Machine Learning Lab is a part of III B.Tech for R20 Regulation CSE-DS students. This lab provides hands-on experience in implementing fundamental machine learning algorithms and techniques. It focuses on practical exposure to supervised and unsupervised learning methods using Python and libraries such as scikit-learn, NumPy, and pandas. The lab enables students to understand the application of machine learning in real-world problem solving.

The expected outcomes from the students are:

• Implement supervised learning algorithms such as linear regression, logistic regression, decision trees, and support vector machines.
• Apply unsupervised learning techniques like K-means and hierarchical clustering.
• Analyze model performance using appropriate evaluation metrics such as confusion matrix, precision, recall, and accuracy.
• Work with feature engineering, data preprocessing, and normalization techniques.
• Understand and apply model validation techniques like cross-validation and grid search.
• Develop the ability to use ML libraries and frameworks to build and evaluate predictive models for real-time data.