Why learn Deep Learning
with EduMonk?
Understand and build neural networks for images, sequences and representation learning. Train models with modern frameworks, diagnose learning behaviour and apply transfer learning responsibly.
The online syllabus follows a deliberate rhythm: learn the underlying concept, apply it in guided practice, confirm it with a checkpoint, and connect it to a project decision. By the end, you will have more than lesson completion—you will have evidence you can discuss.
Start learning for ₹1,650 today.
For the Deep Learning Certificate of Specialisation, pay ₹1,650 now and the remaining ₹1,500 next month. The two payments together cover the complete ₹3,150 programme fee.
Build the skills
behind your career.
Technical knowledge is only one part of becoming career-ready. Your Deep Learning enrolment also includes a focused professional-development journey at no additional cost.
FREE
EduMonk Career Edge
60-Day Soft Skills & Career Readiness Program
Spend approximately 20 minutes every day developing the communication, confidence, workplace and career-readiness skills that complement your technical education.
What you'll learn in Deep Learning.
Every outcome uses an observable action—something you can build, evaluate, explain or improve.
Where Deep Learning is used
Use the programme workflow to frame, build and evaluate image classification.
Use the programme workflow to frame, build and evaluate text and sequence modelling.
Use the programme workflow to frame, build and evaluate anomaly and signal recognition.
Use the programme workflow to frame, build and evaluate transfer-learning prototypes.
Deep Learning skills
you'll build.
The programme develops eight connected skills and gives each one a place in the syllabus, lab work or assessment.
Your 2-month Deep Learning
learning plan.
A sustainable sequence that gives you time to understand, practise and produce useful work.
Enter AI foundations
Complete Introduction to Deep Learning and Python for Deep Learning.
Build with data and ML
Complete Mathematics for Neural Networks, Artificial Neural Networks and the Level 1 projects.
Apply modern AI
Work through Modules 5–8 and complete the applied AI projects.
Understand generative systems
Complete Transfer Learning and Sequence Data & Recurrent Neural Networks.
Build AI applications
Practise LSTM & GRU Networks and Natural Language Processing with Deep Learning.
Integrate, evaluate & demonstrate
Complete the remaining Level 3 modules and deliver the final specialisation capstone.
Deep Learning syllabus:
20 modules, 30 hours.
This is the complete planned syllabus for the Deep Learning Certificate of Specialisation. The 20 focused modules combine concept learning, hands-on practice and completion checks across the two-month journey.
Build practical capability in introduction to deep learning.
01What is Deep Learning?Lesson
02Artificial Intelligence vs Machine Learning vs Deep LearningLesson
03Evolution of neural networksLesson
04Applications of Deep LearningLesson
05Traditional Machine Learning vs Deep LearningLesson
06Structured and unstructured dataLesson
07Deep Learning lifecycleLesson
08Training and inferenceLesson
Time includes lesson study, guided practice, checkpoint preparation and project integration. Learners may spend longer when extending project work.
Hands-on Deep Learning
course projects.
Build 9 practical applications across the syllabus's progressive levels. The 3 capstones ask you to combine each level's skills, evaluate the result and explain the decisions behind it.
Student Result Prediction Using Neural Networks
Build, test and document a practical student result prediction using neural networks using the skills developed in Level 1.
Customer Churn Prediction with Deep Learning
Build, test and document a practical customer churn prediction with deep learning using the skills developed in Level 1.
Deep Learning Credit Risk Prediction
Build, test and document a practical deep learning credit risk prediction using the skills developed in Level 1.
Image Classification System
Build, test and document a practical image classification system using the skills developed in Level 2.
Sales / Stock Trend Prediction Using LSTM
Build, test and document a practical sales / stock trend prediction using lstm using the skills developed in Level 2.
Plant Disease Detection System
Build, test and document a practical plant disease detection system using the skills developed in Level 2.
Deep Learning Sentiment Analysis System
Build, test and document a practical deep learning sentiment analysis system using the skills developed in Level 3.
Image Recognition Using Transfer Learning
Build, test and document a practical image recognition using transfer learning using the skills developed in Level 3.
Intelligent Medical Image Classification Platform
Build, test and document a practical intelligent medical image classification platform using the skills developed in Level 3.
Deep Learning tools you'll work with
How the Deep Learning
course is assessed.
Your result reflects steady application across the programme—not one high-pressure exam.
