Eğitim Adı: Applied AI Systems Engineering Bootcamp

Eğitim Süresi: 24 Gün / 192 Saat / Cumartesi Günleri + Bonus Online Video Dersler

Tarihler: 27.12.2025-20.06.2026

Eğitim Şekli: Canlı Online Ders

Eğitimin Amacı: Bu bootcamp, katılımcılara yapay zekâ modellerinin ötesine geçerek, onları gerçek dünyada çalışabilen sistemlere dönüştürme becerisi kazandırır. Python, ML/DL, Generative AI ve Agentic AI tekniklerini; REST API’ler, Docker container’lar, Qdrant veritabanı, LDAP kimlik doğrulama, loglama ve çoklu ajan mimarileriyle birleştirerek, kurumsal ölçekte AI çözümleri geliştirmenizi sağlar. Katılımcılar, program sonunda AI modellerini, altyapıyı ve otomasyonu bir arada yöneten mühendisler haline gelirler.

Eğitimimiz ücretlidir. Lütfen fiyat bilgisi almak için en alt kısımda yer alan formu doldurun.

Online Canlı Eğitim İçeriği

Part 1: Python

Module 1: Language Overview

Module 2: Standard Data Types

Module 3: Flow Control

Module 4: Functions

Module 5: Lists and Tuples

Module 6: Dictionaries

Module 7: External Libraries

Module 8: Basic File Operations

Module 9: Exception Handling

Module 10: OS Operations & File Management

Module 11: Advanced Algorithms

Module 12: Log File Index Management

Module 13: Python Code Index Management At Any Folder

Module 14: Object Oriented Programming: Encapsulation & Inheritance

Part 2: REST API With Python

Module 1: What is Model Driven Automation

Module 2: What is HTTP ?

Module 3: How HTTP works ?

Module 4: What is Rest API

Module 5: What are API Data Formats?

Module 6: List Comprehension

Module 7: Parsing JSON With Postman

Module 8: Parsing Capital Cities JSON With Python 

Module 9: Parsing Star War JSON With Python 

Module 10: Parsing Game Of Thrones JSON With Python 

Module 11: Parsing Public Colorado Population API With Python 

Module 12: Parsing News API With Python 

Module 13: Parsing NASA API With Python 

Module 14: Working With Open AI API

Module 15: Parsing API With Postman

Module 16: Parsing Forex API & Bitcoin API With Python

Part 3: Data Science

Module 1: What is AI, Data Science, Machine Learning, Deep Learning

Module 2: Creating Data Frames From JSON, Python Data Types, Excel, CSV

Module 3: Working With Numpy

Module 4: Managing Data Frames With Pandas and Numpy

Module 5: Data Frame Append, Concat, Transpose

Module 6: Pandas Values, value_counts, unique, nunique 

Module 7: Sorting Data With Multiple Queries at Data Frames IMKB Stock Market

Module 8: Pandas Group By

Module 9: Analyzing Big Data With Python 

Module 10: How HTML Parsing Works?

Module 11: HTML Parsing For imdb.html

Module 12: HTML Parsing For StackOverFlow

Module 13: HTML Parsing For GitHub

Module 14: Intro to Matplotlib(plot, scatter, bar)

Module 15: Matplotlib Subplots & Pie

Module 16: Matplotlib With Yfinance For Stock Market Visualization

Module 17: Customize Time Series Colorado Lab (rotation, indent, time format)

Module 18: Matplotlib Annotate & Arrowprops

Module 19: Advanced 3D Visualization With Matplotlib

Module 20: Data Visualization With Plotly

Module 21: Plotly With Size & Trendline Ordinary Least Squares Regression

Module 22: Data Visualization With Seaborn

Module 23: Data Analysis Heart Attack

Module 24: Data Analysis Covid19

Module 25: Data Analysis Iphone Sales

Module 26: Project Data Analysis NYC Service Requests

Module 27: Project Data Analysis Netflix

Module 28: Project Data Analysis Sales Analysis

Part 4: Machine Learning With Python

Module 1: What is Machine Learning

Module 2: Machine Learning Methods

Module 3: Supervised Machine Learning Linear Regression, Polynomial Regression

Module 4: Multiple Linear Regression, Multiple Polynomial Regression

Module 5: How to Save Data Model and Load

Module 6: Random Forest Regression, XGBoost Regression

Module 7: Standard Scaler

Module 8: Lasso

Module 9: Cross Validation & Pipeline

Module 10: Supervised Machine Learning Classification: Label Encoder & Logistic Regression

Module 11: Supervised Machine Learning Classification: Decision Tree Classifier

Module 12: Cross Validation For Logistic Regression, Decision Tree Classifier, GaussianNB, SVC, Random Forest Classifier, K Neighbors Classifier, XGBoost Classifier

