Deep Learning Course Details - Fees, Subjects, Syllabus, Duration, Eligibility, Career Scope
Degrees offered: M.E /M.Tech.
What is Deep Learning
Deep Learning course is a subset of machine learning and artificial intelligence that focuses on neural networks with multiple hidden layers to model complex patterns in data. A Deep Learning course equips students with the knowledge and skills to design, train, and evaluate deep learning models for various applications. This specialisation of machine learning and artificial intelligence can be pursued at undergraduate and postgraduate levels. Deep Learning course duration varies from two years to four years depending on the degree type. Deep Learning course fees vary based on the institution and can range between Rs. 10,000 to Rs. 5,00,000.
Highlight: Deep Learning
Branch Name | Deep Learning |
Degree | |
Duration | UG: 4 years PG: 2 years |
Eligibility | UG: 10+2 PG: Bachelor’s degree |
Admission Process | Admission Examination/ Direct Admission |
Top Entrance Exams | JEE Main, JEE Advanced, GATE, TANCET |
Average Course Fees | Rs. 1 lakhs |
Top Colleges | IIIT Bangalore, IIT Ropar, IIT Roorkee, IIT Kanpur, IIT Kharagpur |
Career Options | Machine Learning Engineer, Data Scientist, Natural Language Processing (NLP) Engineer, Robotics Engineer, Data Engineer, Artificial Intelligence (AI) Architect |
Average Salary | Rs. 10 LPA |
Recruiting Companies | Google, Microsoft, Amazon, IBM, Nvidia, OpenAI, Facebook, Apple, Adobe, Qualcomm |
Specialisation or Similar Ones
The Deep Learning course is provided in many undergraduate and postgraduate degree programmes. Deep Learning is a broad field that offers various specialisations for students who are interested in pursuing their careers in this field. Below, we have mentioned some of the key specialisations in a Deep Learning course:
Computer Vision
Natural Language Processing
Reinforcement Learning
Generative Adversarial Networks (GANs)
In the table below, we have mentioned the major UG and PG programmes in Deep Learning and other related course:
Top Deep Learning Colleges in India
Deep Learning courses are offered by a number of public and private universities and colleges. Deep Learning fee is mostly based on the degree programme which is chosen by the student, type and location of the college. Some of India's top Deep Learning colleges are listed in the table below.
Colleges | Fees |
Rs. 99,000 | |
- | |
Rs. 39,999 | |
Rs. 20,000 | |
- | |
- | |
Rs. 15.54 Lakhs | |
- |
Top Private Deep Learning Colleges in India
Deep Learning courses are offered by a number of private universities and colleges in India. Deep Learning admission is offered based on previous academic achievements and the results of the Deep Learning entrance examinations. The table below lists some of India's best private Deep Learning colleges.
Colleges | Fees |
Noorul Islam Centre For Higher Education, Kanyakumari | - |
Graphic Era Hill University, Dehradun | Rs. 15.54 Lakhs |
Top Government Deep Learning Colleges in India
In India, there are many government universities and colleges that offer Deep Learning courses. Prior academic achievements and entrance examination scores are used to determine admission for Deep Learning courses. India's best government Deep Learning colleges are listed in the table below.
Colleges | Fees |
IIIT Bangalore | Rs. 99,000 |
IIT Ropar | - |
IIT Roorkee | Rs. 39,999 |
IIT Kanpur | Rs. 20,000 |
IIT Kharagpur | - |
IIM Kashipur | - |
Eligibility Criteria (UG & PG) of Deep Learning
To pursue a Deep Learning course in India at the undergraduate level, candidates must have completed their 10+2 in Science stream with the major subjects like Mathematics, Physics, and Computer Science. For postgraduate degree programmes, candidates must hold a bachelor's degree in a relevant field, such as Computer science engineering, or mathematics. Students need to appear for entrance examinations such as JEE Main, JEE Advanced or GATE in order to take admission in a Deep Learning course.
