Description for GenAI An Introductory Course
- Acquire the ability to employ Generative AI for automation.
- Create software solutions that utilize generative AI.
- Develop solutions that optimize the output of Generative AI through the implementation of Prompt Engineering.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Duke University
Duration: 37 hours (approximately)
Schedule: Flexible
Pricing for GenAI An Introductory Course
Use Cases for GenAI An Introductory Course
FAQs for GenAI An Introductory Course
Reviews for GenAI An Introductory Course
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for GenAI An Introductory Course
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.
Acquire an extensive understanding of reinforcement learning, deep neural networks, clustering, and dimensionality reduction to effectively address real-world machine learning challenges.
Develop your skills in image processing, augmented reality, and object recognition to prepare yourself to create cutting-edge AI-powered visual apps.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.
In-depth study of the applications of machine learning and computer vision, as well as practical experience in data analysis and Python programming.
A program that emphasizes the practical implementation of data science and machine learning to overcome obstacles through the use of Python.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Featured Tools
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Develop your skills in image processing, augmented reality, and object recognition to prepare yourself to create cutting-edge AI-powered visual apps.
This course covers Python programming, TensorFlow for linear regression, and app development for stock market prediction.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.