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Expected size of Global Generative AI Market by 2030.
Average salary of a Machine Learning Engineer annually.
In this course, you'll explore the fundamentals of Generative AI, covering its principles and practical applications. You'll learn planning a generative AI project step by step, understanding the key stages involved. Through clear examples and discussions, you'll also address the potential risks associated with Generative AI projects and effective ways to mitigate them. By the end of the course, you'll feel confident in your ability to plan and execute Generative AI projects successfully.
A generative AI project involves leveraging algorithms to generate extensive content, providing insights and answers in diverse formats such as text, images, and user-friendly interfaces.
The initial steps in planning a generative AI project entail meticulous data collection and preprocessing. This involves tasks like data cleaning, normalization, and tokenization, alongside algorithm selection, environment setup, and prototype development for testing purposes.
In this free planning a generative AI project course on planning a generative AI project, you will gain insights into crucial stages such as defining the problem, collecting and preprocessing data, selecting suitable algorithms and models, training and fine-tuning these models, evaluating results, and effectively deploying solutions.
The Planning a generative AI project course comprehensively covers an array of tools and technologies essential for generative AI projects, including popular data preprocessing libraries, machine learning frameworks, and deployment platforms, ensuring a well-rounded understanding of the ecosystem.
The key stages of planning a generative AI project encompass defining the problem statement, meticulous data collection and preprocessing, judicious selection of algorithms and models, iterative training and fine-tuning, thorough evaluation, and seamless deployment in real-world scenarios.
This course proactively addresses bias in AI models by discussing effective techniques for detecting and mitigating bias across data, algorithms, and model outputs. It emphasizes ethical considerations, ensuring responsible AI development practices.