| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 6 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹8249 / $103 |
| Tools | Python 3 Jupyter Notebook VS Code Pandas NumPy Flask/Django Git |
About the Python for AI Course
Python Language - Use in AI is an 8-week course that explores Python's pivotal role in the development of AI technologies.
Intended for M.Tech, M.Sc, and MCA students, as well as professionals in various tech industries, the course offers in-depth instruction on Python coding, data handling, machine learning, deep learning, and natural language processing.
Program Highlights
• Comprehensive coverage of Python for AI from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Python Programming
• 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 Python Programming
• Career-oriented training for academic and professional growth in Python Programming
Course Curriculum
Module 1: Overview of Python’s Role in AI Chapter 1: Introduction to Python for AI
- Lesson 1.1 : Python’s Popularity and Role in AI
- Lesson 1.2 : Key Benefits of Python in AI Projects
- Lesson 1.3 : Why Python is Preferred for AI Development
Module 2: Python Fundamentals for AI Chapter 2: Python Programming Essentials
- Lesson 2.1 : Variables, Data Types, and Basic Operations
- Lesson 2.2 : Control Flow: Conditional Statements and Loops
- Lesson 2.3 : Functions and Modular Programming
- Lesson 2.4 : Error Handling and Debugging in Python
Module 3: Data Handling for AI Chapter 4: Working with NumPy for Numerical Computations
- Lesson 4.1 : Introduction to NumPy Arrays
- Lesson 4.2 : Array Manipulation and Broadcasting
- Lesson 4.3 : Numerical Operations and Matrix Computations in NumPy
Module 4: Introduction to Machine Learning with Python Chapter 7: Fundamentals of Machine Learning (ML)
- Lesson 7.1 : Basic Concepts and Terminology in ML
- Lesson 7.2 : Types of Machine Learning: Supervised vs. Unsupervised
- Lesson 7.3 : Understanding Overfitting and Underfitting
Module 5: Deep Learning with Python Chapter 9: Building Neural Networks
- Lesson 9.1 : Introduction to Neural Networks
- Lesson 9.2 : Implementing Neural Networks with TensorFlow
- Lesson 9.3 : Understanding Deep Learning Architectures
Module 6: Natural Language Processing (NLP) with Python Chapter 11: Fundamentals of NLP
- Lesson 11.1 : Tokenization, Stemming, and Lemmatization
- Lesson 11.2 : Using NLTK for Text Processing
- Lesson 11.3 : spaCy for Advanced NLP
Module 7: AI for Data Science and Analytics Chapter 13: Data Science Essentials for AI
- Lesson 13.1 : Exploring Large Datasets with Pandas and NumPy
- Lesson 13.2 : Feature Engineering for AI Models
- Lesson 13.3 : Real-World Case Study: Predictive Analytics
Tools, Techniques, or Platforms Covered
Python 3 Jupyter Notebook VS Code Pandas NumPy Flask/Django Git
Real-World Applications
- Apply Python for AI skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Python Programming competencies
- Solve industry-relevant problems using Python for AI methodologies and tools
- Contribute to open-source projects and collaborative research in Python Programming
- Prepare for competitive examinations, interviews, and professional certifications in Python Programming
Who Should Attend & Prerequisites
- Students pursuing degrees in Python Programming, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Python Programming roles
- Researchers and academicians looking to adopt modern techniques in Python Programming
- Entrepreneurs, freelancers, and self-learners interested in practical Python Programming knowledge
Certification
