skilldevelopment.cpe@lpu.co.in

What you'll learn

This course is designed to equip learners with the practical knowledge and hands-on skills required to build end-to-end machine learning solutions that meet industry standards. It provides a structured understanding of the machine learning pipeline — starting from data collection and preprocessing, through feature engineering, to model development and evaluation. Students will learn to use Python and essential ML libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn to analyze real-world datasets, train predictive models, and interpret results effectively. Learning Outcomes: 1. Understand the fundamentals of Machine Learning and its applications across industries. 2. Perform data preprocessing using techniques such as data cleaning, transformation, encoding, and normalization. 3. Apply feature engineering and selection methods to improve model performance. 4. Implement supervised and unsupervised machine learning algorithms using Scikit-learn. 5. Evaluate model performance using appropriate metrics

  • AI vs ML vs DL, ML pipeline, Real-world ML applications
  • Python refresher: Lists, Loops, Functions, Libraries, Numpy, Pandas basics, Importing, exploring datasets, Data visualization
  • Handling missing values, Outlier detection and treatment, Data scaling and normalization, Encoding categorical variables, Train-test split and cross-validation
  • Feature creation and transformation, Binning, log transform, polynomial features, Feature selection, Dimensionality reduction
  • Linear Regression, Ridge, Lasso, Perceptron, Logistic Regression, Decision Trees, SVM, Random Forest, KNN, Ensemble Learning, Model tuning, Evaluation metrics
  • K-Means and its variants, Hierarchical clustering, DBSCAN, Principal Component Analysis (PCA)

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Dr. Ankita Wadhawan
Assistant Professor

Dr. Ankita Wadhawan is a dedicated educator and data science professional with deep expertise in Machine Learning, Artificial Intelligence, Deep Learning and Applied Data Analytics. Her teaching and training approach focus on building practical, job-ready competencies by engaging learners in the complete lifecycle of machine learning ranging from data preprocessing and feature engineering to model building and evaluation. Skilled in tools such as Python, NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow, she fosters analytical thinking and problem-solving through real-world projects and case-driven learning. Dr. Wadhawan’s commitment to bridging academic learning with industry practice enables participants to develop strong technical foundations and confidently pursue roles in the data and AI-driven technology landscape.


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Dr. Usha Mittal
Associate Professor

Dr. Usha Mittal is an accomplished academic and trainer with expertise in Machine Learning, Artificial Intelligence, and Data-Driven Application Development. Her teaching emphasizes hands-on learning and industry readiness, guiding learners to develop strong analytical and programming skills using Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Dr. Mittal has led multiple student and institutional projects focused on AI-driven decision support systems and predictive analytics. She is passionate about bridging the gap between theory and practice by integrating practical implementation, research insights, and project-based learning into her courses, preparing students for technical and placement-oriented roles in the data and AI domain.


Avatar
Dr. Usha Mittal
Associate Professor

Dr. Usha Mittal is an accomplished academic and trainer with expertise in Machine Learning, Artificial Intelligence, and Data-Driven Application Development. Her teaching emphasizes hands-on learning and industry readiness, guiding learners to develop strong analytical and programming skills using Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Dr. Mittal has led multiple student and institutional projects focused on AI-driven decision support systems and predictive analytics. She is passionate about bridging the gap between theory and practice by integrating practical implementation, research insights, and project-based learning into her courses, preparing students for technical and placement-oriented roles in the data and AI domain.