List of Experiments / Activities
- 1Familiarize students with Python programming fundamentals and Jupyter Notebook.
- 2Perform numerical computations using the NumPy library.
- 3Perform data loading, preprocessing, and manipulation using Pandas.
- 4Visualize datasets using Matplotlib and Seaborn.
- 5Prepare datasets for machine learning applications.
- 6Implement and evaluate a Linear Regression model using Scikit-learn.
- 7Develop a binary classification model using Logistic Regression.
- 8Build and evaluate a Decision Tree classification model.
- 9Implement the Random Forest algorithm for classification.
- 10Implement the K-Nearest Neighbors algorithm for classification.
- 11Implement a Support Vector Machine classifier.
- 12Implement the Naïve Bayes algorithm for classification and evaluate its performance.
- 13Perform unsupervised clustering using the K-Means algorithm.
- 14Apply Principal Component Analysis for dimensionality reduction.
- 15Develop a simple Artificial Neural Network using TensorFlow/Keras.
Course Outcomes (COs)
After completion of the course student will be able to:
- CO1Analyze datasets by applying Python programming, NumPy, and Pandas libraries for data manipulation, preprocessing, and exploratory data analysis.K4
- CO2Evaluate regression and classification models using supervised machine learning algorithms and appropriate performance evaluation metrics.K5
- CO3Analyze datasets using unsupervised learning algorithms.K4
- CO4Analyze dimensionality reduction techniques to discover hidden patterns and extract meaningful insights.K4
- CO5Design and develop Artificial Neural Network models using TensorFlow/Keras for solving real-world predictive and classification problemsK6
Official AKTU syllabus
This subject is on page 24 of the original AKTU PDF.