BCA in Artificial Learning & Machine Learning (AL-ML) at Nopany Institute of Management Studies - Course, Fees, Admission, Seats, Syllabus

As of my last update in April 2023, I don't have access to real-time databases or specific course syllabi, including those for the Nopany Institute of Management Studies BCA in Artificial Learning & Machine Learning (AL-ML). Course syllabi can vary by institution and are subject to change over time. For the most accurate and up-to-date syllabus, I recommend contacting the Nopany Institute of Management Studies directly or visiting their official website. However, I can provide a generic example of what a BCA in Artificial Learning & Machine Learning syllabus might include, structured in a single-line HTML table format as requested. Please note, this is a simplified and hypothetical example and should not be taken as an actual syllabus from the Nopany Institute of Management Studies.

Year	Subjects
1st Year	Introduction to Programming, Mathematics for Computer Science, Introduction to Artificial Intelligence, Basics of Machine Learning, English and Communication Skills
2nd Year	Data Structures and Algorithms, Database Management Systems, Advanced Machine Learning, Deep Learning Fundamentals, Statistics for AI & ML, Mini Project
3rd Year	Natural Language Processing, Computer Vision, Reinforcement Learning, AI & ML Project Management, Major Project

This table includes a hypothetical three-year BCA in AL-ML curriculum, broken down by year with a list of subjects that might be included in such a program. For the actual syllabus, please consult the Nopany Institute of Management Studies or a similar accredited institution.

In my recent update in April 2023 detailed course information for the BCA program in Artificial Learning & Machine Learning (AL ML) at Nopany Institute of Management Studies may not be readily accessible due to frequent curriculum updates and variations among academic institutions. Nonetheless I can offer an overview of essential subjects typically covered in a BCA program specializing in Artificial Intelligence (AI) and Machine Learning (ML). These foundational topics are commonly included in such a program; **Introduction to Artificial Intelligence (AI) and Machine Learning (ML);** Background and evolution of AI Basics of AI and ML Distinctions between AI, ML and Deep Learning (DL) **Programming for AI and ML;** Python programming language (predominantly used in AI/ML) Libraries and frameworks like NumPy, Pandas, Matplotlib, TensorFlow, PyTorch **Mathematics, for AI and ML;** Linear Algebra Calculus Probability and Statistics Optimization methods **Data Preprocessing and Analysis;** Data cleansing and standardization Feature enhancement and selection Exploratory Data Analysis (EDA)In the realm of machine learning one encounters a variety of algorithms, such as supervised learning (linear regression, logistic regression, decision trees, random forests, support vector machines) and unsupervised learning (e.g., clustering, principal component analysis). Additionally there are fundamentals to grasp in reinforcement learning. Delving into learning unveils the basics of neural networks along with specialized areas like Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs). Which include Long Short Term Memory (LSTM) networks. And Generative Adversarial Networks (GANs). Natural Language Processing (NLP) involves tasks like text preprocessing and tokenization utilizing word embeddings such as Word2Vec and GloVe. Sequence models with attention mechanisms play a role in this domain. In Computer Vision one explores image processing techniques alongside concepts like object detection and recognition well as image segmentation methods. Furthermore understanding the dimensions surrounding AI is crucial. This includes discussions on bias and fairness within AI models, privacy concerns in data handling practices and ethical dilemmas that arise in the application of AI technologies. Lastly effective project management is essential for AI/ML projects. Understanding the project lifecycle, in these domains ensures execution and delivery.