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
Post Graduate Program in Digital Marketing

Cohort Starts: 29 Feb, 2024

8 Months$ 3,000
Post Graduate Program in Data Analytics

Cohort Starts: 5 Mar, 2024

8 Months$ 3,749
Post Graduate Program in AI and Machine Learning

Cohort Starts: 14 Mar, 2024

11 Months$ 4,800
Post Graduate Program in Project Management

Cohort Starts: 15 Mar, 2024

6 Months$ 3,000
Post Graduate Program in Lean Six Sigma

Cohort Starts: 18 Mar, 2024

6 Months$ 3,000
Post Graduate Program in Business Analysis

Cohort Starts: 18 Mar, 2024

6 Months$ 3,499
AI & Machine Learning Bootcamp

Cohort Starts: 18 Mar, 2024

6 Months$ 10,000
Post Graduate Program in Data Science

Cohort Starts: 25 Mar, 2024

11 Months$ 4,199
AI and Machine Learning Bootcamp - UT Dallas6 Months$ 8,000

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Generative AI Courses Learner's Reviews

  • Filipe Theodoro

    Filipe Theodoro

    Machine Learning Engineer

    This course gave me the basic knowledge required to start building my own models, from organizing and selecting the data to run and testing the models. Also, the trainers were very clear when explaining and gave us lots of tips.

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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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