AI Engineering Course

Hours: 168

Lessons per week: 3

Duration: 7 Months

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, Machine learning, Deep learning, NLP, Computer vision, LLM, 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.

Course modules

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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Who Can Enroll?

Students and Recent Graduates

For young people who want to gain high-demand IT knowledge and skills and build a competitive advantage in the job market

Those who want to acquire new skills

Those who want to gain new knowledge and skills in artificial intelligence and data science

Mathematics and Logic

Those with a good level of mathematical knowledge and logical reasoning skills

For those who want to change their career path

For those working in other fields who want to build their future in IT and Developer

Course Instructors

All instructors at JET Academy are highly experienced professionals in their field, distinguished by their pedagogical skills

Yusıf Yagublu

AI Engineer (Proxima Tech Solutions)

Omar Mammadov

Mentor (JET Academy)

Frequently Asked Questions

Below you can find answers to the questions we are most frequently asked.

Enroll Now

Other Fields

AI Engineering Course

Hours: 168

Lessons per week: 3

Duration: 7 Months

Who is an Artificial Intelligence Engineer and what do they do?

Course modules

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

AI Engineering Course

Course modules

Module 1: Python for AI Engineering

Module 2: Mathematics & Statistics for ML

Module 3: Applied Machine Learning

Module 4: Deep Learning & NLP

Module 5: Computer Vision & Time Series

Module 6: MLOps & Production AI

Module 7: Generative AI · LLM Engineering · AI Agents

Enroll Now

Who Can Enroll?

Students and Recent Graduates

Those who want to acquire new skills

Mathematics and Logic

For those who want to change their career path

Course Instructors

Frequently Asked Questions

Enroll Now

Other Fields

AI Engineering Course

Course modules

Module 1: Python for AI Engineering

Module 2: Mathematics & Statistics for ML

Module 3: Applied Machine Learning

Module 4: Deep Learning & NLP

Module 5: Computer Vision & Time Series

Module 6: MLOps & Production AI

Module 7: Generative AI · LLM Engineering · AI Agents

Enroll Now

Who Can Enroll?

Students and Recent Graduates

Those who want to acquire new skills

Mathematics and Logic

For those who want to change their career path

Course Instructors

Frequently Asked Questions

Enroll Now

Other Fields