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.
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.
- Introduction and Software Setup
- Google Colab Overview
- Python Crash Course
- Data types: Numbers, Strings, Printing, Lists, Dictionaries, Booleans, Tuples, Sets
- Comparison Operators and if, elif, else Statements
- for Loops, while Loops
- list comprehension
- lambda expressions
- map and filter
- Data Analysis using NumPy
- Indexing and Selection
- Data Analysis using Pandas
- Missing Data
- Merging, Joining and Concatenating
- Data Input and Output
- Data Visualization with Matplotlib
- Data Visualization with Seaborn
- Data Visualization with Plotly and Cufflinks
- Data Capstone Project
- 911 Calls Analysis
- Machine Learning with Python
- Introduction to Scikit Learn
- Linear Regression
- Logistic Regression