BCA in Artificial Learning & Machine Learning (AL-ML) at Jyoti Nivas College - Course, Fees, Admission, Seats, Syllabus

Developing a curriculum or determining the subjects for a Bachelor of Computer Applications (BCA) program with a specialization in Artificial Intelligence (AI) and Machine Learning (ML) involves encompassing fundamental concepts, tools and practical applications that hold significant importance in these fields. Although specific course offerings and emphases may differ across institutions, a program similar to the one offered at Jyoti Nivas College would likely include a combination of theoretical knowledge and hands on skills across various domains. Here are the key areas and subjects that such a program might cover; 1. Introduction to Artificial Intelligence Tracing the history and evolution of AI Establishing foundations of AI; Logic, problem solving and search strategies Understanding agents and environments Exploring applications of AI 2. Programming for AI and ML Mastering Python programming; From basics to advanced levels Utilizing libraries and frameworks like NumPy, Pandas, Matplotlib, Scikit learn, TensorFlow and PyTorch Handling data manipulation and visualization techniques 3. Mathematics for AI and ML Grasping concepts in algebra Embracing fundamentals of calculus Incorporating probability theory along with statistical analysis Employing optimization techniques 4. Fundamentals of Machine Learning Unveiling the distinction between supervised vs. Unsupervised learning approaches Diving into semi supervised learning as well as reinforcement learning methodologies Evaluating metrics for model performance assessment along with model selection considerations Gaining insights into challenges like overfitting, underfitting, as well, as managing model complexity. 5. In this course we will cover topics related to artificial intelligence and machine learning. Here are some of the areas we will explore; 1. Deep Learning; We'll delve into networks, including their architecture, activation functions and backpropagation. We'll also discuss Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) networks. Additionally we'll explore Generative Adversarial Networks (GANs). 2. Natural Language Processing (NLP); We'll cover text preprocessing techniques and feature extraction methods. Word embeddings and language models will be discussed in detail. Furthermore we'll explore sentiment analysis, chatbots development and machine translation. 3. Computer Vision; Image processing techniques will be a point of our study. Object detection and recognition algorithms will also be explored. Face recognition systems well as image generation methods will be covered. 4. Data Science and Big Data; Acquiring data sets cleaning them up and performing analysis are some of the key aspects we'll dive into. You’ll also get acquainted with data technologies such, as Hadoop and Spark.