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Python For Machine Learning

Enrollment in this course is by invitation only

About This Course

This is the first course in B. Tech. Minor in Computer Science and Engineering with specialization in Machine Learning.

This is a programming course where the objective is to provide learners an insight into Python programming, and develop programming skills to manage the development of software systems. It covers programming environment, important instructions, data representations, intermediate level features, Object Oriented Programming and file data processing of Python. This course lays the foundation to develop web applications, Machine Learning, and Artificial Intelligence-based applications and tools, Data Science and Data Visualization applications.

After the completion of the course the student will be able to

  • CO1: Write, test and debug Python programs (Cognitive Knowledge level: Apply)
  • CO2: Illustrate uses of conditional (if, if-else, if-elif-else and switch-case) and iterative (while and for) statements in Python programs (Cognitive Knowledge level: Apply)
  • CO3: Develop programs by utilizing the modules Lists, Tuples, Sets and Dictionaries in Python (Cognitive Knowledge level: Apply)
  • CO4: Implement Object Oriented programs with exception handling (Cognitive Knowledge level: Apply)
  • CO5: Write programs in Python to process data stored in files by utilizing the modules Numpy, Matplotlib, and Pandas (Cognitive Knowledge level: Apply)

Course Syllabus

View Course Syllabus



Course Textbook

  1. Kenneth A Lambert., Fundamentals of Python : First Programs, 2/e, Cengage Publishing,2016
  2. Wes McKinney, Python for Data Analysis, 2/e, Shroff / O’Reilly Publishers, 2017

Course Staff

Course Staff Image #1

Jestin Joy

Jestin Joy is an Assistant Professor at Department of Computer Science and Engineering. Jestin Joy received his B.Tech degree in Computer Science and Engineering from CUSAT in 2009, M.Tech degree in Computer Science with specialization in Information Systems from MG University in 2011 and PhD in Technology from CUSAT in 2020.

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Python version used in this course

Python 3