Overview

BCA (Bachelor of Computer Applications) in Artificial Intelligence and Machine Learning is an undergraduate degree program that aims to provide students with a strong foundation in the fields of AI and ML. The program is designed to help students develop skills in computer programming, data analytics, and machine learning, which are essential for a successful career in these industries.

The program covers various aspects of AI and ML, including programming languages such as Python, Java, and C++, data structures and algorithms, data science and analytics, database management systems, deep learning, neural networks, computer vision, natural language processing, and robotics.

The curriculum is designed to provide students with hands-on training through internships, practical exercises, and case studies. This ensures that students are equipped with the skills and knowledge needed to excel in the industry.

After completing the program, graduates can pursue careers in various sectors, including software development, data analysis, machine learning engineering, robotics engineering, and AI research. Some popular job roles include AI programmer, data analyst, machine learning engineer, robotics engineer, and research scientist.

Overall, BCA in Artificial Intelligence and Machine Learning is an excellent choice for students who are interested in pursuing a career in the rapidly growing field of AI and ML. It provides students with a comprehensive understanding of the industry and equips them with the skills and knowledge required to succeed in this dynamic and exciting sector.

Syllabus

Semester I:

  • Introduction to Computer Science
  • Mathematics for Computing
  • Programming in C
  • Computer Organization and Architecture
  • Business Communication

Semester II:

  • Data Structures and Algorithms
  • Object-Oriented Programming with Java
  • Database Management Systems
  • Discrete Mathematics
  • Environmental Studies

Semester III:

  • Artificial Intelligence Fundamentals
  • Machine Learning Techniques
  • Data Science Essentials
  • Computer Networks
  • Web Technologies

Semester IV:

  • Natural Language Processing
  • Deep Learning
  • Big Data Analytics
  • Operating Systems
  • Software Engineering

Semester V:

  • Reinforcement Learning
  • Computer Vision
  • Cloud Computing
  • Information Security
  • Elective I

Semester VI:

  • Project Work
  • Internship
  • Elective II

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