Department Events
Machine Learning Lab
Course Objectives:
This Course enables the students to:
1. Learn different python libraries and their functionalities.
2. Design machine learning models in python.
Course | Learning Outcome (at courselevel) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | |||
25DBCA502C | Machine Learning Lab (Practical) | CO319 Analyse python libraries and their utility in different problems. CO320 Build data frame, import data set and perform pre-processing, descriptive and predictive analysis on datasets. CO321 Communicate results by designing charts and plots like bar chart, line charts and ROC curve using python libraries. CO322 Design model based on machine learning algorithms using python libraries. CO323 Performance evaluation of machine learning models. CO324 Contribute effectively in course-specific interaction. | Approach in teaching: Interactive Lectures, Discussion, Demonstration,
Learning activities for the students: Self-learning assignments, Practical questions | Class test, Semester end examinations, Quiz, Presentation, Individual and group Assignments, viva- voce |
Contents:
Exercises based on the following topics:
Importing pandas library, Using series and Data Frames, indexing, Grouping, aggregating, Merging data Frames, dealing with missing values using dropna method, filtering or filling in missing data, creating data frames from dictionaries or nested dictionaries, accessing and changing values of data frame using locate replace methods, reading and writing csv excel file
Importing Visualization libraries: Matplotlib: format parameter of pylpot plot, subplots method, checking and defining ranges of axes, using linspace and linstyle, specifying legend, title Style, creating Scatter plots, Bar charts, histogram, Stack charts, Saving plots. Importing seaborn library:Style functions, color palettes, Distribution plots ,categorical plots
Implementing Machine Learning with scikit-learn: loading and Visualizing datasets (sample sklearn datasets), splitting train and test data.
e RESOURCES:
1. https://www.jigsawacademy.com/blogs/business-analytics/
2. NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212
3. Python, w3scool, https://www.w3schools.com/
4. Jupiter :www.jupiter.com
5. Googlecolab: www.googlecolab.com
JOURNALS:
1. Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
2. International Journal of Machine Learning and Cybernetics, springer https://www.springer.com/journal/13042
