Generative Ai For Data Science Training Course: Augment Data Workflows

Introduction

Revolutionize your data science processes with our Generative AI for Data Science Training Course. This program is designed to equip you with the essential skills to use generative AI to augment and improve data science workflows, enabling you to create synthetic data, automate tasks, and enhance model performance. In today's rapidly evolving AI landscape, mastering generative AI is crucial for staying competitive and pushing the boundaries of data science. Our generative AI training course offers hands-on experience and expert guidance, empowering you to integrate cutting-edge AI techniques into your projects.

This augment data workflows training delves into the core concepts of generative AI, covering topics such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs). You'll gain expertise in using industry-standard tools and techniques to use generative AI to augment and improve data science workflows, meeting the demands of modern data-driven organizations. Whether you're a data scientist, machine learning engineer, or AI researcher, this Generative AI for Data Science course will empower you to build and deploy innovative AI solutions.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Data Analysts
• Software Developers
• Data Engineers
• Anyone needing generative AI skills

Course Objectives:

• Understand the fundamentals of generative AI for data science.
• Master generative adversarial networks (GANs) for synthetic data generation.
• Utilize variational autoencoders (VAEs) for latent space modeling.
• Implement large language models (LLMs) for data augmentation and analysis.
• Design and build generative AI models for data science tasks.
• Optimize generative AI models for performance and efficiency.
• Troubleshoot and address common challenges in generative AI applications.
• Implement generative AI techniques for data augmentation and synthetic data generation.
• Integrate generative AI with real-world data science workflows.
• Understand how to handle ethical considerations and biases in generative AI.
• Explore advanced generative AI techniques (e.g., diffusion models, transformer-based generators).
• Apply real world use cases for generative AI in data science.
• Leverage generative AI libraries and tools for efficient development.

Duration

10 Days

Course content

Module 1: Introduction to Generative AI for Data Science

• Fundamentals of generative AI for data science.
• Overview of GANs, VAEs, and LLMs.
• Setting up a generative AI development environment.
• Introduction to generative AI libraries and tools.
• Best practices for generative AI in data science.

Module 2: Generative Adversarial Networks (GANs)

• Implementing GANs for synthetic data generation.
• Utilizing different GAN architectures (DCGAN, StyleGAN).
• Designing and building GAN models for specific data types.
• Optimizing GAN training and evaluation.
• Best practices for GANs.

Module 3: Variational Autoencoders (VAEs)

• Implementing VAEs for latent space modeling.
• Utilizing VAEs for data generation and anomaly detection.
• Designing and building VAE models for data exploration.
• Optimizing VAEs for latent space representation.
• Best practices for VAEs.

Module 4: Large Language Models (LLMs)

• Implementing LLMs for data augmentation and analysis.
• Utilizing transformer-based LLMs (GPT, BERT).
• Designing and building LLM-based data processing pipelines.
• Optimizing LLMs for natural language data tasks.
• Best practices for LLMs.

Module 5: Generative AI Models for Data Science Tasks

• Designing and building generative AI models for data science tasks.
• Implementing generative AI for data imputation and anomaly detection.
• Utilizing generative AI for feature generation and data synthesis.
• Optimizing models for specific data science applications.
• Best practices for model design.

Module 6: Generative AI Model Optimization

• Optimizing generative AI models for performance and efficiency.
• Utilizing hyperparameter tuning and model compression.
• Implementing distributed training and inference.
• Designing scalable generative AI solutions.
• Best practices for model optimization.

Module 7: Troubleshooting Generative AI Challenges

• Debugging common challenges in generative AI applications.
• Analyzing model performance and stability.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common generative AI issues.
• Best practices for troubleshooting.

Module 8: Generative AI for Data Augmentation

• Implementing generative AI techniques for data augmentation.
• Utilizing GANs and VAEs for creating synthetic data.
• Designing and building data augmentation pipelines.
• Optimizing augmentation for model training.
• Best practices for data augmentation.

Module 9: Integration with Data Science Workflows

• Integrating generative AI with real-world data science workflows.
• Utilizing APIs and deployment tools.
• Implementing generative AI in data processing and model training.
• Optimizing integration for business impact.
• Best practices for integration.

Module 10: Ethical Considerations and Biases

• Implementing ethical considerations and bias mitigation in generative AI.
• Utilizing fairness metrics and bias detection techniques.
• Designing and building ethical generative AI applications.
• Optimizing data handling for ethical compliance.
• Best practices for ethics.

Module 11: Advanced Generative AI Techniques

• Exploring advanced generative AI techniques (diffusion models, transformer-based generators).
• Utilizing diffusion models for high-quality data generation.
• Implementing transformer-based generators for sequential data.
• Designing and building advanced generative AI solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing generative AI for synthetic medical data generation.
• Utilizing generative AI for financial fraud detection.
• Implementing generative AI for image and video synthesis.
• Utilizing generative AI for text data augmentation and summarization.
• Best practices for real-world applications.

Module 13: Generative AI Libraries and Tools Implementation

• Utilizing TensorFlow, PyTorch, and Hugging Face Transformers.
• Implementing generative AI models with libraries.
• Designing and building generative AI pipelines.
• Optimizing library usage for efficient development.
• Best practices for library implementation.

Module 14: Model Evaluation and Monitoring

• Implementing model evaluation and monitoring for generative AI.
• Utilizing evaluation metrics and visualization tools.
• Designing and building monitoring dashboards.
• Optimizing monitoring for proactive issue detection.
• Best practices for monitoring.

Module 15: Future Trends in Generative AI for Data Science

• Emerging trends in generative AI for data science.
• Utilizing multimodal generative AI models.
• Implementing generative AI for automated data analysis.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.