AI Engineering Roadmap - Essential Skills and Career Guide for 2025

Overview

Master essential AI engineering skills from mathematics and machine learning to deep learning, LLMs, and ethical considerations - perfect for aspiring AI professionals seeking a comprehensive career roadmap.

Syllabus

• Introduction to AI Engineering
Overview of AI Engineering as a Career
Understanding the AI Ecosystem and Job Roles
• Fundamental Mathematics for AI
Linear Algebra
Calculus
Probability and Statistics
• Programming for AI
Python Basics
Libraries: NumPy, Pandas, and Matplotlib
• Machine Learning Foundations
Supervised vs. Unsupervised Learning
Key Algorithms: Regression, Classification, Clustering
Model Evaluation and Tuning
• Deep Learning Techniques
Neural Networks: Basics and Architectures
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and Transformers
• Large Language Models (LLMs)
Introduction to LLMs
Use Cases and Implementations
Fine-Tuning and Application in Real-World Scenarios
• AI Tools and Platforms
TensorFlow and PyTorch
Model Deployment: From Local to Cloud
• Ethical Considerations in AI
Bias and Fairness
Privacy and Data Security
AI Regulations and Compliance
• Industry Applications and Trends
AI in Healthcare, Finance, and Autonomous Systems
Emerging Technologies and Future Directions
• Career Development in AI
Building a Portfolio
Networking and Professional Growth
Navigating the AI Job Market in 2025
• Capstone Project
Integrating Learned Skills into a Comprehensive AI Project

Subjects

Data Science