Eligibility Criteria UG Courses
Before taking admission in Deep Learning course at the undergraduate level in India, candidates must meet the following Deep Learning eligibility criteria:
Students need to complete 10+2 from a recognised board of India or equivalent qualification in Science stream with a major focus on Mathematics, Physics, and Computer Science.
Students should appear for the entrance examination, such as JEE Main, JEE Advanced.
Students should have a strong foundational knowledge in mathematics, programming, and data structures.
Top Entrance Exams for UG Courses
JEE Main: JEE Main is a national-level exam conducted by the National Testing Agency (NTA) for admission to engineering programs at NITs, IIITs, and other centrally funded technical institutions.
JEE Advanced: JEE Advanced is a national-level exam conducted by the IITs on a rotating basis for admission to their undergraduate degree programmes, including Deep Learning courses.
Eligibility Criteria PG Courses
To pursue a Deep Learning course at the PG level in India, candidates must meet the following eligibility criteria:
Students need to pursue a bachelor's degree in a relevant field, such as Computer Science Engineering or Mathematics.
Students should appear for the entrance examination, such as GATE, TANCET, PGEE.
After qualifying the entrance examination, students should appear for the counselling process.
Top Entrance Exams for PG Courses
GATE: GATE is a national-level exam conducted jointly by IISc and seven IITs on the behalf of the National Coordination Board for admission to postgraduate degree programmes in engineering and technology in many government and private colleges in India.
TANCET: TANCET or Tamil Nadu Common Entrance Test is a state level entrance examination conducted by Anna University for admission to M.Tech programmes in Deep Learning and other engineering courses in Tamilnadu.
Scope of Deep Learning in India and Abroad
The future scope of Deep Learning courses in India is promising because of the rapidly growing job market and industry. As technology advances, the demand for skilled Deep Learning professionals is expected to rise in various sectors, such as healthcare, finance, automotive, and e-commerce. Job roles for Deep Learning graduates include Data Scientist, Machine Learning Engineer, Deep Learning Engineer, AI Research Scientist, and Computer Vision Engineer. With the increasing reliance on data and the integration of AI in various industries, the scope and demand for Deep Learning professionals are only expected to grow in the coming years.
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Course Fees Deep Learning
| Minimum Fees | Maximum Fees | |||
|---|---|---|---|---|
| Private | Government | Private | Government | |
| UG |
16.73 Lakhs
B.E /B.Tech in Deep Learning at Graphic Era Hill University, Dehradun
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16.73 Lakhs
B.E /B.Tech in Deep Learning at Graphic Era Hill University, Dehradun
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Data not available
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| PG |
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Data not available
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78.80 K
M.E /M.Tech. in Deep Learning at Indian Institute of Technology Mandi
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Data not available
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78.80 K
M.E /M.Tech. in Deep Learning at Indian Institute of Technology Mandi
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Course Subjects
Deep Learning courses are offered in many private and government colleges in India. Deep Learning courses are offered in various degree programmes, such as B.Tech, M.Tech, MBA, certificate programmes, Diploma courses. In this section we have mentioned the Deep Learning syllabus of undergraduate and postgraduate courses.
Deep Learning Syllabus for UG course
The undergraduate Deep Learning course focuses on providing students with a strong foundation in computer science, mathematics, and artificial intelligence concepts. Deep Learning syllabus for UG course includes the study of various techniques and algorithms used in Deep Learning. In the table below, we have mentioned the major topics of the UG syllabus of Deep Learning course:
Introduction to Deep Learning | Linear Algebra |
Probability and Statistics | Programming Languages |
Data Structures and Algorithms | Machine Learning |
Recurrent Neural Networks | Reinforcement Learning |
Neural Networks | Convolutional Neural Networks |
Deep Learning Syllabus for PG course
The postgraduate degree programme of Deep Learning course focuses on advanced concepts and techniques in the field, providing students with the knowledge and skills required for a successful career in this domain. In the table below, we have mentioned the major topics of the PG syllabus of Deep Learning course:
Advanced Machine Learning | Deep Learning Frameworks |
Computer Vision | Natural Language Processing |
Reinforcement Learning | Generative Models |
Unsupervised Learning | Transfer Learning and Domain Adaptation |
Optimization Techniques | Scalable Deep Learning |
Careers in Deep Learning
Deep Learning is a subfield of machine learning that has been gaining a lot of attention in recent years due to its remarkable success in various applications, such as computer vision, natural language processing, speech recognition, and robotics. As a result, pursuing a career in Deep Learning can be a very lucrative choice. In the table below, we have discussed several career options available after completing a Deep Learning course.
