Our Generative AI Courses Duration And Fees

Generative AI Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Applied Generative AI Specialization

Cohort Starts: 31 May, 2024

4 Months$ 4,000
Generative AI for Business Transformation

Cohort Starts: 31 May, 2024

4 Months$ 3,350
Post Graduate Program in AI and Machine Learning

Cohort Starts: 3 Jun, 2024

11 Months$ 4,300
AI & Machine Learning Bootcamp

Cohort Starts: 3 Jun, 2024

6 Months$ 10,000

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Generative AI Courses Advisors

  • Amitendra Srivastava

    Amitendra Srivastava

    Chief Data Scientist at Intelytica

    Amitendra’s expertise lies in utilizing data analysis and machine learning techniques to solve complex business problems and drive strategic decisions. As Chief Data Scientist, he leverages the power of data to create value and drive innovation.

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  • Ankit Virmani

    Ankit Virmani

    Data & ML Leader at Google

    Ankit is an ethical AI and data engineering enthusiast with 10+ years of experience at firms like Google, Amazon, and Deloitte. He serves as a member of the Forbes Technology Council, IU's Institute of Business Analytics, and AI 2030.

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  • Arijit Mitra

    Arijit Mitra

    Director and Head of Machine Learning & AI at Pegasystems

    Arijit is an engineering & product leader with expertise in building and deploying AI, NLP, GPT & LLMs at scale for Fortune 500 companies. As head of AI & ML at Pega, he owns the overall AI roadmap with a focus on AI applications across functions.

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  • Armando Galeana

    Armando Galeana

    Founder and CEO at Ubhuru Technologies

    A seasoned data science leader, with extensive experience in digital transformation. Throughout his career, Armando has leveraged his vast expertise in AI & ML to build infrastructure, create new lines of business and drive global implementations.

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  • Dr. Balasubramanian R

    Dr. Balasubramanian R

    Professor at IIT Roorkee

    Esteemed Professor at IIT Roorkee, holding a Ph.D. in Mathematics and Computer Science from IIT Madras. With over 20 years of teaching experience, he advocates the latest AI/ML and Data Analytics trends in his teachings, a valuable asset to our program.

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  • Dr. Sudeb Dasgupta

    Dr. Sudeb Dasgupta

    Professor at IIT Roorkee

    Respected Professor at IIT Roorkee, with a Ph.D. in Electronics Engineering from BHU. His deep understanding of electronics and view on leveraging Generative AI brings a unique perspective to this program

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  • Manish Anand

    Manish Anand

    CEO at iHUB DivyaSampark, IIT Roorkee

    Leading as CEO at iHUB DivyaSampark, IIT Roorkee. An alumnus of IIT Kanpur with an MBA from KAIST, Manish is a seasoned innovator, fostering technological innovation at IHUB  with a keen interest in AI & ML and analytics domain, making him an ideal advisor for our program.

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  • Raghav Goel

    Raghav Goel

    Generative AI & Data Science Consultant

    A passionate and successful corporate trainer who has delivered 150+ training sessions for corporates in India, Middle East, USA, and South East Asia for corporate clients like Publicis Sapient, KPMG, Capgemini, Coforge, ITC, DXC, Huawei, and IBM.

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Generative AI Courses Learner's Reviews

  • Janani Varun

    Janani Varun

    I would give a 5-star rating for the Simplilearn course I took. It helps me understand the content easily through online self-learning videos, and trainers assist us with their enriched knowledge, as well.

  • Abhineet Srivastava

    Abhineet Srivastava

    This course was all worth it! The course content was comprehensive and updated. The journey from a Python-based approach to understanding Statistical concepts, Machine Learning, and other concepts was just incredible. Thanks to all the amazing trainers and co-learners for giving me such an enriched experience.

  • Sudipta Samanta

    Sudipta Samanta

    The courses are well-structured with self-learning, live classes, projects & assessment. The trainers are well trained, connect well with the students, and are good at resolving your questions. The course content is excellent. In the DS course, for instance, they have gone through the right amount of statistics and linear algebra.


Generative AI Courses FAQs

  • 1. What Is Generative AI?

    A subset of artificial intelligence (AI), generative AI processes existing data to create newer, unique outputs in video, audio, text, 3D models, images, or as required.