Module 13: Multi Layer Perceptron

Module 14: Unsupervised Machine Learning: K means Clustering

Module 15: Unsupervised Machine Learning: RFM Analysis/Hierarchical Clustering/Customer Segmentation

Part 5: Deep Learning With Python

Module 1: Introduction To Deep Neural Networks

Module 2: How to Design Deep Neural Networks

Module 3: Regression With Artificial Neural Networks & Early Stop Callback

Module 4: One Hot Encoder & Multiple Classification With Artificial Neural Networks

Module 5: Binary Classification With Artificial Neural Networks & Multiple Early Stop Callbacks

Module 6: Convolutional Neural Networks

Module 7: Data Preprocessing & Data Modelling For Image Processing

Module 8: Digit Detection With Artificial Neural Networks: Mnist Dataset

Module 9: Object Detection With Python

Module 10: Recurrent Neural Networks and LSTM

Module 11: Sales Prediction With Recurrent Neural Networks and LSTM

Module 12: Network Anomaly Detection With Recurrent Neural Networks and LSTM

Module 13: Stock Market Prediction With Prophet

Module 14: Network Anomaly Detection With Prophet

Part 6: Generative AI:

Module 1: What is Generative AI/Generative vs Traditional AI

Module 2: Embeddings: Text Embeddings & Token Embeddings

Module 3: How To Use Open AI API

Module 4: Text & Code Generation With Open AI API

Module 5: Generating Photo With Open AI API

Module 6: Ask Questions About Photo With Open AI API

Module 7: Change Current Photo Details With Open AI API

Module 8: Generating Video With Open AI API

Module 9: Ask Questions About Video With Open AI API

Module 10: Change Current Video Details With Open AI API

Module 11: Working With Webex API

Module 12: Webex API Integration With Open AI API

Module 13: Text To Speech & Speech To Text With Open AI API

Module 14: Data Analysis With Open AI API & Autonomous Code Generation Agent AutoGen

Module 15: How To Use Gemini API

Module 16: Semantic Similarity With Open Source LLMs

Module 17: Sentiment Classification With Open Source LLMs

Module 18: Text Summarization With Open Source LLMs

Module 19: Intent Classification With Open Source LLMs

Module 20: Code Summarization With Open Source LLMs

Module 21: Text Generation With Open Source LLMs

Module 22: Working with Open Source LLMs via Ollama

Module 23: Finetune Open Source LLMs With LLMs

Module 24: Working With Open Source Vision LLMs

Module 25: Finetune Open Source Vision LLMs With Quantization & LoRa

Module 26: Reasoning Models

Part 7: API Development With Python:

Module 1: API Development With Flask-RestX

Module 2: Rest API Query Management At API Development

Module 3: HTTPS: SSL Certificate Creation With Python At Ubuntu

Module 4: Rest API With HTTPS

Module 5: Uploading & Downloading Excel/CSV/JSON Files With Flask-RestX

Module 6: Sql With Python CRUD(Create, Read, Update, Delete)

Module 7: PostGreSQL Insallation At Ubuntu

Module 8: PostGreSQL Configuration At Ubuntu

Module 9: PostGreSQL With Python

Module 10: LDAP With Python

Module 11: Developing Database Management API With HTTPS, Vault, Json Web Token, LDAP & PostGreSQL

Module 12: Accessing Database Management API via Python With HTTP Methods: Get, Post, Put, Patch, Delete

Part 8: Docker:

Module 1: What is Docker?

Module 2: How Docker & Container Works

Module 3: Managing Nginx Container

Module 4: Docker Network

Module 5: Docker Volume

Module 6: Containerize Web Services With Docker

Module 7: Containerize API With Docker

Module 8: Deploy Database Management Services With Load-Balancing, Containerized API, PostGreSQL andLDAP

Part 9: Agentic AI:

Module 1: What is Agentic AI?

Module 2: Ollama Container LLMs

Module 3: Llamafile Container LLMs

Module 4: Tool Calling

Module 5: FastMCP & MCP Server

Module 6: LangGraph ReAct Agent

Module 7: Multi-Agent Orchestration

Module 8: Intent Classification with Agents

Module 9: Ollama Container Agents & MCP Server

Module 10: Llamafile Container Agents & MCP Server

Module 11: Automation Agent With MCP Server

Module 12: Gradio With Open AI API

Module 13: Agent-based RAG

Module 14: Document QA (Token Embeddings LLM+Text Embeddings LLM+Faiss/Chroma+Gradio)

Module 15: Document QA With Ollama Containers +Faiss/Chroma + Gradio

Module 16: Document QA With Llamafile Containers +Faiss/Chroma + Gradio

Module 17: How Qdrant Works?

