MACHINE LEARNING ENGINEER ROADMAP

You’ll receive a structured development roadmap that outlines skills, timelines, courses, and practical tasks. Follow the steps and reach the level employers require.

Provides foundational knowledge about how software and systems work.
Python is the dominant language for ML; solid programming is essential.
Vital for understanding models like neural networks and PCA.
Needed for understanding optimization algorithms like gradient descent.
Core to understanding predictions, distributions, and evaluation metrics.
Crucial for data manipulation and preprocessing in ML pipelines.
Helps in understanding data patterns, outliers, and insights.
Prepares data before feeding it into models; uncovers hidden trends and anomalies.
These are the core models used in real-world business problems.
Useful for clustering, dimensionality reduction, and anomaly detection.
Helps assess model quality and make informed improvements.
These libraries are industry-standard tools for efficient model development.
Helps improve model generalization and performance.
Reinforces theory with hands-on implementation; builds a portfolio.
Foundation of modern AI and complex pattern recognition.
Tools used to build and train deep learning models.
Widely used in ML applications like chatbots and sentiment analysis.
Useful in fields like autonomous vehicles, surveillance, or retail analytics.
Turns models into usable tools or products in real environments.
Supports working in teams, managing experiments, and tracking code.
Enables model automation, monitoring, and retraining at scale.
Industry-standard platforms for hosting and training large-scale models.
Ensures bias-free, ethical, and explainable AI implementation.
Builds domain understanding and business relevance of ML projects.
Adds credibility and may boost job opportunities.
Prepares you for real-world job interviews and competitions.
Helps keep up with new tools, best practices, and peer feedback.