BCA in Artificial Learning & Machine Learning (AL-ML) at Vinayaka Missions Sikkim University - Course, Fees, Admission, Seats, Syllabus

Vinayaka Missions Sikkim University provides a range of courses, including options in the field of Computer Applications such as BCA. When focusing on Artificial Learning (AL) and Machine Learning (ML) within a BCA program the curriculum aims to establish a foundation in both the theoretical and practical aspects of these advanced technologies. While specific courses may vary here are some subjects typically covered in a BCA program with an emphasis on AL and ML; 1. **Introduction to Programming**; Before delving into AL and ML students need a grasp of programming basics. Popular languages like Python commonly used in ML projects are often highlighted. 2. **Mathematics for Computer Science**; This encompasses concepts like linear algebra, probability, statistics and calculus – all vital for comprehending and implementing ML algorithms effectively. 3. **Data Structures and Algorithms**; A understanding of data structures (such as arrays, lists, trees and graphs) as well as algorithms is crucial for efficient data manipulation and algorithm implementation in ML projects. 4. **Database Management Systems (DBMS)**; Proficiency, in DBMS is essential when dealing with the datasets typically involved in ML projects. This includes knowledge of SQL databases well as NoSQL databases. 5. **Foundational Concepts of Artificial Intelligence (AI)**; Before diving into the field of machine learning it is essential to have a grasp of the principles and techniques that underpin AI. These may encompass search algorithms, logic and methods for representing knowledge. 6. **Machine Learning**; This serves as the core focus within the specialization and encompasses an array of subjects ranging from supervised and unsupervised learning to neural networks and deep learning. The course delves into algorithms such as linear regression, logistic regression, decision trees, as well as various clustering techniques in great detail. 7. **Deep Learning**; Building upon machine learning deep learning concentrates on networks with multiple layers. Students will explore topics like Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) along with their applications. 8. **Natural Language Processing (NLP)**; NLP deals with how computers interact with humans through language. It covers aspects such as text processing, sentiment analysis and the development of chatbots. 9. **Computer Vision**; This area involves training computers to comprehend and interpret information from their surroundings. Topics may include image recognition, object detection and even image generation. 10. **Big Data Technologies**; Gaining proficiency in processing and analyzing data is crucial for successful machine learning projects. This segment encompasses understanding tools like Hadoop, Spark along, with significant big data frameworks. 11. **Ethics considerations**