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AI Engineering Course
Hours: 168
Lessons per week: 3
Duration: 7 Months
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About the Course
Who is an Artificial Intelligence Engineer and what do they do?
An Artificial Intelligence Engineer is a specialist who develops artificial intelligence systems, machine learning models and AI-based solutions for real digital products. Artificial Intelligence Engineering combines programming, data analysis, machine learning, deep learning, large language models and automation technologies.
AI Engineers work on chatbots, recommendation systems, semantic search, document analysis, computer vision, natural language processing and automated AI tools. Their role is not limited to building models; they also integrate AI solutions into real systems and ensure that they work efficiently, securely and reliably.
The Artificial Intelligence Engineering Course is designed for those who want to build a career in AI and develop the skills required to create intelligent digital products. The program covers Python, data processing, machine learning, deep learning, NLP, computer vision, LLMs, RAG systems and AI product development.
At JET Academy, the AI Engineering Course helps students understand how artificial intelligence works, build AI models, apply their knowledge through real projects and prepare for a career in one of the most in-demand technology fields.
- Data Types, Variables, Operators, Type Casting
- Object-Oriented Programming: Classes, Inheritance, Polymorphism, Dunder Methods
- Matplotlib and Seaborn: Statistical charts, Subplots, Styling
- Mini-project: End-to-end exploratory data analysis (EDA) on a real dataset
- Vectors, Matrices, Matrix Operations, Dot Product
- Norms, Distance Metrics (Euclidean, Cosine, Manhattan)
- Descriptive Statistics: Mean, Median, Variance, Std Dev, Skewness, Kurtosis
- Probability Basics: Random Variables, PDF, PMF, CDF
- Assumptions: Homoscedasticity, Multicollinearity (VIF), Durbin-Watson
- Model Interpretability: SHAP values, LIME, Feature Importance plots
- Evaluation: Confusion Matrix, ROC-AUC, F1, Precision-Recall curves
- RFM Analysis for customer segmentation
- Loss Functions: BCE, CCE, MSE; Optimizers: SGD, Adam, AdamW
- Hyperparameter tuning with Optuna for neural networks
- Building a Q&A chatbot over documents (end-to-end RAG project)
- LangChain basics: Chains, PromptTemplates, Retrievers, Document Loaders
- Classic Architectures: VGG-16, ResNet-50, EfficientNet
- OCR basics: Tesseract, EasyOCR — practical text extraction
- Time Series concepts: Trend, Seasonality, Stationarity
- Evaluation: MAE, RMSE, MAPE, SMAPE
- MLOps overview: ML system design, pain points in production
- Interactive Applications: Streamlit and Gradio — demos and internal tools
- Model Monitoring: Data Drift (Evidently AI), Model Drift, alerting
- Cost optimization and latency trade-offs in production ML
- LLM Evaluation: BLEU, ROUGE, BERTScore, RAGAS framework, human eval
- AI Ethics and Responsible AI: bias, fairness, transparency, guardrails
- RAG evaluation with RAGAS: context precision, faithfulness, answer relevancy
- Capstone Project: Production-grade RAG + Agent system with FastAPI deployment
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Frequently Asked Questions
Below you can find answers to the questions we are most frequently asked.