ftw_python

FTW Foundation

Repo for FTW Foundation Python Course (Day 1 and Day 2) and Python for Machine Learning (Day 3 and Day 4)

Introduction to Python

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Day 1 Outine: Python Basics

1. Hello, World
2. Python Basics
3. Variables

• 3.1 Strings
• 3.2 Integers
• 3.3 Floats

4. Data Types

• 4.1 Lists
  • Changing, Adding, and Removing Elements
  • Organizing a List
  • Avoiding Index Errors when Working with Lists
  • Looping through an entire list
  • Avoiding indentation errors
  • Making numerical lists
  • working with part of a list
• 4.2 Tuples
• 4.3 Dictionaries (Notebook #5)

5. Functions

• 5.1 Defining a Function
• 5.2 Passing Arguments
• 5.3 Return Values
• 5.4 Passing a List
• 5.5 Passing an Arbitrary Number of Arguments

6. If statements
7. While loops
8. More Functions (Python 6 - note that we didn't get through all of this in class, so please do review in your own time)

A. Appendix

• A.1 Variable Naming Conventions
• A.2 The Zen of Python

Day 2 Outline: Python for Data Science

1. Quick Review

• 1.1. Dictionaries (any additional questions, a few exercies)
• 1.2. More Functions (any additional questions, a few exercies)

2. File IO
3. Try/Except Error Handling
4. Terminal Applications
5. Sets, Math, Dates, and Assorted Important Functions
6. Numpy
7. Pandas
8. Matplotlib
9. More Data Science Functions

Introduction to Machine Learning

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Day 3: Core ML Concepts

• Statistics vs Machine Learning
• What is Machine Learning?
• What is a good model?
• Contextualizing ML Approaches
  • Defining an ML Problem
  • The Analytics Process
  • Model Development Workflow
• Basic ML Concepts
  • Features vs Label / Target Variables
  • Domain Expertise
  • Feature Engineering
  • Train / Test Split
  • Baseline Model
  • Algorithms
  • Champion Model
  • Model Maintenance
• Code Sections
  • Feature Engineering
  • Regression
  • Classification
  • Clustering
  • Modeling Techniques

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Day 4: Development to Deployment

• Code Sections
  • Classification
  • Clustering
  • Modeling Techniques
• Deployment Methods and Frameworks
  • Streamlit
  • FastAPI
• Additional Datasets for Modeling: https://docs.google.com/presentation/d/1sQQ04TtFSwBC0G2NQoe5ba3z_p4sklTTwSixIevlbbs/edit#slide=id.g13cf5ba7d16_0_50

References:

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• Python Crash Course
• Python Data Science Handbook
• Last year's Python Course
• Google's Guide to Machine Learning
• ML Slides created for FTW
• An Introduction to Statistical Learning
• Udemy Course by Jose Portilla
• FTW Course Syllabus