Prerequisites for AI Engineering
Programming Foundation Requirements
- What you Need to Know
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Python Programming Fundamentals
- Variables, data types, control structures, and functions
- Object-oriented programming concepts and classes
- Error handling and debugging techniques
- Resources:
- Python for Everybody - Coursera - University of Michigan (Free audit)
- Python.org Official Tutorial - Complete Python language tutorial
- Automate the Boring Stuff with Python - Free online book for practical Python
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JavaScript and Web Development Basics
- JavaScript fundamentals and DOM manipulation
- HTML/CSS for web interfaces
- Asynchronous programming and API calls
- Resources:
- JavaScript.info - Comprehensive JavaScript tutorial
- freeCodeCamp Web Development - HTML, CSS, JavaScript fundamentals
- MDN Web Docs - Web development learning resources
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Version Control and Collaboration
- Git fundamentals and repository management
- GitHub workflow and collaborative development
- Code documentation and project organization
- Resources:
- Git Tutorial - Atlassian - Comprehensive Git guide
- GitHub Learning Lab - Interactive GitHub tutorials
- Pro Git Book - Complete Git reference (free online)
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Mathematics and Statistics Foundation
- What you Need to Know
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Linear Algebra Basics
- Vectors, matrices, and basic operations
- Understanding of dimensionality and transformations
- Dot products and matrix multiplication concepts
- Resources:
- Linear Algebra - Khan Academy - Interactive linear algebra course
- 3Blue1Brown Linear Algebra - Visual linear algebra explanations
- MIT Linear Algebra Course - MIT OpenCourseWare
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Statistics and Probability
- Descriptive statistics and data distributions
- Probability concepts and Bayes' theorem
- Hypothesis testing and statistical inference
- Resources:
- Statistics and Probability - Khan Academy - Complete statistics course
- Think Stats - Free statistics book with Python examples
- Statistics Course - Coursera - Stanford University (Free audit)
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Data Handling and Analysis
- What you Need to Know
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Data Manipulation with Python
- Pandas for data manipulation and analysis
- NumPy for numerical computing
- Data cleaning and preprocessing techniques
- Resources:
- Pandas Documentation - Official pandas user guide
- NumPy Tutorial - NumPy quickstart guide
- Python Data Science Handbook - Free online data science book
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Data Visualization
- Matplotlib and Seaborn for statistical plots
- Interactive visualizations with Plotly
- Data storytelling and visualization best practices
- Resources:
- Matplotlib Tutorials - Official matplotlib tutorials
- Seaborn Tutorial - Statistical data visualization
- Plotly Python - Interactive plotting library
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API and Web Services Understanding
- What you Need to Know
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REST API Concepts
- HTTP methods and status codes
- JSON data format and parsing
- API authentication and rate limiting
- Resources:
- REST API Tutorial - Complete REST API guide
- HTTP Protocol Guide - MDN - HTTP fundamentals
- JSON Introduction - JSON data format specification
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API Integration and Testing
- Making API calls with Python requests library
- Error handling and response validation
- API testing with Postman and curl
- Resources:
- Python Requests Documentation - HTTP library for Python
- Postman Learning Center - API development and testing
- cURL Tutorial - Command-line API testing
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Cloud Computing Basics
- What you Need to Know
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Cloud Platform Fundamentals
- Understanding of cloud service models (IaaS, PaaS, SaaS)
- Basic knowledge of AWS, Azure, or Google Cloud Platform
- Cloud storage and compute concepts
- Resources:
- AWS Cloud Practitioner Essentials - Free AWS fundamentals
- Microsoft Azure Fundamentals - Azure basics learning path
- Google Cloud Digital Leader - GCP fundamentals
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Containerization Basics
- Docker concepts and container lifecycle
- Container images and registries
- Basic container orchestration concepts
- Resources:
- Docker Get Started - Official Docker tutorial
- Play with Docker - Interactive Docker playground
- Docker for Beginners - Comprehensive Docker guide
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AI and Machine Learning Concepts
- What you Need to Know
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AI/ML Terminology and Concepts
- Supervised, unsupervised, and reinforcement learning
- Training data, models, and inference concepts
- Overfitting, underfitting, and model evaluation
- Resources:
- Machine Learning Crash Course - Google - Free ML fundamentals course
- AI for Everyone - Coursera - Andrew Ng's AI course (Free audit)
- Elements of AI - University of Helsinki AI course
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Neural Networks and Deep Learning Basics
- Artificial neurons and network architectures
- Forward propagation and backpropagation concepts
- Common neural network types (CNN, RNN, Transformer)
- Resources:
- Neural Networks and Deep Learning - Free online book
- Deep Learning Specialization - Coursera - Andrew Ng's course (Free audit)
- 3Blue1Brown Neural Networks - Visual neural network explanations
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Software Development Practices
- What you Need to Know
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Testing and Quality Assurance
- Unit testing and test-driven development
- Code quality and linting tools
- Debugging and profiling techniques
- Resources:
- Python Testing 101 - Python testing fundamentals
- pytest Documentation - Python testing framework
- Code Quality Tools - Linting and formatting tools
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Project Structure and Documentation
- Python package structure and virtual environments
- Technical documentation and README files
- Code comments and docstring conventions
- Resources:
- Python Packaging Guide - Official Python packaging tutorial
- Virtual Environments Guide - Python virtual environments
- Write the Docs - Documentation best practices
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Assessment and Readiness Check
- What you Need to Know
-
Technical Skills Validation
- Build a simple web application with API integration
- Create data visualizations from CSV or JSON data
- Write unit tests for Python functions
- Deploy a basic application to a cloud platform
- Resources:
- Flask Tutorial - Python web framework tutorial
- Heroku Python Deployment - Free cloud deployment
- GitHub Pages - Free static site hosting
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Problem-Solving and Learning Skills
- Ability to read and understand technical documentation
- Debug code issues using error messages and logs
- Learn new libraries and frameworks independently
- Break down complex problems into smaller components
- Resources:
- How to Debug Code - Debugging strategies and techniques
- Stack Overflow - Programming Q&A community
- Python Documentation - Official Python documentation
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Personalized Learning Pathways
- What you Need to Know
-
For Complete Programming Beginners
- Focus on Python fundamentals and programming concepts (8-12 weeks)
- Build basic projects and practice problem-solving
- Learn version control and development tools
- Resources:
- Python for Everybody Specialization - Complete Python learning path
- Codecademy Python Course - Interactive Python learning
- Python Crash Course - Practical Python programming book
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For Experienced Developers
- Focus on AI/ML concepts and data science libraries (4-6 weeks)
- Learn cloud platforms and API integration
- Practice with AI/ML frameworks and tools
- Resources:
- Fast.ai Practical Deep Learning - Practical deep learning course
- Scikit-learn Tutorial - Machine learning library tutorial
- TensorFlow Tutorials - Deep learning framework tutorials
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For Data Scientists Transitioning to Engineering
- Focus on software engineering practices and deployment (6-8 weeks)
- Learn web development and API design
- Practice containerization and cloud deployment
- Resources:
- Software Engineering for Data Scientists - Duke University course
- MLOps Specialization - ML engineering practices
- Docker for Data Science - Containerization for data science
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Ready to Begin? Once you've completed these prerequisites, start with Module 1: AI Fundamentals to begin your AI Engineering journey.