays + WeekendsRegular + Fast Track
22+years in training
20,000+learners across TGC
5classroom locations
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COURSE OVERVIEW
What this course
is built to do.
Build neural networks for image, text and prediction tasks through guided coding, experiment tracking, error analysis and deployment.
Each module combines a live trainer demonstration, guided lab, assignment and review before the next stage.
CONNECTED CAREER DIRECTIONS
Deep Learning AssociateComputer Vision EngineerNLP EngineerJunior AI Research Engineer
IS THIS COURSE RIGHT FOR YOU?
Choose it for the right reason.
You already know Python and basic machine learning
You want hands-on vision and NLP projects
You need experiment and error analysis skills
YOUR LEARNING ARC
From guided foundation to finished work.
Foundation
Neural networks, loss, gradients and optimisation.
Vision
CNNs, transfer learning and image tasks.
Language
Sequences, attention and transformer foundations.
Delivery
Evaluation, deployment and technical presentation.
INDUSTRY TASKS
Practise the work, not only the tool.
- Train and compare neural architectures
- Build a vision or text model
- Deploy and explain a deep-learning capstone
PRO
LED
LED
YOUR TRAINING TEAM
Learn from experienced working professionals.
Live demonstrations, guided practice and direct project feedback are part of the course. Trainers update examples and tool coverage as professional practice changes.
WHAT YOU WILL BE ABLE TO DO
Course outcomes
01
Explain and implement neural-network training
A foundation notebook showing forward, loss and update steps.
02
Build vision and text models
Reviewed computer-vision and NLP projects.
03
Diagnose model performance
Experiment records and segmented error analysis.
04
Deploy and defend a deep-learning system
A working application with model documentation and limitations.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
DETAILED COURSE FLOW
5 learning modules
Build neural networks for image, text and prediction tasks while learning how to train, evaluate, improve and present deep-learning systems.
Neural network and mathematics foundation
Understand the computations behind learning without separating theory from code.
CORE TOPICS
- Vectors, matrices and probability review
- Neurons, layers and activation functions
- Loss, gradients and backpropagation
- Training, validation and optimisation
GUIDED PRACTICE
Build a small neural network from basic components and explain each training step.
MODULE DELIVERABLE
Neural network foundation notebook
TensorFlow, Keras and training workflows
Create reproducible model pipelines and diagnose common training problems.
CORE TOPICS
- Data pipelines and batching
- Model architecture and Keras workflow
- Optimisers, learning rates and regularisation
- Overfitting, checkpoints and experiment records
GUIDED PRACTICE
Train and compare several architectures while recording configuration and results.
MODULE DELIVERABLE
Reproducible deep-learning experiment
Computer vision with CNNs
Build image models and understand what affects visual performance.
CORE TOPICS
- Convolutions and feature maps
- Classification and transfer learning
- Data augmentation and imbalance
- Detection and modern vision overview
GUIDED PRACTICE
Create an image classifier using transfer learning and analyse its failure cases.
MODULE DELIVERABLE
Computer vision portfolio project
Sequence, text and transformer models
Work with language and sequential information using current deep-learning patterns.
CORE TOPICS
- Embeddings and sequence representation
- RNN and LSTM foundations
- Attention and transformer concepts
- Text classification and model adaptation
GUIDED PRACTICE
Build and compare a text model and inspect errors across different examples.
MODULE DELIVERABLE
NLP deep-learning project
Evaluation, deployment and capstone
Turn a trained network into an explained, testable application with clear limits.
CORE TOPICS
- Metrics, calibration and error analysis
- Bias, robustness and model documentation
- Inference optimisation and serving
- Portfolio presentation and technical defence
GUIDED PRACTICE
Deploy a vision, text or prediction model and present its data, design, errors and intended use.
MODULE DELIVERABLE
Deployed deep-learning capstone
HOW THE TRAINING WORKS
Learn it. Apply it. Get it reviewed. Improve it.
Every important skill moves through explanation, demonstration, guided use and independent application. Trainer feedback is used to revise the work before it becomes part of the final portfolio.
Concept briefing
The trainer explains the principle, use case, limitations and the quality standard expected.
Live demonstration
A complete task is demonstrated while the trainer explains decisions, checks and common mistakes.
Guided lab
Learners repeat the method with support, ask questions and correct problems during the session.
Applied assignment
The same method is used on a different brief so the learner must make independent decisions.
Review and revision
Work is checked against a rubric, revised after feedback and prepared for project presentation.
PROGRESS IS CHECKED THROUGHClass exercisesModule deliverablesProject reviewsFinal capstone presentation
PORTFOLIO WORK
Projects you can show
Guided practice brief
Plan, produce, test and present a finished piece with trainer feedback.
Individual application
Plan, produce, test and present a finished piece with trainer feedback.
Workflow build
Plan, produce, test and present a finished piece with trainer feedback.
Industry-style assignment
Plan, produce, test and present a finished piece with trainer feedback.
Quality review
Plan, produce, test and present a finished piece with trainer feedback.
Final capstone
Plan, produce, test and present a finished piece with trainer feedback.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
TOOLS COVERED
PythonTensorFlowKerasPyTorch
Tool coverage may be updated when the industry changes. Core methods remain part of the course.COMMON QUESTIONS
Before you apply
What knowledge is required?
Python and basic machine-learning knowledge are expected, with required mathematics reviewed in context.
＋Are TensorFlow and PyTorch included?
The course uses current frameworks for guided work; the core training and evaluation concepts are taught independently of one library.
＋Does it include deployment?
Yes. The capstone includes inference delivery, model documentation and limitations.
＋Do I need coding experience?
No, unless the course level says otherwise. Your advisor will check the right starting level.
＋Are classes live or recorded?
Classes are trainer-led in the classroom or live online. Recordings may support revision but do not replace class.
＋Will I receive a certificate?
Yes. Course completion requires attendance, assignments and the final project.
＋Can working professionals join?
Yes. Weekday, weekend and selected fast-track schedules are available.
＋NEXT BATCH
Choose your course.
Choose your schedule.
Online or offline. Weekdays or weekends. Regular or fast track. Speak with an AI5 Academy advisor about the right starting level.