Eğitimimiz ücretlidir. 

Katılım ücretini öğrenmek için lütfen en altta yer alan formu doldurun

Eğitim Adı: Yapay Zeka İle Sermaye Piyasaları Yönetimi

Eğitim Süresi: 24 Gün / 168 Saat / Cumartesi Günleri

Tarihler: 21.12.2024-31.05.2025

Eğitim Şekli: Canlı Online Ders

Eğitimin Amacı: Eğitimin amacı yapay zekanın sermaye piyasalarında nasıl kullanılacağını pratik olarak öğretmektir.

Kurs 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 Algoritms

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: Working With Webex API

Module 16: Webex API Integration With OPEN AI API

Module 17: Parsing API With Postman

Module 18: 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: Managing Data Frames With Pandas and Numpy(Add/Delete/Change Index&Columns&Values)

Module 4: DataFrame Append, Concat, Transpose

Module 5: Pandas Values, value_counts, unique, nunique 

Module 6: Sorting Data With Multiple Queries at Data Frames IMKB Stock Market

Module 7: Excel With Python (Create Excel, Merge, Copy, Split Excel Files)

Module 8: Pandas Group By

Module 9: Working With Numpy 

Module 10: Analysing Big Data With Python 

Module 11: HTML Parsing For imdb.html

Module 12: HTML Parsing For StackOverFlow & GitHub Pages

Module 13: Regular Expressions Search, Match & Group

Module 14: Parsing Emails With Regex

Module 15: Intro to Matplotlib(plot, scatter, bar)

Module 16: Matplotlib Subplots & Pie

Module 17: Matplotlib With Yfinance For Stock Martket Visualization

Module 18: Customize Time Series Colorado Lab (rotation, indent, time format)

Module 19: Matplotlib Annotate & Arrowprops

Module 20: Advanced 3D Visualization With Matplotlib

Module 21: Data Visualization With Plotly

Module 22: Plotly With Size & Trendline Ordinary Least Squares Regression

Module 23: Data Visualization With Seaborn (scatterplot, barplot, lineplot, heatmap, pairplot, distplot, catplot, boxplot)

Module 24: Sql With Python CRUD(Create, Read, Update, Delete) 

Module 25: Parsing IMDB Sql Database With Python

Module 26: Data Analysis Heart Attack 

Module 27: Data Analysis Covid19

Module 28: Data Analysis Iphone Sales

Module 29: Data Analysis NYC Service Requests

Module 30: Data Analysis Netflix

Module 31: Data Analysis GDP(Gross Domestic Product)

Module 32: Data Analysis Financial Risk Analysis Of Mutual Funds Investments.

Module 33: Data Analysis Turkish Stock Market With Technical Indicators(SMA20,EMA20,RSI,MACD,HMA)

Module 34: Data Analysis Investment Portfolio Management

Part 4: Application Development With Tkinter

Module 1: Creating Widget With Tkinter Lab

Module 2: Restaurant Menu With Tkinter Lab

Module 3: Weather Forecast 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 Assistant Dashboard With Tkinter

Module 8: Introduction to Flask & How Web Server Works?

Module 9: API Development with Flask RestX

Module 10: API Endpoint & Query Parameters Management With Flask-RESTX Reqparse

Module 11: API Endpoint & Query Parameters With Multiple Choices by Flask-RESTX Reqparse

Module 12: SQL & API Integration: Get/Post/Put/Patch/Delete

Module 13: Flask-RESTX Upload/Display/Download Photo

Module 14: Flask-RESTX Upload/Display/Download Excel

Module 15: Flask-RESTX Chatbot With OpenAI

Module 16: Email With Python

Part 5: Machine Learning With Python

Module 1: What is Machine Learning

Module 2: Machine Learning Methods

Module 3: Supervised Machine Learning Linearr Regression

Module 4: Supervised Machine Learning Polynomial Regression  

Module 5: Multiple Linear Regression

Module 6: Multiple Polynomial Regression

Module 7: How to Save Data Model and Load

Module 8: Stock Market Prediction With Linear Regression

Module 9: Standard Scaler

Module 10: Lasso 

Module 11: Cross Validation & Pipeline

Module 12: Supervised Machine Learning Classification

Module 13: Label Encoder & Logistic Regression: Titanic

Module 14: Advanced Momentum Trading Strategies Via Volatility and Volume Indicators With RandomForestClassifier

Module 15: Multiple Text Classification With Machine Learning

Module 16: Multi Layer Perceptron

Part 6: DeepLearning With Python

Module 1: Introduction To Deep Neural Networks

Module 2: How to Design Deep Neural Networks

Module 3: House Price Prediction With Artificial Neural Networks & Early Stop Callback

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

Module 5: Heart Attack Prediction With Artificial Neural Networks& Multiple Early Stop CallBacks

Module 6: Intent Classification Finance Assistant ChatBot With Artificial Neural Networks & Natural Language Processing 

Module 6: Convolutional Neural Networks

Module 7: Digit Detection With Artificial Neural Networks: Mnist Dataset

Module 8: Data Preprocessing & Data Modelling For Image Processing

Module 9: Multiple Digit Detection With Convolutional Neural Networks

Module 10: Object Detection With Python

Module 11: Recurrent Neural Networks and LSTM

Module 12: Stock Market Prediction With Recurrent Neural Networks and LSTM

Module 13: Financial Anomaly Detection With Prophet

Module 14: Stock Market Prediction With Prophet

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

Module 13: Spam Email Classifier Recurrent Neural Networks and LSTM

Module 14: Deep Reinforcement Learning Stock Market Management

Part 7: Generative AI:

Module 1: What is Generative AI?

Module 2: The differences Of Generative AI From Traditional AI

Module 3: Semantic Similarity

Module 4: Sentimental Classification (Positive, Negative, Neutral)

Module 5: Summarization of Text 

Module 6: Intent-Classification

Module 7: Summarization of Generated Code

Module 8: Text-Generation

Module 9: Auto-Encoders

Module 10: Credit Card Fraud Detection with Autoencoders

Module 11: Working With Large Language Model Falcon

Module 12: Document-Based Question Answering With Falcon & Langchain

Module 13: Working With Large Language Model Llama 3.1

Module 14: Finance(Fin)-Llama

Module 15: Finetune Large Language Models With LoRa & Quantization 

Module 16: Building Multi-Document ReAct Agent For Financial Analysis using LlamaIndex and Qdrant 

Module 17: Time Series Forecasting With Chronos