Module checkpoints
20 focused checks confirm that core concepts and terminology are secure.
Hands-on labs
Every module includes a guided lab tied directly to the syllabus.
Level projects
Complete 6 focused Deep Learning builds across the three levels.
Capstone projects
Plan, build, test and explain 3 progressively more complete solutions.
Certificate eligibility
Complete all required modules, submit the assignments and capstone, and achieve at least 60% overall.
Who should take this
Deep Learning course?
College students
Build practical Deep Learning evidence alongside your academic work.
Working professionals
Use Deep Learning to strengthen your current role or move towards artificial intelligence work.
Builders & analysts
Turn concepts into a guided project, capstone and documented decisions.
Career switchers
Follow a clear starting path with no assumption of professional experience.
Deep Learning course prerequisites
- Basic Python
- High-school algebra
- Machine-learning fundamentals recommended
- A willingness to practise and complete the capstone
Deep Learning career paths
and applications.
This programme does not promise a job. It helps you build relevant capability and evidence for conversations around roles such as:
Deep Learning guides,
roadmaps and comparisons.
Use these editorial resources to understand where Deep Learning fits, what a sensible learning path looks like and how to turn the subject into project evidence.
Beginner guideArtificial Intelligence · Beginner guide · 11 min readDeep learning for beginnersA clear beginner guide to neural networks, layers, training, overfitting, evaluation and choosing a manageable first deep-learning project.
Read the guide ↗ComparisonArtificial Intelligence · Comparison · 10 min readDeep Learning vs Machine LearningCompare deep learning and machine learning by data needs, model complexity, skills, compute, projects and the right learning order for beginners.
Read the guide ↗
RoadmapArtificial Intelligence · Roadmap · 12 min readMachine Learning roadmapFollow a practical machine-learning roadmap through Python, data preparation, modelling, evaluation, projects and responsible deployment thinking.
Read the guide ↗Your Deep Learning
Certificate & LOR.
Learners who successfully meet all programme completion requirements receive an EduMonk Certificate of Specialisation and a programme-specific Letter of Recommendation. Both documents are issued as soft copies, and hard copies are dispatched by post to the learner address on record.
Deep Learning Certificate of Specialisation
Deep Learning Letter of Recommendation
About these samplesFinal documents are personalised with the completed learner's name, programme record, dates and identifiers. Sample names and details are illustrative. Please keep the postal address in your learner record accurate for hard-copy dispatch.
Deep Learning course FAQs.
Is this specialisation beginner friendly?+
Deep Learning is marked intermediate. The syllabus begins with foundations and states any useful prior knowledge clearly. Professional experience is not required.
Is this a real 30-hour syllabus?+
Yes. The programme contains 20 focused modules with concept learning, hands-on practice and knowledge checks, planned across 30 learning hours in total.
What will I build?+
You will complete 9 projects across three levels, including 3 capstones. The sequence begins with Student Result Prediction Using Neural Networks and culminates in the Intelligent Medical Image Classification Platform.
How are learners assessed?+
Completion combines module checkpoints, hands-on labs, a guided project and the final capstone. A minimum overall score of 60% is required for certificate eligibility.
What support is included?+
Your enrolment includes the complete sequenced learning experience, downloadable resources, project guidance and learner support through the connected external learner portal.
Is EduMonk Career Edge included?+
Yes. This Certificate of Specialisation includes complimentary access to EduMonk Career Edge, a 60-day Soft Skills & Career Readiness Program with one focused lesson of approximately 20 minutes each day. Career Edge adds ₹0 to your ₹3,150 programme fee.
How do payment and programme access work?+
The Enrol Now button opens this specialisation’s secure Razorpay Payment Page. EduMonk does not collect card, UPI or bank credentials. After the payment is confirmed, access to this specialisation and EduMonk Career Edge is prepared manually and sent to your registered email within 4 hours.
What certificate will I receive?+
After meeting the completion requirements, you will earn a verifiable EduMonk Certificate of Specialisation in Deep Learning.
Which completion documents are issued?+
Learners who successfully meet the Deep Learning completion requirements receive a soft-copy Certificate of Specialisation and programme-specific Letter of Recommendation. Hard copies of both documents are also dispatched by post to the learner address on record.
How long can I access the programme?+
The structured learning journey lasts 2 Months. Access details, completion records and earned credentials are managed through the connected external learner portal.
