Attribute

Detail

Format

Online (e-LMS)

Level

Beginner

Duration

8 Weeks

Certification

e-Certification + e-Marksheet

Tools

Python, R, TensorFlow, PyTorch, scikit-learn, NLTK

About the Ai Course Course

Unveiling the Foundations of Artificial Intelligence — this comprehensive 8-week program provides a panoramic overview of Artificial Intelligence, exploring its key principles, applications, and methodologies. Participants will gain deep insights into core AI concepts including machine learning, neural networks, natural language processing, and computer vision.

Designed specifically for beginners, the program emphasizes practical understanding through hands-on exercises, real-world case studies, and industrial datasets. Whether you're looking to launch a career in AI or simply understand the technology shaping our future, this program builds the solid foundation you need to thrive in the AI-driven world.

Program Highlights

• Comprehensive coverage of Basics of AI from fundamentals to advanced applications

• Hands-on projects and real-world case studies in AI

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: Python, R, TensorFlow, PyTorch

• Career-oriented training for academic and professional growth in AI

Course Curriculum

Module 1: Introduction to Artificial Intelligence

Explore the definition, history, and evolution of AI alongside key concepts of intelligence and automation
Analyze the philosophical and ethical implications of AI development across different eras
Navigate the AI ecosystem including popular programming languages (Python, R) and frameworks (TensorFlow, PyTorch)

Module 2: Machine Learning

Master supervised learning techniques including regression, classification, and evaluation metrics
Apply unsupervised learning algorithms such as K-means clustering and PCA dimensionality reduction
Implement reinforcement learning concepts including reward systems and Deep Q-Learning

Module 3: Deep Learning

Build neural networks from scratch understanding activation functions, loss functions, and backpropagation
Architect advanced deep learning models including CNNs, RNNs, and Generative Adversarial Networks
Optimize model performance through hyperparameter tuning and regularization techniques

Module 4: Natural Language Processing

Process and represent text data using N-grams, Bag of Words, and TF-IDF vectorization
Develop core NLP applications including sentiment analysis, named entity recognition, and machine translation
Leverage transformer architectures like BERT and GPT for modern language understanding tasks

Module 5: Computer Vision

Process images and videos using fundamental image processing and object detection techniques
Deploy advanced vision architectures like U-Net and Mask R-CNN for image segmentation
Solve real-world problems in healthcare diagnostics, automotive safety, and security surveillance

Module 6: AI in Practice

Implement AI solutions across healthcare, finance, retail, robotics, and smart city applications
Investigate emerging trends in AI research and scientific discovery methodologies
Evaluate responsible AI frameworks, ethical implications, and regulatory compliance requirements

Module 7: Advanced Topics in AI

Design explainable AI models that provide transparency and build user trust
Apply federated learning techniques for privacy-preserving decentralized machine learning
Contribute to AI for social good initiatives in environmental sustainability and public health

Tools, Techniques, or Platforms Covered

Python
R
TensorFlow
PyTorch
scikit-learn
NLTK
spaCy
OpenCV
Jupyter Notebook

Real-World Applications

Apply Basics of AI skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical AI competencies
Solve industry-relevant problems using Basics of AI methodologies and tools
Contribute to open-source projects and collaborative research in AI
Prepare for competitive examinations, interviews, and professional certifications in AI

Who Should Attend & Prerequisites

Industry-recognized e-Certification + e-Marksheet from NSTC
Hands-on training with practical projects and industrial datasets
Dedicated expert mentorship and doubt resolution

Prerequisites:

Frequently Asked Questions

1. What is the format of this Basics of AI course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

No prior experience is required. This course is designed for beginners and takes you step by step from the basics to advanced topics.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 8 Weeks. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to AI. Our mentors are industry experts and experienced professionals.

Enroll in Basics of AI today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI skills that matter.

Brand	NSTC
Format	Online (e-LMS)
Duration	8 Weeks
Level	Beginner
Domain	AI
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	Python, R, TensorFlow, PyTorch, scikit-learn, NLTK, spaCy, OpenCV, Jupyter Notebook

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