Duration6 weeks6–8 hours per week
all-levels · Live Online / Classroom
Deep Learning & Computer Vision in Noida
Build practical capability in Deep Learning & Computer Vision through live instructor-led learning, hands-on labs, mentor feedback and portfolio-ready projects. Work with Python, TensorFlow, PyTorch, OpenCV, Jupyter and learn how to explain your approach, validate results and connect technical skills to real business or career outcomes.
Follow a structured route through Deep Learning & Computer Vision rather than relying on disconnected tutorials. Practise each major concept through guided labs, realistic assignments and review criteria. Create tangible evidence—working files, screenshots, demonstrations and documented decisions—for portfolio conversations.
A-56, Sector-64, Noida, Uttar Pradesh – 201301 Transparent course guidance
PythonTensorFlowPyTorch
Applied learningBuild, test and explain real workflows
Course at a glance
Next batch23 Aug 2026Open for Registration
Learning modeLive Online / ClassroomLive Online / Offline
LevelAll-LevelsLearners with Python and machine-learning fundamentals
Learning hours36 total hours24 guided + 12 practice/project hours
Projects3 guided projects
CertificateCompletion certificatesubject to published attendance, assignment and assessment criteria
Designed for applied outcomes
Why learners choose this course.
01
Follow a structured route through Deep Learning & Computer Vision rather than relying on disconnected tutorials.
02
Practise each major concept through guided labs, realistic assignments and review criteria.
03
Create tangible evidence—working files, screenshots, demonstrations and documented decisions—for portfolio conversations.
04
Receive mentor feedback focused on implementation quality, troubleshooting and improvement.
05
Prepare to explain the business problem, approach, trade-offs, validation method and results in interviews or stakeholder reviews.
Course curriculum
Explore every module, topic and practical outcome.
Open each module to review the complete topic list, guided practice, expected deliverable and assessment criteria before you enrol.Published learning outcomes
What you should be able to demonstrate.
Curriculum reviewed 03 Aug 2026- 01
Explain the core concepts, terminology and end-to-end workflow of Deep Learning & Computer Vision.
- 02
Apply the principal tools and techniques in guided, realistic scenarios.
- 03
Troubleshoot common implementation and quality issues using a structured method.
- 04
Document requirements, decisions, results, limitations and next steps professionally.
- 05
Build a portfolio-ready applied project and explain it in an interview or stakeholder review.
- 06
Use security, privacy, governance and responsible-practice principles appropriate to the domain.
01Module 1Neural network foundations
Learner capability after completion
By the end of this module, learners should be able to explain neural network foundations, apply the concepts in guided exercises, complete a practical task, document key decisions and evaluate the quality of the output against a defined checklist.
Detailed syllabus
8 focused topicsWhat you will learn
- 01
Perceptrons, tensors and feed-forward networks
- 02
Activation functions and network capacity
- 03
Loss functions, gradients and backpropagation
- 04
Optimisers, learning rates and training loops
- 05
Batching, epochs and convergence diagnostics
- 06
Overfitting, dropout and regularisation
- 07
TensorFlow/PyTorch model construction
- 08
Experiment tracking and reproducibility
Applied learning
Hands-on practice
- Complete a guided scenario using realistic inputs and a defined quality checklist.
- Produce a practical deliverable and present the approach, result, limitations and improvement plan.
Evidence of learning
Module deliverable
A reviewed practical output demonstrating neural network foundations, accompanied by implementation notes, screenshots or working files, test results, limitations and improvement actions.
Mastery check
How this module is assessed
Module quizzes15% — concept understanding and terminology
Guided labs25% — correct execution and troubleshooting
Applied assignments25% — independent application to realistic scenarios
Capstone25% — end-to-end solution and evidence pack
Presentation / viva10% — explanation, trade-offs, limitations and recommendations
Module quizzes: 15% — concept understanding and terminologyGuided labs: 25% — correct execution and troubleshootingApplied assignments: 25% — independent application to realistic scenariosCapstone: 25% — end-to-end solution and evidence packPresentation / viva: 10% — explanation, trade-offs, limitations and recommendations
02Module 2CNN architecture and image pipelines
Learner capability after completion
By the end of this module, learners should be able to explain cnn architecture and image pipelines, apply the concepts in guided exercises, complete a practical task, document key decisions and evaluate the quality of the output against a defined checklist.
Detailed syllabus
8 focused topicsWhat you will learn
- 01
Image tensors, colour channels and preprocessing
- 02
Convolution, kernels, padding and stride
- 03
Pooling and receptive fields
- 04
CNN architecture patterns
- 05
Image augmentation and data loaders
- 06
Class imbalance and labelling quality
- 07
Training diagnostics and visualisation
- 08
Building an image-classification pipeline
Applied learning
Hands-on practice
- Complete a guided scenario using realistic inputs and a defined quality checklist.
