LPU - Lovely Professional University
What you'll learn

Applied Artificial Intelligence and Machine Learning is a practical, hands-on course designed to introduce learners to the core concepts and real-world applications of Artificial Intelligence (AI) and Machine Learning (ML). The course begins with the foundations of Python programming, enabling participants to work with essential libraries used in data analysis and machine learning. Learners will gain experience in data preprocessing, exploratory data analysis, feature engineering, and building predictive models using popular machine learning algorithms. The course further covers important techniques such as model evaluation, cross-validation, dimensionality reduction, and hyperparameter tuning to improve model performance. In addition, students will be introduced to the basics of Deep Learning, including neural networks, activation functions, and the fundamentals of building simple deep learning models. Through hands-on exercises, practical implementations, and real-world case studies, participants will learn how to develop intelligent systems that can analyze data, make predictions, and support data-driven decision-making. By the end of the course, learners will have the foundational knowledge and practical skills required to build and evaluate machine learning and basic deep learning models using Python.

  • Introduction to Artificial Intelligence and Machine LearningApplications across domains (business, healthcare, automation, vision, language), Overview of the ML pipeline, types of AI (narrow, general), Key AI problems and techniques, Modern,AI Toolkits (TensorFlow, PyTorch), Python data structures: Lists, tuples, sets, dictionaries, Functions, Modules and packages,File handling.
  • Introduction to Pandas, DataFrames and Series, EDA: Data visualization using Matplotlib & Seaborn, Types of data, Data pre-processing, Feature Engineering
  • Introduction to Machine Learning algorithms, Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN) , Decision Tee Algorithm and Random Forest,
  • Model evaluation, Ensemble Learning , Cross Validation and Hyperparameter tuning
  • Unsupervised Learning : Role of unsupervised learning, Differences between supervised and unsupervised learning, Euclidean, Manhattan, Cosine distances, Choosing appropriate distance metrics, K-Means Clustering, Elbow method, K-Medoids, Hierarchical Clustering
  • Neural Networks, Activation functions, ANN, CNN, RNN

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Anzar Hussain Lone
Assistant Professor

Anzar Hussain Lone is an Assistant Professor in the Department of Computer Science and Engineering at Lovely Professional University, Punjab. With a strong academic background, he holds a B.Tech and M.Tech in Computer Science and Engineering, having completed his master’s degree from National Institute of Technology (NIT) Srinagar. He is also a two-time GATE qualifier, demonstrating his deep understanding of core computer science concepts. With over three years of teaching experience, he has been actively involved in academic instruction, research, and mentoring. His primary research interests include Artificial Intelligence, Data Science, and Machine Learning applications. Mr. Lone has authored four research papers in the domain of Artificial Intelligence, and is currently guiding 2 masters students and 15 B.Tech students on research and innovation projects. An ardent researcher and educator, he has conducted several workshops and training programs on Artificial Intelligence and Prompt Engineering for students and educators. His academic and professional contributions reflect his commitment to fostering innovation, critical thinking, and applied research in emerging technologies.