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AKTU Artificial Intelligence & Machine Learning Using Python (BMBA351) Syllabus PDF – MBA (Business Analytics) 3rd Semester
Artificial Intelligence & Machine Learning Using Python BMBA351
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Official AKTU syllabus of Artificial Intelligence & Machine Learning Using Python (BMBA351) for MBA (Business Analytics) 3rd Semester – 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 Using Python
Download PDFBMBA351 · L-T-P 0-0-2
Course objectives: This course will enable students to
- Make use of Data sets in implementing the machine learning algorithms
- Implement the machine learning and artificial intelligence concepts and algorithms using python Lecture contents Basic Concepts and understanding the application & usage of Machine Learning. Meaning and basics of AI and is practical scenario and its application in medical, manufacturing etc. Introduction to Neural Network Lab Work:
| S.N | Lab Module | Lab work / Class |
| 1 | Write a Python program to load the iris data from a given csv file into a data frame and print the shape of the data, type of the data and first 3 rows. | 1 |
| 2 | Write a Python program using Scikit-learn to print the keys, number of rows-columns, feature names and the description of the Iris data | 1 |
| 3 | Write a Python program to split the iris dataset into its attributes (X) and labels (y). The X variable contains the first four columns (i.e. attributes) and y contains the labels of the dataset | 1 |
| 4 | Write a Python program to draw a scatterplot, then add a joint density estimate to describe individual distributions on the same plot between Sepal length and Sepal width. | 1 |
| 5 | Write a Python program using Scikit-learn to split the iris dataset into 70% train data and 30% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Print both datasets. | 1 |
| 6 | Implement and demonstrate the any suitable algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file. | 1 |
| 7 | For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples. | 1 |
| 8 | Write a program to demonstrate the working of the decision tree using any suitable algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. | 1 |
| 9 | Build an Artificial Neural Network by implementing the Back propagation algorithm. | 1 |
| 10 | Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file | 1 |
| 11 | Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. | 2 |
| 12 | Apply any suitable algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. | 2 |
| 13 | Write a program to implement k-Nearest Neighbor algorithm to classify the iris data set. | 2 |
| 14 | Implement the non-parametric Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs. | 2 |
| 15 | Write a Python program to get the accuracy of the Logistic Regression. | 2 |
| 16 | Demonstrate min-max scaling and standardization using Scikit-learn on a numerical dataset. Show how scaling affects different ML algorithms. | 2 |
| 17 | Implement the k-Nearest Neighbors classifier using Scikit-learn on the Iris dataset. Evaluate using accuracy, precision, and confusion matrix. | 2 |
| 18 | Implement linear regression using Scikit-learn on a dataset (e.g., housing prices). Visualize the regression line and report error metrics (MAE, RMSE). | 2 |
| 19 | Build a Decision Tree classifier using Scikit-learn on a suitable dataset. Visualize the tree structure. | 2 |
| 20 | Write a Python program to handle missing data using mean/median imputation, label encoding, and one-hot encoding for categorical variables in a dataset (e.g., Titanic dataset). | 2 |
Course Outcomes
| CO Code | CO Statement | Bloom’s K Levels |
|---|---|---|
| CO1 | Ability to use data sets in implementing the machine learning algorithms | K2, K4 |
| CO2 | Ability to build an Artificial Neural Network | K2, K4 |
| CO3 | Understand and apply k-Nearest Neighbour algorithm | K2, K4 |
| CO4 | Ability to implement the Regression algorithms | K2, K4 |
| CO5 | Ability to use Scikit-learn for machine learning | K4 |
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