machinelearning

Code Repository for the Machine Learning class at Bridgerland Technical College.

Machine Learning

The Machine Learning (Python) course teaches the basics of machine learning and how to use industry standard Python libraries. Students will learn to pre-process data, the differences between various algorithms, and ways to validate a model. The differences between supervised, unsupervised, and reinforcement algorithms will be detailed, as well as the appropriate uses of each. The course also introduces students to complications that arise when interpreting the output of a machine learning model. Students who complete this course are able to find solutions to a range of challenges faced in predictive data analysis using machine learning.

Objectives:

• Demonstrate knowledge of the differences between major machine learning algorithms.
• Demonstrate proficiency with Python to pre-process data to prepare for use in machine learning.
• Train multiple machine learning models using real-world data.
• Demonstrate accepted methods of model validation.
• Utilize machine learning algorithms to process data for pattern and problem detection.

While assignments are available in this code repository, they are also available on Canvas. An outline of the course is detailed below:

• Module 1: Introduction to Machine Learning
  • 1.1 Introduction to Machine Learning (Quiz)
  • 1.2 Course Overview (Quiz)
  • Module 1 Extra Practice (Optional)
• Module 2: Machine Learning Tools
  • 2.1 Google Colab
    • Google Colab Assignment
  • 2.2 Scikit Learn
    • Scikit Learn Assignment
  • 2.3 GitHub
    • GitHub Assignment
  • Module 2 Extra Practice (Optional)
• Module 3: Machine Learning Concepts
  • 3.1 Statistics Concepts
    • Statistics Assignment
  • 3.2 Math Concepts
    • Math Assignment
  • 3.3 Machine Learning Process
    • Machine Learning Process Assignment
  • 3.4 Machine Learning Algorithms
    • Machine Learning Algorithms Assignment
  • 3.5 Cost Function & Cross-validation
    • Cost Function & Cross-validation Assignment
  • Module 3 Extra Practice (Optional)
• Module 4: Machine Learning Models
  • 4.1 Linear Regression
    • Linear Regression Assignment
  • 4.2 Decision Tree
  • 4.3 Random Forest
    • Decision Tree & Random Forest Assignment
  • Module 4 Extra Practice (Optional)
• Module 5: Final Machine Learning Project
  • Final Project
  • Module 5 Extra Practice (Optional)