Course Overview:

This course provides a comprehensive guide to preparing a codebase for machine learning, focusing on using the CodeGenesis model. Participants will learn to manage, preprocess, and optimize code from a software development perspective to enhance machine learning training effectiveness. The course will cover practical skills involving Python scripting, data handling, and machine learning basics tailored for code optimization.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• Software Developers interested in AI

Course Structure:

Module 1: Introduction to Code Preparation for Machine Learning

• Overview of machine learning in code analysis
• Introduction to the CodeGenesis model
• Course tools and environment setup

Module 2: Consolidating Your Codebase

• Identifying and gathering relevant code files from multiple directories
• Automating file collection using Python’s os and shutil libraries
• Practical exercise: Write a script to copy specific file types from c:\project1 and c:\project2

Module 3: Cleaning and Preprocessing Code

• Techniques to clean code: removing comments, normalizing whitespace and indentation
• Using regex for text manipulation in Python
• Practical exercise: Create a script to preprocess code files

Module 4: Code Tokenization

• Understanding tokenization and its importance in machine learning
• Exploring tools like tree-sitter for advanced parsing
• Practical exercise: Tokenize sample code using Python

Module 5: Vectorizing the Code

• Introduction to embeddings and vectorization
• Using libraries like gensim for creating embeddings
• Practical exercise: Generate embeddings from tokenized code

Module 6: Organizing the Data

• Preparing data for machine learning models
• Using numpy and h5py for handling large datasets
• Practical exercise: Organize and store processed code

Module 7: Configuring the Training Environment

• Setting up the machine learning environment with necessary tools and libraries
• Ensuring proper GPU setup and configurations
• Practical exercise: Configure a basic machine learning environment

Module 8: Model Training

• Loading data into the CodeGenesis model
• Monitoring and adjusting training parameters
• Practical exercise: Train a model with prepared codebase