Artificial Intelligence & Machine Learning using Python

• 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

After completion of the course student will be able to:

1. CO1Ability to use data sets in implementing the machine learning algorithmsK2, K4
2. CO2Ability to build an Artificial Neural NetworkK2, K4
3. CO3Understand and apply k-Nearest Neighbour algorithmK2, K4
4. CO4Ability to implement the Regression algorithmsK2, K4
5. CO5Ability to use Scikit-learn for machine learningK4