- Produce a practical deliverable and present the approach, result, limitations and improvement plan.
Evidence of learning
Module deliverable
A reviewed practical output demonstrating cnn architecture and image pipelines, accompanied by implementation notes, screenshots or working files, test results, limitations and improvement actions.
Mastery check
How this module is assessed
Module quizzes15% — concept understanding and terminology
Guided labs25% — correct execution and troubleshooting
Applied assignments25% — independent application to realistic scenarios
Capstone25% — end-to-end solution and evidence pack
Presentation / viva10% — explanation, trade-offs, limitations and recommendations
Module quizzes: 15% — concept understanding and terminologyGuided labs: 25% — correct execution and troubleshootingApplied assignments: 25% — independent application to realistic scenariosCapstone: 25% — end-to-end solution and evidence packPresentation / viva: 10% — explanation, trade-offs, limitations and recommendations
03Module 3Transfer learning
Learner capability after completion
By the end of this module, learners should be able to explain transfer learning, apply the concepts in guided exercises, complete a practical task, document key decisions and evaluate the quality of the output against a defined checklist.
Detailed syllabus
8 focused topicsWhat you will learn
- 01
Pretrained model families and feature reuse
- 02
Freezing, unfreezing and fine-tuning layers
- 03
Learning-rate strategies for fine-tuning
- 04
Domain shift and dataset-size considerations
- 05
Augmentation and regularisation
- 06
Benchmarking against a simple baseline
- 07
Efficient experimentation and checkpointing
- 08
Model interpretation and error review
Applied learning
Hands-on practice
- Complete a guided scenario using realistic inputs and a defined quality checklist.
- Produce a practical deliverable and present the approach, result, limitations and improvement plan.
Evidence of learning
Module deliverable
A reviewed practical output demonstrating transfer learning, accompanied by implementation notes, screenshots or working files, test results, limitations and improvement actions.
Mastery check
How this module is assessed
Module quizzes15% — concept understanding and terminology
Guided labs25% — correct execution and troubleshooting
Applied assignments25% — independent application to realistic scenarios
Capstone25% — end-to-end solution and evidence pack
Presentation / viva10% — explanation, trade-offs, limitations and recommendations
Module quizzes: 15% — concept understanding and terminologyGuided labs: 25% — correct execution and troubleshootingApplied assignments: 25% — independent application to realistic scenariosCapstone: 25% — end-to-end solution and evidence packPresentation / viva: 10% — explanation, trade-offs, limitations and recommendations
04Module 4Object detection and visual inspection
Learner capability after completion
By the end of this module, learners should be able to explain object detection and visual inspection, apply the concepts in guided exercises, complete a practical task, document key decisions and evaluate the quality of the output against a defined checklist.
Detailed syllabus
8 focused topicsWhat you will learn
- 01
Bounding boxes, anchors and intersection-over-union
- 02
One-stage and two-stage detector concepts
- 03
Annotation formats and labelling workflows
- 04
Precision-recall and mean average precision
- 05
Non-maximum suppression and thresholding
- 06
Defect detection and industrial inspection scenarios
- 07
Inference visualisation and false-positive analysis
- 08
Responsible deployment in operational settings
Applied learning
Hands-on practice
- Complete a guided scenario using realistic inputs and a defined quality checklist.
- Produce a practical deliverable and present the approach, result, limitations and improvement plan.
Evidence of learning
Module deliverable
A reviewed practical output demonstrating object detection and visual inspection, accompanied by implementation notes, screenshots or working files, test results, limitations and improvement actions.
Mastery check
How this module is assessed
Module quizzes15% — concept understanding and terminology
Guided labs25% — correct execution and troubleshooting
Applied assignments25% — independent application to realistic scenarios
Capstone25% — end-to-end solution and evidence pack
Presentation / viva10% — explanation, trade-offs, limitations and recommendations
Module quizzes: 15% — concept understanding and terminologyGuided labs: 25% — correct execution and troubleshootingApplied assignments: 25% — independent application to realistic scenariosCapstone: 25% — end-to-end solution and evidence packPresentation / viva: 10% — explanation, trade-offs, limitations and recommendations
05Module 5Model optimisation and deployment
Learner capability after completion
By the end of this module, learners should be able to explain model optimisation and deployment, apply the concepts in guided exercises, complete a practical task, document key decisions and evaluate the quality of the output against a defined checklist.
