Gen AI Applications and Popular Tools
The "Generative AI Applications and Popular Tools" course provides a comprehensive exploration of chatbot technology and popular Generative AI tools. It targets a diverse audience interested in enhancing their skills in these areas, offering accessibility to both beginners and professionals, regardless of prior knowledge in AI and programming.
Description for Gen AI Applications and Popular Tools
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Edureka
Duration: 11 hours (approximately)
Schedule: Flexible
Pricing for Gen AI Applications and Popular Tools
Use Cases for Gen AI Applications and Popular Tools
FAQs for Gen AI Applications and Popular Tools
Reviews for Gen AI Applications and Popular Tools
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Gen AI Applications and Popular Tools
Learn the Rasa framework to create AI-powered chatbots, which is suitable for Python programmers who are new to chatbot development and lack prior machine learning experience. This course covers the fundamental components and practical applications.
Featured Tools
Master Python programming for software development and data science, including core logic, Jupyter Notebooks, libraries like NumPy and Pandas, and web data gathering with Beautiful Soup and APIs.
Besides Python programming and data science fundamentals, the course covers supervised machine learning regression, which includes training models for continuous outcomes, error metrics, Elastic Net, LASSO, Ridge regularization, and data science fundamentals for aspiring data scientists.
Confidently navigate the realm of data. Acquire the necessary skills in AI, scientific reasoning, and data analysis to facilitate informed decision-making.
This course instructs students on the Rhyme platform of Coursera, where they will evaluate random forest classifiers using Yellowbrick, address class imbalance, and conduct feature analysis with regression, cross-validation, and hyperparameter optimization.
This course equips students with the necessary business leadership skills and technical knowledge to propel the success of ML.