GenAI for Data Scientists
Master the intricacies of Generative AI with our specialized course designed for data scientists. Engage with expert-led sessions that include hands-on experience in building and optimizing generative models. Enroll today to harness the power of Generative AI for transformative data analysis and innovative business solutions.
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Training Provider Pricing
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What you'll learn
- Implement best practices in data preprocessing for training generative models
- Understand the foundational concepts and impact of Generative AI in data science
- Learn to use key tools and platforms like TensorFlow, PyTorch, and Hugging Face
- Fine-tune and optimize generative models for specific data science tasks
- Integrate Generative AI into existing workflows to enhance decision-making processes
- Build basic to advanced generative models through guided hands-on exercises
- Explore ethical considerations and address potential biases in model deployment
- Complete a capstone project applying learned skills to a real-world generative AI challenge
Why should you attend?
This course offers a deep dive into the realm of Generative AI, focusing on its application within the field of data science. Starting with an introduction to Generative AI technologies, students will explore the significant impact these technologies have on data science and understand how they differ from traditional models. The program progresses to cover essential tools and platforms such as TensorFlow, PyTorch, JAX, Hugging Face, OpenAI, Anthropic, and Meta, which support the development of Generative AI solutions. As the course advances, participants will engage in practical exercises including data handling and preprocessing tailored for generative models. This includes best practices for data collection and cleaning as well as creating synthetic datasets. Hands-on labs provide step-by-step guidance on building simple generative models with applications in text and image generation. Further exploration into advanced models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) is included, highlighting their use in synthetic data generation for training machine learning models. The latter part of the course addresses fine-tuning techniques, ethical considerations, limitations of generative AI, and its integration into existing data pipelines. Participants will also learn about scaling generative models for larger datasets and high-performance computing resources. The curriculum culminates in a capstone project where participants apply their acquired skills to solve a real-world problem using generative AI, alongside discussions on future trends in this rapidly evolving field.
Course Syllabus
Day 1 - GenAI Fundamentals & Models
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Business Applications & Integration
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Ratings and Reviews
FAQs
Why should you attend?
This course offers a deep dive into the realm of Generative AI, focusing on its application within the field of data science. Starting with an introduction to Generative AI technologies, students will explore the significant impact these technologies have on data science and understand how they differ from traditional models. The program progresses to cover essential tools and platforms such as TensorFlow, PyTorch, JAX, Hugging Face, OpenAI, Anthropic, and Meta, which support the development of Generative AI solutions. As the course advances, participants will engage in practical exercises including data handling and preprocessing tailored for generative models. This includes best practices for data collection and cleaning as well as creating synthetic datasets. Hands-on labs provide step-by-step guidance on building simple generative models with applications in text and image generation. Further exploration into advanced models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) is included, highlighting their use in synthetic data generation for training machine learning models. The latter part of the course addresses fine-tuning techniques, ethical considerations, limitations of generative AI, and its integration into existing data pipelines. Participants will also learn about scaling generative models for larger datasets and high-performance computing resources. The curriculum culminates in a capstone project where participants apply their acquired skills to solve a real-world problem using generative AI, alongside discussions on future trends in this rapidly evolving field.
What you'll learn
- Implement best practices in data preprocessing for training generative models
- Understand the foundational concepts and impact of Generative AI in data science
- Learn to use key tools and platforms like TensorFlow, PyTorch, and Hugging Face
- Fine-tune and optimize generative models for specific data science tasks
- Integrate Generative AI into existing workflows to enhance decision-making processes
- Build basic to advanced generative models through guided hands-on exercises
- Explore ethical considerations and address potential biases in model deployment
- Complete a capstone project applying learned skills to a real-world generative AI challenge
Course Syllabus
Day 1 - GenAI Fundamentals & Models
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Business Applications & Integration
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Public Pricing
Corporate Pricing
Pax:
Training Provider Pricing
Pax:
Features
Subsidies
Ratings and Reviews
FAQs
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