Description
The Use of Artificial Intelligence in Banking training is a 5-day online program that covers banking applications of artificial intelligence and machine learning through data analysis, Python, and model development. Participants work through preparing and analyzing financial data sets and evaluating model results.
The training connects artificial intelligence concepts to examples from the financial sector and covers the data science business process and application development on financial data. The regression, classification, and clustering topics in the curriculum are listed in detail below.
Training objectives
- Get to know the core concepts of artificial intelligence and machine learning and their areas of use in banking.
- Examine the financial data analysis, data preparation, and model development process through examples.
- Evaluate Python and machine learning tools in the context of applying them to banking data.
Who should attend?
- Banking sector professionals
- Data scientists and analysts
- Information technology and software development teams
- Managers looking for innovative solutions in banking
Outline
Artificial Intelligence Fundamentals
- 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?
- What are the Future of Artificial Intelligence and Its Approaches?
- Overview of Artificial Intelligence and Its Applications
- Artificial Intelligence Sub-Branches
- Machine Learning
- Differences Between Machine Learning and Traditional Programming
- What are the Types of Machine Learning?
- Application Examples of Machine Learning in the Financial Sector
- Deep Learning
- Differences Between Machine Learning and Deep Learning
- Application Examples of Deep Learning in the Financial Sector
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)
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Application Examples of Data Analytics in the Financial Sector
How to Apply Data Science in the Business Process Cycle
- CRISP-DM Methodology
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
Developing Data Science Applications with Python
- Python Programming Language Overview
- Development Environments Used to Develop Programs with Python
- Anaconda and the Python Development Environment (Spyder, Jupyter Notebook)
- Google Colaboratory (The Environment Where Applications Will Be Carried Out During the Training)
- Using a Virtual Environment
- Fundamentals of Python Programming Language
- Modules and Packages
- Pattern Recognition with RE (Regular Expressions)
Data Literacy
- Data Set Loading/Reading from File (Excel, CSV, etc.)
- Display of Data
- Obtaining Information About the Size of the Data
- Sampling in Data
- Data Types in Data
- Obtaining Basic Statistical Information
- Measures of Central Tendency: mode, median, arithmetic mean
- Measures of Dispersion: standard deviation, variance, covariance, correlation
Data Preparation and Cleaning
- Data Recognition
- Attribute Viewing and Selecting
- Sorting and Grouping
- Operations on Attributes (Adding, Deleting, Updating)
- Operations on Observations (Display, Insert, Delete)
- Data Filtering
- Filtering with Dictionaries and Lists
- Filtering with Query
- Operations on Missing Data (Detection, Deletion, Filling)
- Operations on Repetitive Data (Detection, Cleaning)
- Data Conversion Operations
- Data Scaling and Normalization,
- Aggregation
- Categorical Data
- Detecting Outlier/Extreme Data
- Data Visualization
- Plotting Charts (line, bar, pie, heatmap, etc.)
- Working with Charts
Technical Concepts in the Machine Learning Application Process
- 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
- Machine Learning Models Success Evaluation Methods
- Confusion Matrix
- Accuracy, Recall, Precision
- 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 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 Neighbours (KNN)
- Classification with Decision Trees (CART)
- Clustering Models (Theory, Model, Prediction) (Unsupervised Learning Application)
- Clustering with the K-Means Algorithm
Prerequisites
The current training description does not specify a mandatory prerequisite for attendance. You can confirm your readiness for the Python and data analysis topics when you submit your training request.
Related Courses and Quote
To go deeper with credit risk examples, see the Credit Risk and AI Applications Training page, and for Python fundamentals, see the Python Programming for Financiers Training page.
To get a training plan and price quote for your organization, you can fill out the Contact Form on this page. For other ways to reach us, use the BlueMark Academy contact page.
Frequently Asked Questions
How many days does the Use of Artificial Intelligence in Banking training last?
The training lasts 5 days.
Is the training held online?
Yes. The page lists the training format as Online / In Person.
Who can attend this training?
The target audience is banking professionals, data scientists, analysts, IT and software development teams, and managers looking for innovative solutions.
Does the training include hands-on work with Python?
Yes. The curriculum includes Python development environments, Google Colaboratory, and data science applications on financial data.
Which machine learning topics are covered?
The curriculum covers regression, classification, clustering, feature engineering, and model performance evaluation.
Are there any prerequisites for attending?
The current training page does not specify a prerequisite for attendance. You can confirm the background knowledge you need when you submit your training request.