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Please ensure to replace the placeholders (e.g., [Course Duration], [Tuition Fees]) with the actual data related to the BCA in Artificial Learning & Machine Learning (AL-ML) program at Manav Bharti University.

As of my recent update in April 2023 the specific course outlines for the BCA program in Artificial Learning & Machine Learning (AL ML) at Manav Bharti University may vary. However I can provide you with an overview of the important topics typically covered in a BCA program that focuses on Artificial Intelligence (AI) and Machine Learning (ML). These topics serve as the foundation. Are crucial for understanding and working within the fields of AI and ML. 1. Introduction to Artificial Intelligence; Exploring the history and evolution of AI Understanding the concepts of AI Examining real world applications of AI across various domains Discussing ethical considerations related to AI 2. Programming for AI and ML; Python programming with a focus on libraries like NumPy, Pandas, Matplotlib and Scikit learn Understanding of R programming for statistical analysis Exploring data structures and algorithms essential for AI and ML 3. Mathematics for AI and ML; Linear Algebra principles applied to AI and ML Calculus concepts used in these fields Probability theory and statistical methods relevant to AI development Understanding mathematics as it relates to these areas 4. Data Science and Data Analysis; Preprocessing techniques for cleaning data to analysis Conducting exploratory data analysis (EDA) Utilizing various data visualization techniques Gaining familiarity with databases and data warehousing concepts 5. Machine Learning; Supervised learning methods such, as regression or classification algorithms Unsupervised learning techniques including clustering or dimensionality reduction Basics of reinforcement learning Evaluation metrics employed to assess performance of machine learning models 6. Deep learning is a field that encompasses various topics. Some of the aspects include neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Additionally there are adversarial networks (GANs) that play a significant role. Moving on to natural language processing (NLP) we explore concepts like text preprocessing, tokenization and word embeddings like Word2Vec and GloVe. Sentiment analysis and machine translation are also parts of NLP. Computer vision is another area to delve into. It involves image processing fundamentals, object detection and recognition techniques well as face recognition. Furthermore computer vision finds applications in domains. When it comes to robotics and automation understanding the basics of robotics is crucial. We can explore how AI is integrated into robotics systems and how automation benefits from AI technologies. In the stages of your AI and ML journey you will work on a capstone project or thesis. This involves working with real world datasets documenting your project progress comprehensively and presenting your findings effectively. Additionally there are topics to consider such as the ethical implications of AI technology in society. Exploring emerging trends like quantum computings impact on AI or its applications, in healthcare can be intriguing too. Finally understanding industry uses of AI an