Module 18: Document QA (Token Embeddings LLM+Text Embeddings LLM+ Qdrant +Gradio)

Module 19: Document QA With Ollama Containers + Qdrant + Gradio

Module 20: Document QA With Llamafile Containers + Qdrant + Gradio

Module 21: Multi-Document Conversational RAG With Qdrant & Open AI API

Module 22: Multi-Document Conversational RAG With Qdrant & Llamafile Containers

Module 23: Reasoning RAG With GPT OSS 20B

Module 24: Data Analysis With Llamafile Containers + Autonomous Code Generation Agent AutoGen

Module 25: Streamlit Llamafile Containers

Module 26: Containerize, Secure, Scale RAG Application With Qdrant and OpenAI API

Module 27: Containerize, Secure, Scale RAG Application With Qdrant and Open Source LLMs

Bonus Online Video Eğitim İçeriği

Part 1: RegEx With Python

Module 1: Parsing Star Wars Episode 3 Scenario

Module 2: Parsing Words.txt with RegEx

Module 3: RegEx search match group

Module 4: Parsing Emails With RegEx.

Part 2: Email With Python

Module 1: smtplib & email.message

Module 2: Yagmail

Module 3: Attached Email with Email.Message

Module 4: Time Scheduled Mail

Module 5: Bitcoin Api Auto Alarm Management With Email

Module 6: Email With Excel

Module 7: Sending attached file as html in email

Module 8: Sending Email within VPN

Part 3: Excel With Python

Module 1: Create Excel With Openpyxl

Module 2: Creating Data Frame From Excel

Module 3: Copy Sheet-Delete Row & Columns at Excel

Module 4: Seperate & Save Sheets As New WorkBooks at Excel

Module 5: Merge & Seperate Workbooks into New Workbooks at Excel

Module 6: Excel to TXT

Module 7: Receive Specific Data From Excel Sheets at Excel Workbook.

Part 4: GUI Development With Tkinter

Module 1: Creating Widget With Tkinter Lab

Module 2: Restaurant Menu With Tkinter Lab

Module 3: Weather Application With Tkinter, SQL & JSON

Module 4: Making Mp3 Player Application With Tkinter

Module 5: Managing Charts With Tkinter

Module 6: Managing Hyperlinked URLs With Tkinter

Module 7: Making Finance Dashboard Application With Tkinter

Module 8: Multiple Digit Detection App With Tkinter

Part 5: Linux

Module 1: Login: How to login and gather info about system

Module 2: History: Linux History and Distributions

Module 3: Basic Management Commands: alias, ls, cd, pwd

Module 4: BASH: Shell types: Bourne, Korn, Bash, Set Shell variables, env

Module 5: BASH: I/O Redirection: stdin, stdout, stderr, and sudo

Module 6: VI: Vi editor basic and advanced features

Module 7: Permissions

Module 8: Archiving: tar, gzip, bzip2, xz

Module 9: Installation: Linux operating system installation

Module 10: Filesystem Management

Module 11: Services: Management

Module 12: Account Management

Module 13: Process Management

Module 14: Software Management

Module 15: Log Management

Part 6: Docker

Module 1: Docker-Computing Evolution

Module 2: Docker-Software & Application Development-Waterfall versus Agile

Module 3: Docker-Software Test Types

Module 4: Docker-Construct A Python Unit Test Lab

Module 5: Docker-Introduction to DevOps

Module 6: Docker-Kubernetes Architecture

Module 7: Docker Architecture

Module 8: Docker on Linux Mac Windows

Module 9: How Docker Works

Module 10: Docker Host Installation On Centos v7.0

Module 11: NGINX Web Service Container Installation

Module 12: Interpret A Dockerfile

Module 13: Working With Container Images & Docker Hub

Bonus: API Development With Python

Module 14: Containerize Flask Web Service

Module 15: Containerize Flask Web Service With Virtual Environment

Module 16: Containerize An API

Module 17: Docker Cluster Services

Module 18: Container Network

Module 19: Volumes

Module 20: Containerize An Application Using Docker

Module 21: Docker-Compose

Module 22: Docker Swarm

Module 23: Containerize An AI Chatbot

Module 24: Load Balance An AI Application

Part 7: RAG in Azure with OpenAI API

Module 1: Creating Azure resources using the portal

Module 2: Creating Azure resources using command line

Module 3: Connecting to OpenAI API

Module 4: Counting the tokens for all documents

Module 5: Cleaning the markdown files

Module 6: Creating the embedding vector

Module 7: Chunking the documents to lower the number of tokens

Module 8: Creating Search Index in Azure AI Search

Module 9: Uploading the chunks to AI Search

Module 10: Searching using Vector embedding

Module 11: Chatting with OpenAI API with documents