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
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.
Learn to develop, train, and assess neural networks using TensorFlow to resolve classification issues by understanding the fundamental principles of neural networks.
Learn to perform inferential statistical analysis, assess and improve data visualizations, integrate machine learning into data analysis, and analyze social network connectivity.
Using Vertex AI and BigQuery ML, the course instructs students on how to improve data quality, construct AutoML models, and optimize models using performance metrics.
Learn to develop a text processing pipeline and understand LSTM and Recurrent Neural Networks to train and assess deep learning models.