ML Fundamentals - Learning Projects 🧠
Collection of small projects I'm building to learn machine learning fundamentals. These are learning exercises to understand classification, regression, and data preprocessing using industry-standard tools.
Technology Stack
What I'm Learning
- Classification: Building classifiers using logistic regression, decision trees, and random forests
- Regression: Predicting continuous values using linear regression and polynomial features
- Data Preprocessing: Handling missing data, feature scaling, and encoding categorical variables
- Model Evaluation: Understanding accuracy, precision, recall, and cross-validation
- Feature Engineering: Creating meaningful features from raw data
Current Focus
I'm working through online courses and building small projects with public datasets (Iris, Titanic, Housing prices). The goal is to understand the fundamentals before moving to more complex topics like neural networks and deep learning. I'm particularly interested in how ML can be applied to production systems and backend workflows.
Learning Approach
As a backend engineer, I approach ML from a practical, systems-oriented perspective. I focus on understanding:
- How models integrate with production systems
- Data pipeline design and preprocessing
- Model serving and API design
- Performance optimization and scalability
Interested in ML + Backend?
I'm exploring how to bridge robust backend engineering with intelligent data processing. Let's connect!
Connect With Me