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DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

“Python for Artificial Intelligence”  – (30 hours)

Introduction:

This course is designed to provide students with an introduction to Artificial Intelligence (AI) and its applications using the Python programming language. The curriculum focuses on the essential concepts and techniques in AI, and will provide a solid foundation for further study.

Prerequisites:

Basic knowledge of programming concepts

Basic understanding of mathematics (linear algebra, calculus)

Duration:

The course is designed to be completed in 30 hours of instruction, spread out over several weeks or months.

Course Outline:

Module I. Python Fundamentals (5 hours) 

  • Introduction to Python programming
  • Data Types and Variables
  • Operators and Expressions
  • Control Structures
  • Functions

Module II. Data Science with Python (5 hours)

  • Introduction to Data Science
  • Numpy and Pandas
  • Data Visualization using Matplotlib
  • Data Preprocessing
  • Regression and Classification

Module III. Machine Learning with Python (10 hours)

  • Introduction to Machine Learning
  • Supervised Learning (Linear Regression, Logistic Regression)
  • Unsupervised Learning (K-Means, PCA)
  • Neural Networks and Deep Learning

 Module IV. Natural Language Processing with Python (5 hours)

  • Introduction to NLP
  • Text Preprocessing (Tokenization, Stop Word Removal, Stemming)
  • Sentiment Analysis

Module V. Project-based Learning (5 hours)

Students will work on a real-world project using the AI techniques and algorithms covered in the course. This will provide hands-on experience and help to reinforce the concepts learned in the course.

Resources:

Recommended textbook:

Python for Data Science Handbook by Jake VanderPlas

Online tutorials and videos

Python libraries for AI and Machine Learning (Numpy, Pandas, Matplotlib, Scikit-learn, Tensorflow, Keras)

Assessment:

Quizzes and exams to test understanding of the course material

Project evaluations to assess the application of AI techniques and algorithms to real-world problems.

 

Faculty for theory classes:

Ms. Shemimol.B, Ms. Neetha Alex

Hands on sessions:

Ms. Shemimol B, Ms. Neetha Alex, Ms. Ansia S

Ms. Soorya P, Ms. Chinnu Ravi

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