Fundamentals of Gen AI for Beginners
Gain a foundational understanding of generative AI, including its functions, key concepts like large language models, datasets, and prompts, and the components used to build and operate AI solutions.
Description for Fundamentals of Gen AI for Beginners
Features of Course
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Amazon Web Services
Duration: 3 weeks at 1 hour a week
Schedule: Flexible
Pricing for Fundamentals of Gen AI for Beginners
Use Cases for Fundamentals of Gen AI for Beginners
FAQs for Fundamentals of Gen AI for Beginners
Reviews for Fundamentals of Gen AI for Beginners
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Fundamentals of Gen AI for Beginners
The AI tool specializes in sentiment analysis, competitive analysis, custom analytics, Amazon marketplace analysis, review export, comprehensive help resources, and social media presence to meet diverse user needs effectively.
The AI tool enables organizations to create personalized multi-channel experiences for their clientele, featuring audience segmentation and a user-friendly platform with a complimentary 14-day trial and enterprise pricing options.
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Utilizing AI technology, this tool streamlines statistical analysis tasks, automates calculations, supports various data formats, and provides visualization tools for efficient and precise scientific research.
The AI generator, drawing from various sources, facilitates user interaction to produce content, making it beneficial for startups and individuals seeking to explore and enhance their knowledge across different subjects.
The tool employs AI to assist users in understanding intricate documents, offering features such as content analysis, summarization, and language analysis, with plans for further enhancements.
The AI tool utilizes advanced technology to streamline product research and feedback analysis, offering quick insights, collaborative opportunities, integration options, a user-friendly interface, a free tier option, and team collaboration features.
CensusGPT is an AI tool that simplifies access to census data, offering tabular data and visual representations in response to user queries. It targets economists, researchers, and individuals interested in demographic analysis, leveraging the TextSQL framework for seamless interaction with datasets.
The AI Task Manager simplifies project management through features like project cost calculation, automated scheduling, data analysis, and user-friendly interface, enabling efficient planning and timely project completion.
Breadcrumb.ai swiftly converts data into interactive presentations, reports, and interfaces, leveraging AI for intuitive insights exploration and seamless integration with various data sources, facilitating quick decision-making.
Featured Tools
Construct the gradient descent algorithm, execute univariate linear regression with NumPy and Python, and create data visualizations with matplotlib.
Staffing, planning, and executing projects, creating product bills of materials, validating and calibrating sensors, and comprehending solid state and hard drives are covered in the course.
Accelerate your career in data analytics. In this certificate program, you will acquire skills that are in high demand at your own tempo, regardless of your degree or experience.
Learn to select optimal deployment and monitoring patterns, optimize model performance, and address production challenges across various data types while enhancing label consistency.
The course's topics including the distinction between deep learning, machine learning, and artificial intelligence, the process of developing machine learning models, the difference between supervised and unsupervised learning, and the use of metrics for evaluating classification models.