Detailed syllabus
8 focused topicsWhat you will learn
- 01
Model pruning, quantisation and distillation concepts
- 02
Latency, throughput and memory profiling
- 03
Batch versus real-time inference
- 04
Export formats and serving options
- 05
Containerisation and API packaging
- 06
Monitoring latency, failures and model quality
- 07
Edge, cloud and hybrid deployment trade-offs
- 08
Versioning and rollback plans
Applied learning
Hands-on practice
- Complete a guided scenario using realistic inputs and a defined quality checklist.
- Produce a practical deliverable and present the approach, result, limitations and improvement plan.
Evidence of learning
Module deliverable
A reviewed practical output demonstrating model optimisation and deployment, accompanied by implementation notes, screenshots or working files, test results, limitations and improvement actions.
Mastery check
How this module is assessed
Module quizzes15% — concept understanding and terminology
Guided labs25% — correct execution and troubleshooting
Applied assignments25% — independent application to realistic scenarios
Capstone25% — end-to-end solution and evidence pack
Presentation / viva10% — explanation, trade-offs, limitations and recommendations
Module quizzes: 15% — concept understanding and terminologyGuided labs: 25% — correct execution and troubleshootingApplied assignments: 25% — independent application to realistic scenariosCapstone: 25% — end-to-end solution and evidence packPresentation / viva: 10% — explanation, trade-offs, limitations and recommendations
Tools and platforms
Use the practical stack behind each workflow.
Account and software requirements are confirmed before your batch begins.PythonTensorFlowPyTorchOpenCVJupyter
Applied portfolio projects
Build applied work you can explain with evidence.
Every project is framed around a realistic challenge, working implementation, testing results, limitations and practical value.Project 01
Image classification solution
- Business challenge
- Apply Deep Learning & Computer Vision skills to a realistic scenario where a team needs a reliable, repeatable solution.
- Learner build
- Image classification solution.
- Evidence produced
- problem statement, implementation notes, screenshots or demonstration, testing results, limitations, business value and next-step recommendations.
- Portfolio package
- concise case study, repository or working files where appropriate, architecture/process visual and a two-minute interview-ready explanation.
Project 02
Visual defect detection model
- Business challenge
- Apply Deep Learning & Computer Vision skills to a realistic scenario where a team needs a reliable, repeatable solution.
- Learner build
- Visual defect detection model.
- Evidence produced
- problem statement, implementation notes, screenshots or demonstration, testing results, limitations, business value and next-step recommendations.
- Portfolio package
- concise case study, repository or working files where appropriate, architecture/process visual and a two-minute interview-ready explanation.
Project 03
Object detection prototype
- Business challenge
- Apply Deep Learning & Computer Vision skills to a realistic scenario where a team needs a reliable, repeatable solution.
- Learner build
- Object detection prototype.
- Evidence produced
- problem statement, implementation notes, screenshots or demonstration, testing results, limitations, business value and next-step recommendations.
- Portfolio package
- concise case study, repository or working files where appropriate, architecture/process visual and a two-minute interview-ready explanation.
Evidence 01Business or user problem statement and success criteria.
Evidence 02Architecture, workflow or process diagram.
Evidence 03Source files, repository, configuration or working artefact as appropriate.
Evidence 04Screenshots, demonstration or report showing the solution in operation.
Evidence 05Testing, evaluation or reconciliation results.
Evidence 06Security, privacy, access and responsible-use considerations.
Evidence 07Known limitations, risks and next-step improvements.
Evidence 08Two-minute interview-ready explanation and concise portfolio case study.
Primary portfolio output: Image classification solution. Create a practical image classification solution with a clear problem statement, implementation approach, screenshots or demonstrations, results, limitations and next-step recommendations. Learners should be guided to convert the final output into a concise portfolio case study.
Who should enrol
Confirm that this course matches your goals and starting point.
Learners with Python and machine-learning fundamentals- Yes, where the published evening/weekend schedule fits the learner’s availability.
- If you are unsure whether this course matches your background and goals, complete the career assessment or book a short suitability call before paying.
Learning methodology
Live guidance, deliberate practice and reviewable outputs.
Live expert-led sessions
Learn concepts through demonstrations, guided discussion and practical examples.
Hands-on labs
Complete structured exercises that reinforce each key skill.
Applied assignments
Solve realistic tasks with clear review criteria and mentor feedback.
Project portfolio
Build evidence of practical ability that can support career conversations.
Career guidance
Receive support with role mapping, resume positioning, project storytelling and interview preparation.
Flexible access
Use available live, weekend or online batch options based on the published schedule.
