BCA in Artificial Learning & Machine Learning (AL-ML) at Kanya Mahavidyalaya - Course, Fees, Admission, Seats, Syllabus

Semester	Subjects	Credits
1	Introduction to Subject, Basic Concepts	4
2	Intermediate Subject Study, Applied Concepts	4
3	Advanced Subject Study, Specialized Topics	4

This HTML snippet creates a simple table with three columns (Semester, Subjects, Credits) and a row for each semester's details. You would need to replace the placeholders with the actual syllabus details for Kanya Mahavidyalaya or any other institution's specific course information. For the most accurate and up-to-date syllabus for Kanya Mahavidyalaya, I recommend visiting the official website of the institution or contacting the relevant department directly. They can provide you with the specific syllabus details for the semester and course you're interested in.

As of my last update in April 2023, specific syllabus details for Kanya Mahavidyalaya's BCA in Artificial Learning & Machine Learning (AL-ML) specialization can vary over time as academic institutions frequently update their course content to keep pace with technological advancements and industry needs. However, I can provide you with a general overview of important topics that are typically covered in such programs. Please note, for the most accurate and up-to-date syllabus, you should consult the official Kanya Mahavidyalaya website or contact the institution directly. Given the constraints, here's a simplified, single-line HTML format representation of what such a syllabus might include, focusing on broad, essential topics:

Core Topics	Tools & Technologies	Applied Areas
Introduction to Programming	Python, R	Data Preprocessing
Data Structures and Algorithms	TensorFlow, PyTorch	Classification Models
Mathematics for Machine Learning	Scikit-learn	Regression Analysis
Principles of Artificial Intelligence	Matplotlib, Pandas	Natural Language Processing
Machine Learning Algorithms	SQL, NoSQL Databases	Computer Vision
Deep Learning	Keras	Neural Networks
Statistics and Probability	Git, Docker	Reinforcement Learning
Big Data Analytics	Hadoop, Spark	Time Series Analysis
Cloud Computing for AI	AWS, Azure ML	AI Ethics and Law

This table is a simplified representation and includes core topics like programming fundamentals, data structures, machine learning and artificial intelligence principles, alongside tools and technologies such as Python, TensorFlow, and cloud platforms like AWS. Applied areas cover practical applications of machine learning algorithms, including natural language processing, computer vision, and more. Remember, the actual syllabus may include more detailed topics, additional subjects, and specific course requirements. Always refer to the official course documentation for the most accurate information.