    With advancing generative models, generative AI tools can produce complex content, solve problems, create art, and even assist in research. The most recent breakthroughs which have brought generative AI to the forefront are GPT and Midjourney.

  • 2. What Is The Difference Between Generative AI and AI?

    AI or artificial intelligence is a machine's capability to perform cognitive functions like the human brain, such as learning, reasoning, interacting, and problem-solving. Traditional AI, or conventional AI or artificial general intelligence, performs tasks according to preset rules.

    The most common uses of AI technology include search engines, stock trading, medical diagnosis, etc.

    Generative AI, on the other hand, uses existing data for fresh content creation. This could mean image generation, text description, or video creation, similar to the training data.

  • 3. How Does Generative AI Work?

    Generative AI uses neural networks to identify patterns or structures in the input data supplied by human intelligence. The learning could be supervised, semi-supervised, or unsupervised to train AI models. 

    Unsupervised learning enables generative models to process unlabeled data, saving time and creating foundation models. These foundation models are then used as a base for generative models.

    Once the generative AI systems process the training data, the generative models produce fresh content. This could be in the form of generating images, videos, text, etc.

  • 4. What Are The Benefits Of Generative AI?

    Generative AI is beneficial as it helps:

    • Create new, original content similar to human-generated content. This finds application in different entertainment industries. 
    • Improve existing AI models.
    • Analyze complex data and make predictions to help improve business processes and business functions.
    • Automate tasks, therefore saving resources and time.

  • 5. What Are The Different Types of Generative AI Models?

    Generative AI is most commonly distinguished into three types:

    • Transformer Generative AI models

    These neural networks, generally used for NLP tasks, process sequential data and identify relationships. These are the basis for most foundation models.

    • Generative Adversarial Networks

    This generative AI uses two neural networks to produce realistic content, finding application in art and content creation.

    • Variational Autoencoders

    This generative AI finds patterns in a dataset by compressing it into a lower-dimensional space. Further, the AI system learns to generate data by sampling the compressed space. 

  • 6. What Is The Role Of Training Data In AI Models?

    Training data refers to the data that is given as input to generative AI models. This data is analyzed, processed, and used to create neural networks, based on which the generative AI further performs its tasks.

  • 7. Why Should One Learn Generative AI?

    Generative AI is constantly growing, with predictions showing its rise from 1% to 10% in the next ten years. According to Bloomberg Intelligence, the generative AI market can reach $41.3 trillion by 2032 at a CAGR of 42%. Since AI learning is finding its application in multiple industries, with more and more big players adopting it for the growth of their companies, the demand for generative AI models is bound to increase. Moreover, to make tasks quicker and easier, generative AI is handy. However, generative AI is only as good as the tasks and prompts it is commanded with. Therefore, learning generative AI to use it properly and even create new generative AI tools is vital.

  • 9. Will Generative AI Take Up People's Jobs?

    Generative AI is highly useful in automating tasks and processing complex data that human minds cannot comprehend. However, generative AI tools and models can only be created with human help.

    Moreover, most generative AI models require human assistance in the form of assigning tasks and prompts. Therefore, as generative AI expands, so will the need for employees well-versed in generative AI tools.

    Generative AI, therefore, is a chance to create a symbiotic relationship with artificial intelligence, helping improve an employee's work range and efficiency.

  • 10. What Are The Real World Applications Of Generative AI?

    Generative AI is slowly being applied in multiple fields, including medicine, engineering, and business. With speech generation, predictability models, and other forms of generative AI, its uses are widespread, including:

    1. Storyline Generation: New characters, storylines, plot twists, content ideas, etc., can be formed using Generative AI.

    2. Video Games: It is now possible to create landscapes, characters, and even narratives for video games with the help of Generative AI.

    3. Music: Generative AI can be used to compose fresh music that is in line with the artist’s style.

    4. Image Synthesis: Generative AI helps create realistic images for art, graphics, design departments, etc.

    5. Text Generation: Generative AI can produce text for chatbots, language translation, virtual assistants, and content generation for media.

    6. Data Augmentation: By creating synthetic data, generative AI helps in the development of other machine learning models.

    7. Medicine: Generative AI is used in medical imaging and in drug discovery by generating new molecular structures.

    8. Product Designs: Finding applications in architecture and engineering, generative AI can help explore and test different design variations.

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