BCA in Artificial Learning & Machine Learning (AL-ML) at SAGE University Indore - Course, Fees, Admission, Seats, Syllabus

Based on my update in April 2023 it may not be easy to find specific course outlines for SAGE University Indores BCA program in Artificial Learning & Machine Learning (AL ML) without accessing their curriculum documents directly. However by considering the AL ML courses offered in BCA programs and the essential topics covered in similar programs I can provide a general list of important subjects that are likely to be included in such a course. It's important to note that for the accurate and up to date syllabus information it is recommended to refer to the universitys official resources or reach out to the institution directly. The following list offers an overview of topics typically emphasized in a BCA program with a focus, on Artificial Intelligence and Machine Learning; **An Introduction to Programming*; This covers the basics of programming languages like Python, which is extensively used in projects related to AI and machine learning. *Mathematics for Machine Learning*; This section focuses on mathematical concepts such as linear algebra, calculus, statistics and probability theory. These concepts form the foundation for understanding and developing machine learning algorithms. *Data Structures and Algorithms*; Understanding data structures and algorithms is essential for handling and processing data, which plays a crucial role in machine learning projects. *Database Management Systems*; This involves gaining knowledge about how databases function, including SQL and NoSQL databases. Understanding database management systems is important for managing the data used in machine learning models. *Fundamentals of Artificial Intelligence*; In this section we provide an overview of intelligence including its history, applications and ethical considerations. *Machine Learning Algorithms*; Here we cover types of machine learning algorithms such as supervised learning, unsupervised learning, reinforcement learning, neural networks, decision trees, clustering techniques, regression analysis, etc. *Deep Learning*; This part introduces learning along with neural networks such as convolutional neural networks (CNNs) recurrent neural networks (RNNs) and their applications, in different domains. *Natural Language Processing (NLP)*; This module focuses on understanding natural language processing techniques used to analyze human language data. Analyzing human language including sentiment analysis and chatbots. Enabling computers to. Understand images and videos through techniques like computer vision, which includes object detection and image recognition. Introduction to data analytics technologies, tools and frameworks such as Hadoop and Spark which are used to process and analyze large datasets. Discussion on the ethical implications of artificial intelligence (AI) and machine learning (ML) including topics like privacy, bias and fairness. Best practices for managing projects related to AI and ML covering areas such as data preparation, model selection, training, evaluation and deployment. These topics provide a foundation in both the theory and practical aspects of AI and ML. They equip students, with the skills required for this field.