5307: Introduction To Machine Learning Lab Syllabus for Robotics Process Automation 5th Sem 2021 Revision SITTTR

Introduction To Machine Learning Lab detailed syllabus for Robotics Process Automation (RP) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Robotics Process Automation students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Robotics Process Automation 5th Sem scheme and its subjects, do visit Robotics Process Automation (RP) 5th Sem 2021 revision scheme. The detailed syllabus of introduction to machine learning lab is as follows.

Course Objectives:

• Learn to perform data extraction and preprocessing using python tool kits.
• Demonstrate the working of classification and regression algorithms.
• Demonstrate the working of clustering algorithms
• Understand and analyze the performance of neural networks in classification.
• To obtain practical knowledge in real world problems.

Course Outcomes:

On completion of the course student will be able to:

1. Understand the features of machine learning to apply on real world problems using python programming language.
2. Understand the different types of Classification and regression algorithms.
3. Design and evaluate the unsupervised models through python in built functions.
4. Analyze the concepts of perceptron and SVM and implement using python packages.

Module 1:

1. Study of Python Basic Libraries such as Statistics, Math, Numpy and Scipy
2. Study of Python Libraries for ML application such as Pandas and Matplotlib

Module 2:

1. Preprocess the dataset
2. Implementation of Bayesian classifier and analyzing the classification performance.
3. Implementation of simple and linear regression using python built in functions.
4. Implementation of multiple linear regression using python built in functions.

Module 3:

1. Implement K-means clustering algorithm to cluster a set of data stored in a csv file
2. Implement EM clustering algorithm to cluster a set of data stored in a csv file

Module 4:

1. Implement perceptron classifier using python sklearn
2. Implement SVM classifier using python sklearn
3. Performance analysis of classification algorithms on a specific dataset.

Micro Project

Students are expected to do a micro project in machine learning during the course for the purpose of continuous evaluation. This experiment shall be included in the bona-fide record. Example: Develop program such as

• Classification of Mushroom dataset (UCI repository)
• House price prediction (Regression)
• K-Means clustering (Iris dataset)

Text Books:

1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
2. Ethem Alpaydın, Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, 2004.

Online Resources

1. https://www.javatpoint.com/machine-learning
2. https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory primer
3. https://www.tutorialspoint.com/machine_learning/index.htm
4. https://ml-course.github.io/master/labs/Lab%201%20-%20Tutorial
5. https://www.geeksforgeeks.org/introduction-machine-learning-using-python/

List of Experiments:

1. Write a python program to demonstrate the working of Numpy functions.
2. Write a python program to demonstrate the working of math library functions.
3. Write a python program to read data from a csv file and convert it to pandas data frame.
4. Write a python program to plot graphs using Matplotlib library.
5. Write a python program to preprocess the dataset by substituting mean, mode or median for missing data.
6. Write a python program to implement a Bayesian classifier and analyze the classification performance.
7. Write a python program to implement simple linear regression using built in functions.
8. Write a python program to implement multiple linear regression using built in functions.
9. Implement K-means clustering algorithm to cluster a set of data stored in a csv file
10. Implement EM clustering algorithm to cluster a set of data stored in a csv file
11. Implement perceptron classifier using python sklearn
12. Implement SVM classifier using python sklearn
13. Write a python program to do the performance analysis of classification algorithms on a specific dataset.

For detailed syllabus of all other subjects of Robotics Process Automation (RP), 2021 revision curriculum do visit Robotics Process Automation 5th Sem subject syllabuses for 2021 revision.

To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..

To see the results of Robotics Process Automation (RP) of diploma 2021 revision curriculum do visit SITTTR diploma Robotics Process Automation (RP) results..

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