AI Engineering & Machine Learning Course

AI courseMachine Learning courseArtificial Intelligence trainingData Science coursePython for AIAI Engineering courseML trainingLearn AI onlineData analytics courseAI bootcamp

About the Course

Artificial Intelligence is no longer the future — it's one of the most in-demand skills today. The AI Engineering & Machine Learning course is designed to take you from beginner to job-ready by working on real-world projects.

In this course, you will learn:

Python programming for AI and data analysis
Data preprocessing and analysis techniques
Core Machine Learning algorithms (Regression, Classification, Clustering)
Model building, evaluation, and optimization
Hands-on experience with real business cases

This course is ideal for beginners and professionals who want to transition into AI, Data Science, or Machine Learning roles.

By the end of the course:

✔️ You can pursue a career as an AI Engineer or ML Engineer

✔️ You will build a strong project portfolio

✔️ You will gain practical data-driven decision-making skills

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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Course Instructors

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

Gunduz Haydarov

Python Back-End Development instructor

Mammadali Mammadaliyev

Python Back-End Development instructor

Murad Orujov

Full Stack Development instructor

Shamil Vasiyev

Full Stack Development instructor

Ramin Mammadazada

Full Stack Development instructor

Gulnara Dashdamirova

Microsoft Office Programs instructor

Vahid Teymurlu

Data Analytics instructor

Mushvig Manafli

Java Back-End Development instructor

Huseynaga Gurbanov

Practical Computer Knowledge instructor

Jeyhun Sharafkhanov

IT Help Desk and Network Administration Trainer

Arif Mukhtarov

IT Help Desk instructor

Tural Karimov

IT Help Desk instructor

Nazli Ibrahimli

English instructor

Nijat Babayev

Cybersecurity instructor

Huseyn Rustamli

Java Back-End Programming mentor

Yusif Teymurlu

Python Back-End Development mentor

Nijat Suleymanov

Cybersecurity Mentor

Yusıf Yaqublu

AI Engineering Development instructor

Frequently Asked Questions

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

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Other Fields

AI Engineering & Machine Learning Course

AI courseMachine Learning courseArtificial Intelligence trainingData Science coursePython for AIAI Engineering courseML trainingLearn AI onlineData analytics courseAI bootcamp

About the Course

In this course, you will learn:

Python programming for AI and data analysis
Data preprocessing and analysis techniques
Core Machine Learning algorithms (Regression, Classification, Clustering)
Model building, evaluation, and optimization
Hands-on experience with real business cases

This course is ideal for beginners and professionals who want to transition into AI, Data Science, or Machine Learning roles.

By the end of the course:

✔️ You can pursue a career as an AI Engineer or ML Engineer

✔️ You will build a strong project portfolio

✔️ You will gain practical data-driven decision-making skills

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