A Machine Learning Engineer is responsible for designing, implementing, and maintaining machine learning algorithms and systems. He or she also works on data analysis, feature engineering, and model deployment. | |
A Data Scientist is responsible for analysing and interpreting complex data using statistical and machine learning techniques. He or she develops models that can help solve business problems and provide insights into the data. | |
Natural Language Processing (NLP) Engineer | An NLP Engineer is responsible for developing algorithms and systems that can process and analyse natural language data. He or she works on applications such as chatbots, sentiment analysis, and language translation. |
A Robotics Engineer is responsible for developing algorithms and systems that can control and operate robots. He or she works on applications such as autonomous vehicles, drones, and industrial robots. | |
A Data Engineer is responsible for developing and maintaining the infrastructure that is required for storing and processing large amounts of data. He or she works on designing databases, data pipelines, and data warehouses. | |
Artificial Intelligence (AI) Architect | An AI Architect is responsible for designing and developing AI systems that can solve complex business problems. He or she works on developing architectures that can integrate different AI technologies and tools. |
Upcoming trends
Deep Learning is a rapidly evolving field, with new techniques and applications emerging regularly. In this section, we have discussed some of the trending topics in the Deep Learning course that are shaping the future of artificial intelligence. The following are some of the most significant trends:
Transformers: An innovative architecture that has revolutionised natural language processing and is now being applied to other domains, such as computer vision and reinforcement learning.
AutoML: The development of automated machine learning techniques for model selection, hyperparameter tuning, and architecture search, enabling faster and more efficient model development.
Federated Learning: A decentralised approach to training machine learning models on distributed data, preserving privacy while enhancing the collective learning process.
Capsule Networks: A novel architecture designed to address the limitations of convolutional neural networks by incorporating spatial hierarchies and preserving viewpoint information.
Explainable AI: Developing techniques to understand and interpret the decisions made by complex Deep Learning models, fostering trust and transparency in AI systems.
Edge AI: Running Deep Learning models on edge devices, such as smartphones and IoT sensors, enabling real-time decision-making and reducing data transmission costs.
Job Profiles and Top Recruiters
Deep Learning graduates are employed in various companies, including government and private sectors. Top recruiters for Deep Learning professionals include a mix of technology companies, startups, and research institutions. Some of the prominent names are:
Google
Microsoft
Amazon
IBM
Nvidia
OpenAI
Facebook
Apple
Adobe
Qualcomm
Average Salary
In the table below, we have mentioned the average salary of the Deep Learning graduates:
Job profiles | Average salary |
Machine Learning Engineer | Rs. 6.5 LPA |
Data Scientist | Rs. 10 LPA |
Natural Language Processing (NLP) Engineer | Rs. 8 LPA |
Robotics Engineer | Rs. 5 LPA |
Data Engineer | Rs. 7.7 LPA |
Artificial Intelligence (AI) Architect | Rs. 20 LPA |
Note: These salary figures may vary depending on various factors, such as designation, experience, skills, job location.
Required Skillset for Deep Learning
Success in the Deep Learning field requires a combination of technical and soft skills. These soft skills and technical skills will help students in academics as well as prepare them for a rewarding career in the Deep Learning field.
Programming Skills | Mathematics |
Machine Learning | Problem-solving Skills |
Communication | Adaptability |
Critical Thinking | Creativity |
Course Curriculum for Deep Learning
The Deep Learning course curriculum aims to provide students with a comprehensive understanding of the core concepts, techniques, and applications in this exciting field. Deep Learning course curriculum typically includes the following key topics and modules:
Introduction to Deep Learning: Overview of the field, its history, and applications.