Assessment approachEvidence-based progress, not passive attendance
Earn the completion certificate by maintaining at least 75% attendance, completing required labs and assignments, submitting the capstone, achieving at least 60% overall, and following academic integrity and safe-lab requirements.
15+ yearsIndustry advisor
Learn with guidance shaped by real technology and data leadership.
Ranjeet Kumar
Advisor, FutureEdgeAI Academy · Innovation & Growth LeaderA technologist and data leader with 15+ years of experience applying data, artificial intelligence and machine learning to complex problems, scalable products and business growth.- Data science and machine learning solution leadership
- AI product strategy and scalable data-platform development
- High-performing technology team leadership and mentorship
Career direction
Translate course work into a credible professional story.
Skills from this programme may support responsibilities connected to the following role families. This is not an employment guarantee; suitability depends on prior experience, project quality, interview performance, employer requirements and market conditions.- AI/ML project contributor
- Junior AI application developer
- AI automation or solutions associate
- Technical product or implementation associate
Career-path consultation
Clarify suitable roles and realistic next steps based on background and goals.
Portfolio development
Convert projects into structured case studies with evidence and interview talking points.
Resume and LinkedIn guidance
Position relevant skills, tools and project outcomes accurately.
Mock interviews
Practise technical, project, behavioural and HR discussions, subject to programme eligibility.
Opportunity visibility
Share relevant opportunities or hiring-partner connections where available; never imply guaranteed placement.
Post-course roadmap
Recommend further practice, certifications and portfolio improvement.
Career support is not an employment guarantee. Suitability and outcomes depend on prior experience, project quality, interview performance, employer requirements and current market conditions.
Noida learning centre
Local guidance for learners across Delhi NCR.
FutureEdgeAI Academy supports learners from Noida, Greater Noida, Ghaziabad, Delhi, Gurgaon, Gurugram and the wider Delhi NCR region. Published delivery modes explain whether a particular cohort is live online, classroom-supported or hybrid.A-56, Sector-64, Noida, Uttar Pradesh – 201301Course FAQs
Clear answers before you enrol.
Who can join the Deep Learning & Computer Vision?
The course is suitable for learners with python and machine-learning fundamentals. A counselling conversation can help confirm the right starting level.
Do I need previous experience?
Python and introductory machine-learning knowledge are recommended, including train-test splits, basic metrics and matrix operations.
Are the classes live or recorded?
Classes are live and instructor-led. Supporting recordings and LMS resources are provided according to the published cohort policy.
Will I work on practical projects?
Yes. The learning design includes guided exercises, applied assignments and portfolio-ready projects aligned to the course skills.
Is career support included?
Eligible learners may receive resume guidance, LinkedIn optimisation, project review, mock interview support and opportunity visibility. Outcomes are not guaranteed.
Can working professionals join?
Yes. The current weekend batch runs on Saturday and Sunday from 10:00 AM to 1:00 PM.
Is a certificate provided?
A completion certificate may be issued after the learner meets published attendance, assignment, project and assessment requirements.
How can I know whether this course is right for me?
Book a counselling call or complete the FutureEdgeAI career assessment to discuss your background, goals, prerequisites and recommended learning path.
What is the weekly time commitment?
Approximately 24 guided/live hours plus 12 hours of practice and project work
What is included in the fee?
The course fee is ₹30,000 plus GST. Admissions confirms payment options, current inclusions and the published refund terms before payment.
Can I attend a demo before enrolling?
Yes. The current free demo class is scheduled for 16 August 2026 at 11:00 AM, subject to seat availability.
How is this different from free online learning?
Explain the value of sequencing, live guidance, feedback, assessed projects, accountability and career support without dismissing free resources.
Current fee: ₹30,000. Payment options and refund terms are explained before payment. Review the academy’s refund policy for published terms.
Learner proof
Practical learning, explained by the people who experienced it.
Published learner feedback from FutureEdgeAI pathways shows how guided projects, mentor review and career preparation can help learners communicate their work with greater confidence.5.0
“I started with basic Excel knowledge. The SQL, Power BI and Python projects helped me explain business insights clearly and move into an analyst role.”
Anisha SharmaBusiness Analyst
5.0
“The SOC project helped me practise alert triage, investigation notes, incident response and the professional reporting expected from analysts.”
Arjun MehtaCybersecurity Learner
5.0
“I progressed from AI fundamentals to building GenAI applications and a source-aware RAG assistant I could confidently demonstrate.”
Neha SinghApplied AI Learner
Individual learning and career outcomes vary by starting point, participation, project quality, experience and market conditions. Testimonials do not guarantee employment or placement.
Build capabilities used across modern technology teams
Ready to explore Deep Learning & Computer Vision?