Published on Computer Science & IT (https://csit.iisuniv.ac.in)

Home > PYTHON & MACHINE LEARNING LAB

PYTHON & MACHINE LEARNING LAB [1]

Paper Code: 
25DCAI 702A
Credits: 
06
Periods/week: 
12
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

  1. Define the basic concepts of python programming.
  2. Learn and use python library functions.
  3. Analyse data and solve problems using different python libraries.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

title

 

25DCAI 702A

 

PYTHON & MACHINE LEARNING LAB

(PRACTICAL)

CO103.Develop python programs using control statements and functions to tackle any decision-making scenario.

CO104. Apply data structures (lists, dictionaries, tuples, sets) for solving diverse problems.

CO105. Apply data frames for dataset import and analyze using pre-processing, descriptive, and predictive methods.

CO106. Compare data by designing charts and plots like bar charts, line charts using python libraries.

CO107. Apply machine learning algorithms using python libraries.

CO108. Contribute effectively in course-specific interaction

 

Approach in teaching:

Discussions, Demonstrations

 

Learning activities for the students:

 

Self-learning assignments, Practical questions

Class test, Quiz, Practical Assignments, Semester end examinations

 

Unit I: 

Exercises given will be covering entire syllabi as follows

     Jupyter Installation for Python, Features of Python, Python Applications

  • Basics of Python: variables, data types, operators & expressions, decision  statements.
  • Loop control statements.
  • Functions.
  • Understand the difference between a function and an object.
  • String manipulation.
  • Tuples, sets and dictionaries: Need, Creation, Operations and in-built      functions
  • 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 data frames  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 pyplot.plot subplots method, checking and defining ranges of axes, using linspace       and linstyle, specifying legend, title Style,creating Scatter plots ,Bar  charts.
  • Implementing Machine Learning with scikit-learn: loading and Visualizing atasets (sample sklearn  datasets), Apriori algorithm, FP tree algorithm, clustering algorithms, Classification and Regression Trees(CART) and C    5.0. Linear Regression, Multiple Linear Regression,Logistic Regression, Naïve Bayes, Support Vector Machines(SVM) and  Simple neural network.

 

ESSENTIAL READINGS: 

Suggested Text Books:

  1. Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Third     Edition,2019.
  2. McKinney, Python for Data Analysis. O’ Reilly Publication,3rd Edition,2017.

 

REFERENCES: 

        Suggested Reference Books:

  1. Curtis Miller,” Hands-On Data Analysis with NumPy and Pandas",First      Edition,2018

       Reference Journals:

  1. Journal of the Brazilian Computer Society, Springer Open
  2. Journal of Internet Services and Applications, Springer Open

      e-Resources including links

  1. https://www.python.org/downloads/ [2]
  2. https://jupyter.org/
  3. https://nptel.ac.in/courses/106106182 [3]
  4. https://www.geeksforgeeks.org/ [4]

 

Academic Year: 
2025-26 [5]

Follow Computer Science & IT on:

IIS (Deemed to be University)

Gurukul Marg, SFS, Mansarovar, Jaipur 302020, (Raj.) India Phone:- +91-141-2400160-61, 2397906-07, Fax: 2395494, 2781158

Source URL: https://csit.iisuniv.ac.in/courses/subjects/python-machine-learning-lab-1

Links:
[1] https://csit.iisuniv.ac.in/courses/subjects/python-machine-learning-lab-1
[2] https://www.python.org/downloads/
[3] https://nptel.ac.in/courses/106106182
[4] https://www.geeksforgeeks.org/
[5] https://csit.iisuniv.ac.in/academic-year/2025-26