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Generative AI for Application Development

Overview

At Canadian College Of Business Health And Arts, we recognize and harness the transformative power of customized guidance and strategic insight to propel both individuals and organizations towards their objectives.

Whether you are a seasoned executive or a budding entrepreneur, our services are meticulously crafted to meet you at your current stage and facilitate your progress toward your aspirations.

Acknowledging the unique landscape of each business, along with its distinct challenges and opportunities, we adopt a tailored approach, collaborating closely with you to devise strategies that directly address your specific requirements and foster substantial advancement.

Our mission is to empower you with the essential tools, knowledge, and confidence necessary to navigate complex business environments and attain sustainable success. Our commitment transcends conventional coaching techniques, integrating an array of proven methods and innovative practices to provide comprehensive support.

From strategic planning and leadership development to enhancing team dynamics and performance, our expertise encompasses diverse facets of business management. We take pride in cultivating a collaborative and supportive atmosphere where you are encouraged to explore new concepts, overcome challenges, and unlock your full potential.

With a history of success and an unwavering dedication to excellence, we stand ready to partner with you on your journey toward growth and achievement, transforming your vision into reality and laying the groundwork for enduring success.

What You’ll Learn

In this course, you’ll gain hands-on experience and deep knowledge in applying generative AI techniques to build cutting-edge applications. Here’s what you can expect to learn:

Foundations of Generative AI:

    • Understand the core principles behind generative models like GANs, VAEs, and transformers.
    • Learn the evolution of generative AI and its impact across various industries.
  2. Text Generation and Natural Language Processing (NLP):
    • Master techniques for generating human-like text with advanced NLP models (e.g., GPT-3).
    • Learn to build and fine-tune models for applications like chatbots, automated content creation, and virtual assistants.
  3. Image and Media Creation with AI:
    • Explore image generation techniques using GANs and models like StyleGAN and DALL·E.
    • Create custom images and artwork based on textual prompts or existing datasets, and apply them to real-world design and creative tasks.
  4. Audio and Video Synthesis:
    • Dive into AI-powered audio and video generation, including deepfake technology and speech synthesis.
    • Build systems that can create or alter multimedia content, such as AI-generated music or realistic video generation.
  5. Building End-to-End Generative AI Solutions:
    • Learn how to design and develop a full generative AI pipeline, from data collection and model training to deployment and maintenance.
    • Gain skills in integrating generative models into live applications, ensuring scalability and performance.
  6. Ethics and Best Practices in Generative AI:
    • Explore the ethical implications of generative AI, including issues related to deepfakes, bias, and security.
    • Learn best practices to mitigate ethical concerns and responsibly deploy generative AI systems.
  7. Hands-On Application Development:
    • Apply your learning in building real-world generative AI applications like AI-driven creative tools, automated content generators, and interactive user experiences.
    • Develop an understanding of how to optimize, deploy, and monitor generative models in production environments.
  8. Capstone Project:
    • Put everything you’ve learned into practice by creating a generative AI application of your own design, solving real business or creative problems.
    • Showcase your project, demonstrate its value, and gain feedback from industry professionals.

By the end of this course, you’ll be equipped with the skills to develop innovative generative AI applications that can transform industries, from creative arts to business automation.

Job Opportunities:

  • Generative AI Developer.
  • AI Solutions Architect.
  • AI Researcher (Focusing on generative models).
  • Data Scientist specializing in AI and creative applications.
  • Content Automation Specialist using generative AI.

Next Possible Courses:

  • Deep Learning for AI Applications.
  • Advanced Natural Language Processing.
  • AI Ethics and Security.
  • AI and Creativity in Business.

 Additional Highlights:

  • Access to industry-standard tools and frameworks like TensorFlow, PyTorch, and OpenAI API.
  • Networking opportunities with professionals in AI and tech.
  • Certificate of Completion showcasing expertise in generative AI.

Popularity Rationale: Generative AI is revolutionizing industries by enabling machines to create content such as text, images, and even music. With advancements in models like GPT-4 and DALL·E, businesses are leveraging generative AI to streamline operations, enhance creativity, and drive innovation. This course delves into how generative AI can be applied across various fields, empowering learners to harness its potential for problem-solving, content creation, and business growth.

Target Audience:

  • AI enthusiasts looking to explore generative models.
  • Data scientists interested in cutting-edge AI techniques.
  • Developers seeking to integrate AI into applications.
  • Business leaders and product managers aiming to innovate using AI.
  • Students and researchers exploring AI for creative and practical applications.

