LPU - Lovely Professional University
What you'll learn

This course provides a concise yet comprehensive understanding of Artificial Intelligence and Machine Learning, covering foundational concepts, mathematical and programming essentials, data preparation, and classical as well as advanced machine learning techniques. It introduces deep learning, computer vision, and natural language processing, along with emerging areas such as generative AI and agent-based systems. Through hands-on practice using tools like Python and modern AI frameworks, learners will be able to build, evaluate, and deploy intelligent models to solve real-world problems across various domains.

  • AI, Machine Learning, and Deep Learning Basics, Evolution of AI from Rule-Based Systems to Generative AI, Learning Types: Supervised, Unsupervised, Reinforcement, Generative AI and Agent-based Systems (concept and use cases), AI Project Workflow and Deployment Basics, Python Setup, Jupyter Notebook, Google Colab
  • Linear Algebra Basics: Vectors, Matrices, Dot Product, Probability and Statistics: Basic Distributions and Bayes’ Concept, Optimization Basics: Gradient Descent and Cost Functions, Data Handling using Python (NumPy, Pandas), Data Visualization , Overview of Data Analytics Process
  • Data Collection and Cleaning, Basic Feature Engineering and Encoding, Train-Test Split and Cross Validation, Bias-Variance Concept, Regression: Linear and Logistic, Classification Models: KNN, Naive Bayes, Practice: Predictive Model on Dataset
  • Decision Trees and Random Forest Basics, Support Vector Machines (SVM) Overview, Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC, Overfitting and Regularization Basics, Basic Hyperparameter Tuning, Model Explainability (intro to SHAP/LIME), Overview of MLOps
  • Neural Networks Basics: Perceptron and Activation Functions,Backpropagation Concept, Deep Learning Basics,Computer Vision Basics: CNN and Image Classification,Natural Language Processing Basics: Text preprocessing (tokenization, stemming, embeddings),Sentiment Analysis,,Basics of Transformers and LLMs
  • Generative AI Basics (LLMs, Diffusion overview),Prompt Writing Techniques,Tools Overview: APIs, Hugging Face, LangChain, Agent-based Systems: Decision-making and Task Flow,Building AI Agents, Real-world Examples: Chatbots, Smart Systems, Predictive Applications

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Ajmer Singh
Assistant Professor

Mr. Ajmer Singh is an experienced academician at Lovely Professional University (LPU) with a strong background in teaching Electronics, Embedded Systems, Digital Design, Python programming, Artificial Intelligence and Machine Learning. He is committed to delivering high-impact technical education through a hands-on, learner-centric approach. His pedagogy emphasizes bridging theoretical concepts with practical implementation, extending from circuit analysis and simulation environments to data-driven modeling, intelligent systems, and real-world AI applications using modern tools and frameworks.


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Dr. Rosepreet Kaur Bhogal
Associate Professor

Dr. Rosepreet Kaur Bhogal has over 15 years of academic and training experience in the fields of Python programming, Artificial Intelligence, Machine Learning, and Signal Processing. She has successfully conducted various skill development programs, workshops, and technical training sessions aimed at enhancing students’ practical and industry-relevant competencies. Her teaching methodology blends conceptual clarity with hands-on implementation, empowering learners to solve real-world problems through coding, automation, and analytical thinking. Her experience also includes mentoring student projects, supporting placements, and contributing to research and innovation initiatives at the university level.