Machine Learning Using Python Lab

Paper Code: 
25CBDA312
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

 

 

 

 

 

 

 

 

 

 

 

25CBDA

312

 

 

 

 

 

 

 

 

Machine Learning using Python Lab

(Practical)

 

Contents:

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: 

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 READINGS:

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: 
2025-26
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