Machine Learning Using Python Lab

Paper Code: 
24CBDA312
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

This Course enables the students to:

  1. Learn different python libraries and their functionalities.
  2. Design machine learning models in python.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

CO133.Analyse python libraries and their utility in different problems.

CO134.Build data frame, import data set and perform pre-processing, descriptive and predictive analysis on datasets.

CO135.Communicate results by designing charts and plots like bar chart, line charts and ROC curve using python libraries.

CO136.Design model based on machine learning algorithms using python libraries.

CO137.Performance evaluation of machine learning models.

CO138. Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion, Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, Exercises related with Machine Learning algorithm, presentations

Class test, Semester end examinations, Quiz, Practical Assignments, Presentation

24CBDA 312

Machine Learning using Python Lab

(Practical)

 

 

Exercises based on the following topics:

Importing pandas library,Using series and Data Frames, indexing, Grouping, aggregating, Merging dataFrames, dealing with missing values using dropna method ,filtering or filling in missing data, creating dataframes from dictionaries or nested dictionaries, accessing and changing values of data frame using loc,at,replace methods,reading and writing csv,excel files

 

Importing Visualisation 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.

 

ESSENTIAL READINGS: 

SUGGESTED TEXT BOOKS

  1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  2. Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Second Edition,Packt>.
  3. McKinney, Python for Data Analysis. O’ Reilly Publication,2017.

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS

1.    Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018. (Latest editions of the above books are to be referred)

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 

Academic Year: 
2024-25
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