Eğitim Adı: Applied AI Automation Bootcamp

Eğitim Süresi: 28 Gün / 224 Saat / Cumartesi Günleri + Bonus Online Video Dersler

Tarihler: 15.08.2026-20.02.2027

Eğitim Şekli: Canlı Online Ders

Eğitimin Amacı: Applied AI Automation Bootcamp, katılımcılara yalnızca yapay zekâ modelleri geliştirmeyi değil, bu modelleri kurumsal ortamlarda çalışabilen, ölçeklenebilir ve güvenli sistemlere dönüştürmeyi öğretir. Program kapsamında Python programlama, REST API geliştirme, Veri Bilimi, Makine Öğrenmesi (ML), Derin Öğrenme (DL), Generative AI, Agentic AI, RAG (Retrieval-Augmented Generation), çoklu ajan sistemleri, MCP Server, Qdrant vektör veritabanı, LDAP kimlik doğrulama, Docker container teknolojileri ve AI uygulamalarının güvenli dağıtımı uygulamalı olarak ele alınır. Katılımcılar eğitim süresince gerçek dünya senaryoları üzerinden AI destekli servisler, chatbotlar, RAG sistemleri, ajan tabanlı otomasyon çözümleri ve kurumsal API servisleri geliştirirler. Program sonunda katılımcılar; yapay zekâ modellerini, veri altyapılarını, API servislerini ve otomasyon süreçlerini bir arada tasarlayıp yönetebilen Applied AI Automation Engineer seviyesinde bilgi ve deneyim kazanırlar.

Eğitimimiz ücretlidir. Fiyat bilgisi almak için lütfen sayfanın alt kısmında bulunan başvuru formunu doldurunuz.

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: Introduction to Artificial Intelligence and Data Science

Module 2: Data Sources and Data Structures in Python

Module 3: Numerical Computing with NumPy

Module 4: Data Analysis with Pandas

Module 5: DataFrame Manipulation Techniques

Module 6: Exploring and Summarizing Data Frames

Module 7: Data Filtering and Sorting Techniques

Module 8: Data Aggregation and Grouping

Module 9: Analyzing Big Data With Python 

Module 10: Regex With Python

Module 11: Excel With Python

Module 12: How HTML Parsing Works?

Module 13: HTML Parsing With Python

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: Data Analysis NYC Service Requests

Module 27: Data Analysis Netflix

Module 28: Data Analysis AAL Sales Analysis

Module 29: Data Analysis Marketing Campaign Problem

Module 30: Data Analysis Restaurant Sales

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: How to Save Data Model and Load

Module 5: Encoding: One Hot Encoding, Binary Encoding,Label Encoding, Ordinal Encoding

Module 6: Multiple Linear & Polynomial Regressions

Module 7: Random Forest Regression, XGBoost Regression

Module 8: Standard Scaler

Module 9: Lasso

Module 10: Cross Validation & Pipeline

Module 11: Ridge Regression

Module 12: Supervised Machine Learning Classification

Module 13: Logistic Regression

Module 14: Principal Component Analysis (PCA)

Module 15: Pandas.get_dummies

Module 16: Decision Tree Classifier

Module 17: Comparing Classifion Models: Logistic Regression, Decision Tree, GaussianNB, SVC, Random Forest/XGBoost, K Neighbors

Module 18: Multi Layer Perceptron

Module 19: Unsupervised Machine Learning:

Module 20: K means Clustering

Module 21: RFM Analysis/Hierarchical Clustering/Customer Segmentation

Part 5: Deep Learning With Python

Module 1: Introduction To Deep Neural Networks

Module 2: How Deep Neural Networks Works?

