TCS NPT Artificial Intelligence Course
The TCS NPT Artificial Intelligence (AI) course is a comprehensive learning program designed to build foundational knowledge and practical expertise in the field of AI. It aims to equip learners with the skills necessary to understand and apply AI concepts effectively. This course is divided into two sections: Test of Knowledge and Test of Application, ensuring a balanced approach to theoretical learning and practical implementation.
What You Will Learn with the TCS NPT Artificial Intelligence Course?
This course offers an in-depth understanding of key aspects of Artificial Intelligence, including its fundamentals, applications, and advanced concepts. It is structured to help participants develop both a theoretical understanding and real-world problem-solving skills. The syllabus covers core topics such as Neural Networks, Machine Learning (ML), Deep Learning, Reasoning, Search Algorithms, Time Series Analysis, and TensorFlow. By the end of the course, learners will have a solid foundation in AI concepts and their practical applications.
TCS NPT Artificial Intelligence - Overview
Artificial Intelligence is driving innovation across industries by enabling systems to replicate human intelligence. The TCS NPT Artificial Intelligence course provides a thorough introduction to the field, covering essential areas such as:
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Introduction to Artificial Intelligence (AI)
- Importance: AI is transforming how industries operate by automating complex tasks and providing intelligent solutions.
- Learning Focus: Basic concepts of AI, including terminology, architecture, and applications.
- Skills Covered: Understanding Neural Networks, their architecture, and types.
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Search Algorithms
- Importance: Search algorithms form the backbone of AI, enabling efficient problem-solving.
- Learning Focus: Search techniques, such as Constraint Satisfaction Problems, Intelligent Agents, and Heuristic Search.
- Skills Covered: State Space Search, Adversarial Problem Solving, and Directed Search.
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Reasoning
- Importance: Logical reasoning allows AI systems to make decisions and draw conclusions.
- Learning Focus: Logical reasoning, proposition and first-order logic, and handling uncertainty using probabilistic methods.
- Skills Covered: Deduction, Semantic Networks, Belief Networks, and Data Analytics.
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Machine Learning (ML)
- Importance: ML enables systems to learn and improve from data without explicit programming.
- Learning Focus: Fundamentals of supervised and unsupervised learning, and advanced algorithms.
- Skills Covered: Linear regression, K-means clustering, SVM, and ensemble classifiers.
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Deep Learning
- Importance: Deep Learning drives advancements in AI by enabling neural networks to perform complex tasks.
- Learning Focus: Training and optimizing neural networks using techniques like backpropagation, regularization, and dropout.
- Skills Covered: Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP).
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Time Series Analysis
- Importance: Analyzing sequential data is crucial for forecasting and decision-making.
- Learning Focus: Time Series modeling, ARIMA, and signal transformations.
- Skills Covered: Stationary and smoothing techniques, autocorrelation functions, and data representation.
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TensorFlow
- Importance: TensorFlow is a key framework for implementing AI and ML solutions.
- Learning Focus: Basics of TensorFlow, and its application in ML/DL tasks like clustering and regression.
- Skills Covered: Building CNNs and implementing AI algorithms using TensorFlow.
TCS NPT Artificial Intelligence Exam Pattern
The TCS iON NPT Artificial Intelligence Test is divided into two parts:
- Part A: Test of Knowledge
- Part B: Test of Application
Part A: Test of Knowledge
- Total Questions: 50
- Marks per Question: 1
- Total Marks: 50
- Duration: 60 minutes
TCS NPT AI Exam Syllabus for Part A
Sr. No. |
Module |
Topics |
Subtopics |
Weightage |
1 |
Introduction to AI |
Basics of AI |
Terminologies, Architecture, Applications |
100% |
2 |
Search Algorithms |
Search Techniques |
Heuristic Search, Adversarial Problems |
100% |
3 |
Reasoning |
Logical Reasoning |
Probabilistic Reasoning, Belief Networks |
100% |
4 |
Machine Learning |
Supervised/Unsupervised Learning |
Regression, SVM, K-means, Ensemble Models |
100% |
5 |
Deep Learning |
Neural Networks |
CNNs, Backpropagation, NLP |
100% |
Part B: Test of Application
- Total Questions (Case Studies): 6
- Total Marks: 50
- Duration: 60 minutes
TCS NPT AI Exam Syllabus for Part B
Case No. |
No. of Questions |
Total Marks |
Module Coverage |
Weightage |
Case 1 |
2 |
20 |
Introduction to AI, Search |
40% |
Case 2 |
4 |
30 |
Reasoning, ML, DL, TensorFlow |
60% |
This structured learning approach ensures participants gain a deep understanding of Artificial Intelligence and its applications, preparing them for real-world AI challenges.