inaraX

AI for Engineers

For engineers & technical learners

A structured, level-based program (Associate → Intermediate → Advanced) that takes you from core machine learning and deep learning fundamentals to NLP/LLMs, MLOps, deployment, and production-grade AI system design—ending with an end-to-end capstone project.

Created by InaraX
Instruction language: English
InaraX

Earn a certificate from InaraX

After completion of all courses in the InaraX AI for Engineers Certificate path, earn a professional certificate that you can share on social media, LinkedIn, resume, or CV.

What you'll learn

  • ✓Build a strong ML foundation (supervised, unsupervised, evaluation)
  • ✓Master data preprocessing + feature engineering workflows
  • ✓Understand and implement neural networks + deep learning fundamentals
  • ✓Apply deep learning to Computer Vision and NLP/LLMs
  • ✓Deploy and serve models with production constraints in mind
  • ✓Implement MLOps practices (lifecycle, pipelines, monitoring)
  • ✓Design AI systems and architecture patterns for real-world use
  • ✓Ship an end-to-end capstone AI application

Course content

3 levels • 25 modules

  • Module 1: Introduction to AI and Machine LearningLocked
  • Module 2: Python and Data Science ToolingLocked
  • Module 3: Data Exploration and PreprocessingLocked
  • Module 4: Supervised Learning - RegressionLocked
  • Module 5: Supervised Learning - ClassificationLocked
  • Module 6: Model Evaluation and ImprovementLocked
  • Module 7: Unsupervised LearningLocked
  • Module 8: Neural Networks and Deep Learning BasicsLocked
  • Module 9: AI Applications and Next StepsLocked

Requirements

  • Comfort with basic programming concepts (variables, functions, loops).
  • Helpful (not required): basic Python and basic math (algebra, statistics).

Description

AI for Engineering is a level-based curriculum designed for engineers who want to build real, production-ready AI systems. You’ll start with machine learning fundamentals and strong data workflows, progress into deep learning for computer vision and NLP/LLMs, and finish with MLOps, deployment strategies, and AI system architecture—plus an end-to-end capstone to pull everything together.

By the end of this course, you will:

  • Build and evaluate ML models (regression, classification, clustering) with clean workflows.
  • Prepare datasets with preprocessing, feature engineering, and reproducible experiments.
  • Implement deep learning fundamentals and apply them to Computer Vision and NLP/LLMs.
  • Deploy and serve models, understanding practical constraints and trade-offs.
  • Apply MLOps practices for lifecycle management, pipelines, and monitoring.
  • Design production-grade AI systems using architecture patterns and real-world considerations.
  • Deliver an end-to-end capstone AI application.

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