Machine Learning – I (F19SMT3D)


Learn fundamentals of Machine Learning using Python.

Level: Pegasus

Tools: Python, NumPy, Pandas, Matplotlib, Seaborn, Plotly, Cufflinks

Prerequisite: Basic Programming

Students will take home: Project files

In this class students will,

  • Learn Elements of Python programming language required for machine learning
  • Perform Data Analysis using popular Python libraries  – NumPy and Pandas.
  • Learn Data Visualization using libraries like Matplotlib.
  • Solidify their understanding with a data capstone project.
  • Learn to use Machine Learning library – Scikit Learn.
  • Understand how Neural Networks work.
    • Learn about Linear and Logistic Regression.
  • Build a foundation for more advanced machine learning content.

Course Content

Please note that each of the topics below will be followed by hands-on exercises that students are expected to complete along with possible homework.

  1. Introduction and Software Setup
    • Google Colab Overview
  2. Python Crash Course
    • Data types: Numbers, Strings, Printing, Lists, Dictionaries, Booleans, Tuples, Sets
    • Comparison Operators and if, elif, else Statements
    • for Loops, while Loops
    • range()
    • list comprehension
    • functions
    • lambda expressions
    • map and filter
    • methods
  3. Data Analysis using NumPy
    • Arrays
    • Indexing and Selection
    • Operations
  4. Data Analysis using Pandas
    • Series
    • DataFrames
    • Missing Data
    • Groupby
    • Merging, Joining and Concatenating
    • Operations
    • Data Input and Output
  5. Data Visualization with Matplotlib
  6. Data Visualization with Seaborn
  7. Data Visualization with Plotly and Cufflinks
  8. Data Capstone Project
    • 911 Calls Analysis
  9. Machine Learning with Python
    • Introduction to Scikit Learn
  10. Linear Regression
  11. Logistic Regression