AI and Intelligent Systems
- Instructor : Satwinder
- Duration : 3 Months
- Language : English
- Certificate : Yes
- Access : Lifetime
Overview
This course covers the fundamentals and advanced concepts of artificial intelligence (AI) and machine learning. Students will learn to develop intelligent systems that can perform tasks such as data analysis, pattern recognition, and decision making.
This course delves into the fundamentals and advanced principles of artificial intelligence (AI) and machine learning, equipping students with skills to develop intelligent systems. Beginning with core AI concepts, students will explore supervised and unsupervised learning, neural networks, and deep learning models. They will gain hands-on experience in data analysis, enabling machines to recognize patterns and make informed decisions.
Through practical applications, students will learn how to apply algorithms to real-world problems, from predictive modeling to image and speech recognition.
How much you learn each ?
Practice
Introduction to this Course
Python: Primary language used for AI and machine learning.
R: Statistical programming for AI-related data analysis.
Supervised Learning:Training models on labeled data.
Unsupervised Learning: Finding hidden patterns in unlabeled data.
Reinforcement Learning: Training agents to make decisions by rewarding desirable actions.
Neural Networks: Framework for creating complex models.
Convolutional Neural Networks (CNNs): Models for image recognition.
Recurrent Neural Networks (RNNs): Models for sequential data analysis.
TensorFlow & Keras: Libraries for building and training deep learning models.
PyTorch: Deep learning framework providing flexibility and speed.
NLTK & SpaCy: Libraries for processing and analyzing human language data.
BERT: Model for understanding the nuances of language in context