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AKTU Artificial Intelligence & Machine Learning Lab (BDS451) Syllabus PDF – B.Tech 2nd Year
Artificial Intelligence & Machine Learning Lab BDS451
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- Semester 4
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Official AKTU syllabus of Artificial Intelligence & Machine Learning Lab (BDS451) for B.Tech 2nd Year – 1 credits, L-T-P 0-0-2. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Artificial Intelligence & Machine Learning Lab
Download PDFBDS451 · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of course, the student will be able to understand
| CO1 | Analyze datasets by applying Python programming, NumPy, and Pandas libraries for data manipulation, preprocessing, and exploratory data analysis. | K4 |
| CO2 | Evaluate regression and classification models using supervised machine learning algorithms and appropriate performance evaluation metrics. | K5 |
| CO3 | Analyze datasets using unsupervised learning algorithms. | K4 |
| CO4 | Analyze dimensionality reduction techniques to discover hidden patterns and extract meaningful insights. | K4 |
| CO5 | Design and develop Artificial Neural Network models using TensorFlow/Keras for solving real-world predictive and classification problems | K6 |
Course Content
| Experiment No. | Experiment |
| 1 | Familiarize students with Python programming fundamentals and Jupyter Notebook. |
| 2 | Perform numerical computations using the NumPy library. |
| 3 | Perform data loading, preprocessing, and manipulation using Pandas. |
| 4 | Visualize datasets using Matplotlib and Seaborn. |
| 5 | Prepare datasets for machine learning applications. |
| 6 | Implement and evaluate a Linear Regression model using Scikit-learn. |
| 7 | Develop a binary classification model using Logistic Regression. |
| 8 | Build and evaluate a Decision Tree classification model. |
| 9 | Implement the Random Forest algorithm for classification. |
| 10 | Implement the K-Nearest Neighbors algorithm for classification. |
| 11 | Implement a Support Vector Machine classifier. |
| 12 | Implement the Naïve Bayes algorithm for classification and evaluate its performance. |
| 13 | Perform unsupervised clustering using the K-Means algorithm. |
| 14 | Apply Principal Component Analysis for dimensionality reduction. |
| 15 | Develop a simple Artificial Neural Network using TensorFlow/Keras. |
Note: The Instructor may add/delete/modify experiments, wherever he/she feels in a justified manner.
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