| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Beginner |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | R RStudio |
About the AI Foundations for Students and Beginners Course
This course, AI Foundations for Students and Beginners, is an 8-week program that provides a solid introduction to artificial intelligence (AI).
It is perfect for students or those starting out, who want to understand AI concepts, machine learning (ML), and learn programming using Python. By the end, learners will have the skills to apply AI in real-world situations.
Program Highlights
• Comprehensive coverage of AI Foundations for Students and Beginners from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Science & Technology
• 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
• Exposure to industry-standard tools and platforms used in Science & Technology
• Career-oriented training for academic and professional growth in Science & Technology
Course Curriculum
Module 1: Machine Learning and AI Basics (2 Weeks)
- Intro to AI, its branches (ML, Deep Learning, NLP)
- How AI is used in industries
- Key concepts: supervised vs unsupervised learning, classification, regression
- Simple AI models using Python and Scikit-learn
Module 2: Python Programming for AI (2 Weeks)
- Learn Python basics for AI programming
- Setting up Python and Jupyter Notebooks
- Python basics: data types, loops, functions
- Introduction to NumPy and Pandas for data manipulation
Module 3: Natural Language Processing (NLP) (2 Weeks)
- Understanding NLP and how machines process language
- Working with text data and preprocessing
- Basic NLP models: tokenization, stemming, lemmatization
- Using libraries like NLTK and spaCy
Module 4: Scikit-learn for Machine Learning (2 Weeks)
- Introduction to Scikit-learn
- Building models like Decision Trees, K-Nearest Neighbors, and Linear Regression
- Measuring performance with accuracy, precision, recall
- Project: Predict student performance based on previous data
Tools, Techniques, or Platforms Covered
R RStudio
Real-World Applications
- Apply AI Foundations for Students and Beginners skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Science & Technology competencies
- Solve industry-relevant problems using AI Foundations for Students and Beginners methodologies and tools
- Contribute to open-source projects and collaborative research in Science & Technology
- Prepare for competitive examinations, interviews, and professional certifications in Science & Technology
Who Should Attend & Prerequisites
- Students pursuing degrees in Science & Technology, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Science & Technology roles
- Researchers and academicians looking to adopt modern techniques in Science & Technology
- Entrepreneurs, freelancers, and self-learners interested in practical Science & Technology knowledge
Certification
