MACHINE LEARNING USING PYTHON LAB

Paper Code: 
CBDA 312
Credits: 
3
Periods/week: 
6
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 (COs).

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will:

  1. Categorize basic libraries of python with their utility in different problems.
  2. Build data frame, import data set and perform pre-processing, descriptive and predictive analysis on datasets.
  3. Communicate results by designing charts and plots like bar chart, line charts and ROC curve using python libraries.
  4. Design model based on machine learning algorithms using python libraries .
  5. Performance evaluation of machine learning models.

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, 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

 

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. Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas"

(Latest editions of the above books are to be referred)

 

E RESOURCES:

 

JOURNALS:

 

Academic Year: 
2022-23
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