Artificial Intelligence
- 3 Years
- Intakes: Jan, Apr, Jun, Oct
Overview
This course fills in as a prologue to the principal ideas and procedures of Computerized reasoning (simulated intelligence). It covers a large number of subjects connected with AI, normal language handling, PC vision, and critical thinking.
Course Goals
- Grasping Artificial Intelligence Establishments:
- An overview of AI’s development and history.
- comprehending AI’s objectives and challenges.
AI Fundamentals
- An overview of both supervised and unsupervised instruction.
- An overview of the classification and regression algorithms.
- hands-on experience training models for machine learning.
- Learning by doing:
- Prologue to brain organizations and profound learning.
- Understanding convolutional and repetitive brain organizations.
- Deep learning’s practical applications in speech and image recognition
- Regular Language Handling (NLP):
- Nuts and bolts of NLP and its applications.
- Message preprocessing, opinion examination, and language interpretation.
- Prologue to chatbots and remote helpers.
PC Vision
a foundation in image processing.
Object recognition and picture characterization utilizing PC vision.
Utilizations of PC vision in certifiable situations.
Critical thinking and man-made intelligence Morals:
AI-based strategies for solving problems.
Moral contemplations in man-made intelligence improvement and sending.
Ethical dilemmas are highlighted in case studies.
Advanced mechanics and man-made intelligence Applications:
Prologue to mechanical technology and its association with artificial intelligence.
AI’s practical applications in various sectors
Arising patterns and future bearings in artificial intelligence.
Real-world projects
Execution of simulated intelligence calculations utilizing famous programming dialects (e.g., Python).
Creating and conveying simulated intelligence models in genuine situations.
Appraisals and Assessment
Tests, tasks, and a last venture.
assessment of practical abilities to apply AI concepts.
Prerequisites :
Fundamental programming abilities (ideally in Python), comprehension of essential science (straight variable based math, likelihood), and a solid interest in man-made brainpower.
Suggested Reading material
“Man-made reasoning: Stuart Russell and Peter Norvig’s “A Modern Approach”
Aurélien Géron’s “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”
Appraisal Technique
- Tests and tasks: 40% of the Project 30%
- Last test of the year: 30%
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Consultation
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PIN CODE: 492001
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- You Apply
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- You Get Ready
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