Machine Learning Lab detailed syllabus for Artificial Intelligence & Machine Learning (AM) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Artificial Intelligence & Machine Learning 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 Artificial Intelligence & Machine Learning 4th Sem scheme and its subjects, do visit Artificial Intelligence & Machine Learning (AM) 4th Sem 2021 revision scheme. The detailed syllabus of machine learning lab is as follows.
Course Objectives:
Students will be able to make use of Data sets in implementing the machine learning algorithms and also to implement the machine learning concepts and algorithms in Python.
Course Outcomes:
On completion of the course, the student will be able to:
- Installation of Anaconda and familiarization of various packages 1
- Develop solution for Supervised learning
- Develop solution for Unsupervised learning
- Open Ended Experiments
Module 1:
- Practice installation of anaconda
- Installation and familiarization of various packages – NumPy, Pandas, sklearn, matlibplot etc.
- Practice import and export data using library functions by identifying any five synthetic data sets from various online resources. Data sets can be taken from standard repositories (eg:https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students. Use appropriate data sets, for modelling the below problems, wherever necessary
- Write a Python program to do the following operations: Library: NumPy a) Create multi-dimensional arrays and find its shape and dimension b) Create a matrix full of zeros and ones c) Reshape and flatten data in the array d) Append data vertically and horizontally e) Apply indexing and slicing on array f) Use statistical functions on array – Min, Max, Mean, Median and Standard Deviation
- Implement data preprocessing (eg: Normalization, Processing missing data)
Module 2:
- Implement k-nearest neighbours classification on datasets
- Implement linear regression on datasets
- Implement decision tree
- Implement Random Forest
- Implement Naive Bayes
Module 3:
- Implement dimensionality reduction using Principal Component Analysis (PCA)
- Implement K-means clustering
- Implement Mean Shift clustering
Suggested Open Ended Experiments
(Not for End Semester Examination but compulsory to be included in Continuous Internal Evaluation. Students can do open ended experiments as a group of 2-3. There is no duplication
in experiments between groups)
- Coronavirus break down
- Cancer subtype identification
- Prediction on House prices
- Prediction of survival on Titanic
Text Books:
- Kevin P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press 2012.
- Ethem Alpaydin, Introduction to Machine Learning, 2nd edition, MIT Press 2010
- Mitchell M., T., Machine Learning, McGraw Hill (1997) 1stEdition.
Online Resources
- https://developers.google.com/machine-learning/crash-course/
- https://studyglance.in/labprograms/mllabprograms.php
- https://machinehack.com/
For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning (AM), 2021 revision curriculum do visit Artificial Intelligence & Machine Learning 4th 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 Artificial Intelligence & Machine Learning (AM) of diploma 2021 revision curriculum do visit SITTTR diploma Artificial Intelligence & Machine Learning (AM) results..
For all Artificial Intelligence & Machine Learning academic calendars, visit Artificial Intelligence & Machine Learning all semesters academic calendar direct link.