Module 3: How to Design Artificial Neural Networks

Module 4: Multiple Early Stop Callbacks

Module 5: Multiple Regression With ANN & Early Stop Callback

Module 6: One Hot Encoding & Multiple Classification With ANN

Module 7: Multiple Early Stop Callbacks

Module 8: Binary Classification With ANN Via One Hot Encoding, OrdinalEncoder, Standard Scaler

Module 9: SMOTE (Synthetic Minority Over-sampling Technique)

Module 10: Anomaly Detection With SMOTE & ANN

Module 11: Convolutional Neural Networks

Module 12: Data Preprocessing For Image Processing

Module 13: Digit Detection With CNN: Mnist Dataset

Module 14: Multi Digit Detection With CNN For Blurred & Low Contrast Images

Module 15: Multi Digit Detection With CNN For Hand-Written Numbers

Module 16: Automatic License Plate Recognition (ALPR) With CNN

Module 17: Object Detection With Python With Yolo12

Module 18: Fine-tune Yolo12 With Custom Dataset

Module 19: Recurrent Neural Networks and LSTM

Module 20: Sales Prediction With RNN LSTM

Module 21: Stock Price Prediction With RNN LSTM

Module 22: Prophet

Module 23: Sales Prediction With Prophet

Module 24: Network Anomaly Detection With Prophet

Part 6: Generative AI With Python:

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: Agentic AI With Python:

Module 1: What is Agentic AI?

Module 2: Tool Calling

Module 3: FastMCP & MCP Server

Module 4: LangGraph ReAct Agent

Module 5: Multi-Agent Orchestration

Module 8: Intent Classification with Agents

Module 9: Automation Agent With MCP Server

Module 10: Gradio With Open AI API

Module 11: RAG (Retrieval-Augmented Generation)

Module 12: RAG (Token Embeddings LLM+Text Embeddings LLM+Faiss/Chroma+Gradio)

Module 13: How Qdrant Works?

Module 14: RAG With Token Embeddings LLM, Text Embeddings LLM, Qdrant & Gradio

Module 15: Multi-Document Conversational RAG With Qdrant & Open AI API

Module 16: Reasoning RAG With GPT OSS 20B

Module 17: Data Analysis With Llamafile Containers & Autonomous Code Generation Agent AutoGen

Module 18: Streamlit

Module 19: Knowledge Graph RAG

Module 20: Context Engineering

Module 21: SIM: Open-source platform to build and deploy AI agent workflows

Module 22: OpenClaw Personal AI Assistant

Module 23: Build Your Personal AI Assistant with Claude Code

Part 8: 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/HTML Files With Flask-RestX

Module 6: LDAP With Python

Module 7 Basic Authentication

Module 8: Developing ChatBot API With HTTPS, Basic Authentication & LDAP

Module 9: Accessing ChatBot API With HTTPS, Basic Authentication & LDAP

Module 10: Developing ChatBot API With HTTPS, Json Web Token & LDAP

Module 11: Accessing ChatBot API With HTTPS, Json Web Token & LDAP

Part 9: Docker:

Module 1: What is Docker?

Module 2: How Docker & Container Works

Module 3: Managing Nginx Container

Module 4: Docker Network

Module 5: Ollama Container LLMs

Module 6: Llamafile Container LLMs

Module 7: Ollama Container Agents & MCP Server

Module 8: Llamafile Container Agents & MCP Server

Module 9: Docker Volume For Qdrant

Module 10: Containerize Web Services With Docker

Module 11: Containerize API With Docker

Module 12: Deploy ChatBot Services With Load-Balancing, HTTPS, Json Web Token & LDAP

Module 13: RAG With Ollama Containers, Faiss/Chroma & Gradio

Module 14: RAG With Llamafile Containers, Faiss/Chroma & Gradio

Module 15: RAG With Ollama Containers, Qdrant & Gradio

Module 16: RAG With Llamafile Containers, Qdrant & Gradio

Module 17: Multi-Document Conversational RAG With Qdrant & Llamafile Containers

Module 18: Containerize, Secure, Scale RAG Application With Qdrant & OpenAI API

Module 19: Containerize, Secure, Scale RAG Application With Qdrant & Llamafile Containers

Bonus Online Video Eğitim İçeriği

Part 1: Data Science With Python

Module 1: Python

Module 2: Rest API With Python

Module 3: Data Preprocessing With Python

Module 4: Data Visualization With Python

Module 5: Data Analysis With Python

Module 6: RegEx With Python

Module 7: Excel With Python

Module 8: HTML Parsing With Python

Module 9: SQL With Python

Module 10: Log Management With Python

Part 2: GUI Development With Tkinter

Module 1: Creating Widget With Tkinter Lab

Module 2: Restaurant Menu With Tkinter Lab

Module 3: Weather Application With Tkinter & 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

Part 3: 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 4: 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 5: 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