Welcome to my Machine Learning (ML) repository! This collection of code files serves as a comprehensive resource for mastering various aspects of the ML workflow. From fetching data from scikit-learn datasets to advanced tasks like feature engineering and model deployment using Amazon SageMaker, this repository covers it all.
Technologies Used:
- Amazon SageMaker: Utilized for running Jupyter notebooks, managing training jobs, and deploying models.
- Python: Programming language used extensively for ML model development and scripting.
- scikit-learn: Essential library for data preprocessing, feature extraction, and model training.
- Pandas: Used for data manipulation and analysis.
- NumPy: Fundamental library for numerical operations and array processing in Python.
- Matplotlib and Seaborn: Libraries for data visualization and exploratory data analysis (EDA).
Whether you're new to ML or looking to expand your expertise, these code examples provide hands-on experience and practical insights into building and deploying ML models effectively with Amazon SageMaker.
Feel free to explore the code, experiment with different techniques, and elevate your ML skills with this repository!