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
andshutil
libraries - Practical exercise: Write a script to copy specific file types from
c:\project1
andc:\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
andh5py
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
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