Foundations of Mathematics: Linear algebra, calculus, and probability theory, essential for understanding Deep Learning techniques.
Programming and Data Structures: Learning programming languages like Python, along with data structures and algorithms required for model implementation.
Machine Learning Basics: Introduction to the fundamentals of machine learning, including supervised, unsupervised, and reinforcement learning.
Neural Networks: Understanding the architecture and training techniques of artificial neural networks, including feedforward, recurrent, and convolutional networks.
Deep Learning Frameworks: Working with popular frameworks like TensorFlow and PyTorch to develop and train models.
Computer Vision: Techniques for image recognition, object detection, and segmentation using Deep Learning models.
Natural Language Processing: Applications of Deep Learning in text analysis, sentiment analysis, language translation.
Popular Deep Learning Entrance Exams in India
GATE
Exam Date: 01 Feb, 2025 - 02 Feb, 2025
Frequently Asked Questions (FAQs)
Question: What are the top recruiting companies that hire Deep Learning students?
Answer :
The top recruiting companies that hire Deep Learning students are Google, Microsoft, Amazon, IBM, Nvidia, OpenAI, Facebook, Apple, Adobe, Qualcomm.
Question: What are the top career choices for the Deep Learning students?
Answer :
The top career choices for the Deep Learning students are Machine Learning Engineer, Data Scientist, Natural Language Processing (NLP) Engineer, Robotics Engineer, Data Engineer, Artificial Intelligence (AI) Architect.
Question: What are the skills required for a Deep Learning course?
Answer :
The skills required for a Deep Learning course are Programming Skills, Mathematics, Machine Learning, Problem-solving Skills, Communication Skills, Adaptability, Critical Thinking, Creativity.
Question: What are the entrance exams required for Deep Learning?
Answer :
The entrance exams required for Deep Learning are JEE Main, JEE Advanced, GATE, TANCET.
Question: What are the specialisations of Deep Learning courses?
Answer :
Some of the specialisations of Deep Learning courses are Computer Vision, Natural Language Processing, Reinforcement Learning, Generative Adversarial Networks (GANs).
Question: What is the future scope of a Deep Learning course?
Answer :
The future scope of a Deep Learning course is promising, as technology advances, the demand for skilled deep learning professionals is expected to grow, offering exciting career opportunities in fields such as healthcare, finance, autonomous vehicles, natural language processing, and computer vision.
Question: Who is eligible for pursuing a Deep Learning Course?
Answer :
Eligibility criteria for pursuing a Deep Learning course depend on the specific program and educational level. For undergraduate programs, students should have completed their high school education with a strong foundation in mathematics and programming. For postgraduate programs, a bachelor's degree in a relevant field, such as computer science, engineering, or mathematics, is usually required. Some courses may also require prior knowledge of machine learning or artificial intelligence concepts.
Question: What are the benefits of pursuing a Deep Learning course?
Answer :
Pursuing a Deep Learning course has several benefits such as high demand for skilled professionals; Deep learning professionals often command higher salaries due to their specialised knowledge and the value they bring to companies. A Deep Learning course equips students with the latest techniques and knowledge in the field, allowing them to contribute to the advancement of artificial intelligence. The course helps develop problem-solving skills and critical thinking abilities that are valuable in any industry.
Question: Is a Deep Learning course difficult?
Answer :
The difficulty level of a Deep Learning course varies depending on an individual's background and experience in programming, mathematics, and machine learning. While it can be challenging due to the complexity of some concepts and the required mathematical foundations, students with a strong interest in artificial intelligence and a willingness to learn can successfully complete the course. A strong support system, including knowledgeable instructors and accessible resources, can also contribute to making the learning process more manageable.
Question: What is a Deep Learning course?
Answer :
A Deep Learning course focuses on teaching the fundamentals, techniques, and applications of deep learning, a subfield of artificial intelligence and machine learning. It covers topics such as neural networks, convolutional neural networks, recurrent neural networks, natural language processing, computer vision, and deep reinforcement learning. The Deep Learning course also includes hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.
