Fundamentals of Artificial Intelligence
- What is Artificial Intelligence?
- What are the Application Areas of Artificial Intelligence? Real Life Examples.
- What are the Applications of Artificial Intelligence in the Financial Sector?
- The Future and Approaches of Artificial Intelligence.
Fundamentals of Data Science
- What is Data Analysis? What Can Be Done with Data Analysis?
- What is Data Science?
- What are the Elements of Data Science?
- How Do the Stages of Extracting Useful Information from Data Work? (Data Analytics)
- Application Examples of Data Analytics in the Financial Sector.
How Data Science is Applied in the Business Process Cycle
- CRISP-DM Methodology
- Basic Stages of Application Development in Data Science
Machine Learning
- What is Machine Learning? How Does Machine Learning Work?
- What are the Differences Between Machine Learning and Traditional Programming?
- What are the Types of Machine Learning?
- In Which Areas Do Machine Learning Algorithms Provide Solutions?
- The Future of Machine Learning
- Application Examples of Machine Learning in the Financial Sector.
Deep Learning
- What is Deep Learning? How Does Deep Learning Work?
- Differences Between Machine Learning and Deep Learning
- Application Examples of Deep Learning in the Financial Sector.
CRM Analytics
- What is CRM Analytics?
- What is RFM Analysis?
- What is the Relationship Between Machine Learning and CRM Analytics?
Developing Data Science (Artificial Intelligence) Applications – Exploratory Data Analysis
Developing Data Science Applications with Python
- Overview of the Python Programming Language
- Development Environments Used for Developing Programs with Python
- Basics of the Python Programming Language
Data Literacy
- Loading/Reading Data Sets from Files (Excel, CSV, etc.)
- Data Representation
- Understanding Data Dimensions
- Sampling in Data
- Data Types
- Basic Statistical Information
Data Preparation and Cleaning
- Understanding Data
- Viewing and Selecting Features
- Sorting and Grouping
- Operations on Features
- Operations on Observations
- Data Filtering
- Missing Data
- Duplicate Data
- Data Transformation Operations
- Detecting Outliers
- Data Visualization
Data Analysis on Financial Data Sets
- Financial Analysis on Credit Risk Data Set
- Analysis on Temporal Data Set (fetching and analyzing data from Yahoo Finance)
Developing Data Science (Artificial Intelligence) Applications – Machine Learning
Basic Concepts and Terminology
- Problem Types (Regression, Classification)
- Model
- Splitting the Data Set into Training and Test Sets
- Overfitting
- Model Validation
Types of Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Feature Engineering
- Outlier Detection
- Data Cleaning
- Data Transformation (encoding, scaling)
- Data Reduction
- Feature Extraction
Methods for Evaluating the Success of Machine Learning Models
- Confusion Matrix
- R2 Score
- F1 Score
- AUC-ROC Curve
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
Scikit-Learn Module and Machine Learning
Machine Learning Algorithms/Models on Financial Data Sets and Application Development
Regression Models (Theory, Model, Prediction)
- Simple Linear
- Multiple Linear
Classification Models (Theory, Model, Prediction)
- Classification with Logistic Regression
- Classification with K-Nearest Neighbors (KNN)
- Classification with Decision Trees (CART)
- Classification with Support Vector Machines (SVM)
Clustering Models (Theory, Model, Prediction) (Unsupervised Learning Application)
- Clustering with K-Means Algorithm
- Hierarchical Clustering
Boosting Models (Theory, Model, Prediction)
- Gradient Boosting
- XGBoost
- LightGBM
RFM (Recency, Frequency, Monetary) Analysis within CRM Analytics
- Customer Segmentation with RFM Analysis
- Clustering with K-Means (K-Means Clustering)
Real-Time (Streaming Data) Machine Learning Application
- Developing a Prediction Application for an ML Model with Data Pulled from an Online Database (Firebase)
Saving and Transferring Machine Learning Models to Other Applications
- Methods for Using Data Science Models in Different Applications
- Model Transfer with Pickle
- Model Transfer with Joblib
- Converting to Native Code with m2cgen
