PYTHON & MACHINE LEARNING LAB

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

This course will help 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.

 

Learning Outcome

Learning and Teaching Strategies

Assessment Strategies-

The students will:

CO86: Write python programs using programming and looping constructs to tackle any decision-making scenario.

CO87. Relate the process of structuring the   data using lists, dictionaries, tuples and sets.

CO88: Apply data frame, import data set and perform pre-processing, descriptive and predictive analysis on datasets.

CO89: Correlate data by designing charts and plots like bar charts, line charts using python libraries.

CO90:Implement  machine learning algorithms using python libraries.

Approach in teaching:

Discussions,Tutorials, reading assignments,Demonstrations, Self-learning assignments, Effective questions, Simulation, Performing practical

 

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

 

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 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 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 datasets (sample sklearn datasets), Apriori algorithm, FP tree algorithm,clustering algorithms,Classification and Regression Trees (CART) and C5.0. Linear Regression, Multiple Linear Regression, Logistic Regression, Naïve Bayes, Support Vector Machines(SVM) and Simple neural network

 

 

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

 

 

REFERENCES: 

Suggested Reference Books

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

E-Resources including links

 

 

Academic Year: 
2023-24
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