---

If you’re serious about building a career in Data Science, Data Engineering, Machine Learning, MLOps, or Generative AI, this curated collection of free courses can save you months of searching.

Instead of jumping randomly between resources, use this structured roadmap to learn step by step — from foundations to advanced specialization.

Let’s break it down by domain.

---

Data Science

Data Science is the foundation of modern AI careers. It combines statistics, programming, and data analysis.

1. Python for Everybody
   A perfect starting point for beginners. Learn Python fundamentals, programming logic, and problem-solving.
2. Data Analysis with Python
   Covers practical data manipulation using libraries like Pandas and NumPy. Ideal for aspiring data analysts and data scientists.
3. Databases & SQL
   Understand relational databases and learn SQL for querying, filtering, and managing structured data.
4. Intro to Inferential Statistics
   Learn how to make predictions using probability, hypothesis testing, and statistical inference.
5. ML Zoomcamp
   A highly practical course focused on real-world machine learning projects and implementation.

---

Data Engineering

Data Engineers build the backbone of data systems — pipelines, warehouses, and infrastructure.

1. Data Engineering Course
   Covers fundamentals of pipelines, ETL processes, and distributed systems.
2. Data Engineer Learning Path
   A structured roadmap that guides you from beginner to professional data engineer.
3. Database Engineer Course
   A deeper dive into database architecture, optimization, and system design.
4. Big Data Specialization
   Explore big data technologies like Hadoop and Spark and understand large-scale processing.
5. Data Engineering Zoomcamp
   A hands-on, project-based course to learn real-world data engineering workflows.

---

Machine Learning

Machine Learning transforms data into predictive systems.

1. Intro to Machine Learning
   A beginner-friendly introduction to ML concepts and workflows.
2. ML for Everybody
   Explains machine learning in simple terms — great for non-technical learners.
3. Machine Learning in Python
   Focuses on Scikit-Learn and practical implementation of ML algorithms.
4. ML Crash Course
   A fast-paced but comprehensive overview of key ML concepts.
5. CS229 – Machine Learning
   An advanced academic-level course for those who want to deeply understand ML mathematics and theory.

---

Machine Learning Operations (MLOps)

MLOps connects machine learning with production systems.

1. Python Essentials for MLOps
   Strengthen your Python skills for model deployment and automation.
2. MLOps for Beginners
   A practical introduction to deploying, monitoring, and maintaining ML systems.
3. ML Engineering Course
   Bridges software engineering and machine learning.
4. MLOps Specialization
   Focuses on CI/CD, model versioning, pipelines, and scalable ML systems.
5. Made With ML
   Combines theory with real-world ML production workflows.

---

Generative Artificial Intelligence

Generative AI is shaping the future of technology.

1. Generative AI for Beginners
   Learn how to build generative AI applications from scratch.
2. Generative AI Fundamentals
   Understand transformers, diffusion models, and generative modeling concepts.
3. Intro to Generative AI
   From learning large language models to understanding the principles of responsible AI.
4. Generative AI with LLMs
   Learn business applications of LLMs with practical use cases.
5. Generative AI for Everyone
   A high-level course explaining how generative AI works, its limitations, and its impact.