AI Engineering

What You’ll Learn

AI Engineering Foundations

• AI system design and architecture
• Machine learning lifecycle (data → model → deployment)
• Model evaluation and performance optimization

Advanced Machine Learning

• Supervised, unsupervised and reinforcement learning
• Feature engineering and model selection
• Hyperparameter tuning and optimization

Deep Learning & Neural Networks

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN) and transformers basics

Natural Language Processing (NLP)

• Text processing and analysis
• Chatbots and language models
• Sentiment analysis and text classification

Computer Vision

• Image processing and object detection
• Face recognition and classification systems
• Real-world AI applications

AI Deployment & MLOps

• Model deployment (APIs, cloud services)
• Model monitoring and versioning
• CI/CD for machine learning systems

Tools & Technologies

• Python (NumPy, Pandas, Scikit-learn)
• TensorFlow / PyTorch
• OpenCV (Computer Vision)
• NLP Libraries (NLTK, SpaCy)
• Docker & Cloud Platforms (AWS basics)
• Git & GitHub

Who This Course Is For

• Developers and software engineers
• Data scientists and analysts
• AI enthusiasts
• Professionals transitioning into AI engineering

What You Gain

• Ability to build and deploy AI-powered systems
• Hands-on experience with real-world AI projects
• Strong foundation in machine learning and deep learning
• Skills to work as an AI Engineer or ML Engineer

Build the future with AI—engineer intelligent solutions that make real impact with Specmak Technology.