Chapters

Module 1: Introduction to Generative AI (4 hours)

  • Content:
    • Overview of generative AI and its applications.
    • History of generative models: from GANs to diffusion models.
    • Key concepts in neural networks and deep learning.
  • Module Objectives (MOs):
    1. Grasp the foundational concepts of generative AI.
    2. Explore the evolution of generative models.
    3. Understand the core algorithms powering generative AI.

Module 2: Generative Models: GANs and VAEs (6 hours)

  • Content:
    • Introduction to GANs (Generative Adversarial Networks).
    • Working principles behind VAEs (Variational Autoencoders).
    • Comparison and practical use cases.
  • Module Objectives (MOs):
    1. Implement basic GAN and VAE models.
    2. Compare the strengths and weaknesses of GANs vs VAEs.
    3. Explore real-world applications of these models, such as in image generation and data augmentation.

Module 3: Text Generation with AI (6 hours)

  • Content:
    • Understanding transformer models (e.g., GPT-3, BERT).
    • Techniques in natural language processing (NLP) for text generation.
    • Hands-on exercises for training text generation models.
  • Module Objectives (MOs):
    1. Build and fine-tune generative models for text creation.
    2. Develop applications for text generation, such as chatbots and content creation tools.
    3. Evaluate the effectiveness of generative models in various NLP tasks.

Module 4: Image Generation with AI (6 hours)

  • Content:
    • Deep dive into image generation with GANs and other models.
    • Exploring tools like DALL·E and StyleGAN.
    • Practical exercises for creating and refining AI-generated images.
  • Module Objectives (MOs):
    1. Generate realistic images using AI models.
    2. Understand the underlying architecture of image generation models.
    3. Apply generative techniques to real-world image creation tasks, such as art, design, and advertising.

Module 5: Advanced Applications of Generative AI (6 hours)

  • Content:
    • Audio generation and deepfake technology.
    • Combining multiple types of media (text, image, audio) for complex AI applications.
    • Ethics, bias, and security concerns in generative AI.
  • Module Objectives (MOs):
    1. Generate audio and video using AI techniques.
    2. Address ethical considerations and mitigate biases in generative models.
    3. Explore security issues surrounding AI-generated content.

Module 6: Implementing a Full Generative AI Workflow (5 hours)

  • Content:
    • Building a complete generative AI pipeline from data collection to model training and deployment.
    • Practical examples: creating a generative art app, AI-based content generator, etc.
    • Deployment strategies for generative AI models.
  • Module Objectives (MOs):
    1. Design and implement a full generative AI solution.
    2. Explore model optimization and deployment techniques.
    3. Deploy generative models in a cloud or production environment.

Module 7: Capstone Project: Building a Generative AI Application (3 hours)

  • Content:
    • Conceptualizing and creating a project that leverages generative AI.
    • Presenting a working prototype and demonstrating its value.
    • Peer review and constructive feedback session.
  • Module Objectives (MOs):
    1. Build a generative AI-based application.
    2. Showcase your project through a working demonstration.
    3. Communicate technical concepts and business value to stakeholders.
Students are required to complete 39 hours of in-class training.
Subject/Module Outline For Each Subject In the Program

39 hours
Remaining Hours (Total Program Instruction Hours minus Total Instruction Hours Entered. Will be populated as Subject Instruction Hours are entered below):
0.00

Program Summary

This table will display a summary of total instructional hours, delivery format and percentage weight for every Type of Learning entered in the List of Subjects. The ministry, career college and any subject or education assessors may refer to this section for a general understanding of the components of the program.

Type of Learning Total Instruction Hours Delivery Format % Weight
Theory 20.00 On-Line 58.67%
Practical 19.00 On-Line 41.33%
Total Type of Learning 39.00
Total Program Hours 39.00 100.00%

$CAD

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Details

Certification

39 Hours

Updated: 03/01/2025

Prerequisites

If you attended a post-secondary institution after high school, we need both your high school transcript and your post-secondary transcript.

  • Entry into many of our programs is competitive and your post-secondary marks can strengthen your application.
  • If you did not graduate from high school and have a GED, send us a copy.
  • Even if you graduated a while ago, we still need your high school transcript.
  • If you cannot obtain it, let us know and we can discuss your options. 
  • If you studied at college or university, you must submit your post-secondary transcript AND
    your high-school transcript. This may strengthen your application.
  • Basic understanding of machine learning concepts.
  • Familiarity with Python programming and libraries like TensorFlow or PyTorch.
  • Understanding of neural networks and deep learning fundamentals.
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Email

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Address

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