Career Path: Machine Learning Engineer

This roadmap prepares you to build, evaluate, and deploy Machine Learning models. It focuses on applied ML engineering skills used in real-world systems.

Phase 1: Programming & Data Foundations

Strong foundations required for ML engineering.

Python (Complete) Statistics Mathematics

Phase 2: Data Handling & Analysis

Prepare, clean, and analyze data for ML models.

Pandas NumPy EDA Feature Engineering

Phase 3: Core Machine Learning

Learn and apply core ML algorithms.

ML Basics Algorithms Machine Learning

Phase 4: Model Evaluation & Optimization

Train, tune, and evaluate models effectively.

Model Evaluation Hyperparameter Tuning ML Case Studies

Phase 5: Advanced ML Domains

Specialized ML areas commonly used in industry.

Time Series NLP Computer Vision

Phase 6: ML Systems & Deployment Basics

Understand how ML models are deployed and maintained.

Linux Admin Docker ML Pipelines

Phase 7: Projects & Career Readiness

Build real ML projects and prepare for interviews.

ML Projects Portfolio Interview Prep