Comprehensive training of Machine Learning Algorithms for Trading, Risk Management, Investment Management, Credit Scoring
Classes : 50 Days : 6 months Duration : Weekdays / Weekends
This course is designed to provide a comprehensive understanding of machine learning techniques applied to financial markets. It combines theoretical knowledge with practical applications to equip participants with the skills necessary to build, evaluate, and deploy machine learning models in the financial domain.
We shall introduce you to the exciting and rapidly evolving field of Financial Machine Learning (FML). You will learn how machine learning algorithms are used to solve real-world problems in finance, including:
- Trading: Predicting market movements, identifying trading opportunities, and automating trading strategies.
- Risk Management: Assessing and managing financial risks, detecting fraud, and building robust financial models.
- Investment Management: Building optimal portfolios, predicting asset prices, and making informed investment decisions.
- Credit Scoring: Assessing creditworthiness, predicting default rates, and optimizing loan decisions.
By the end of this course, you will be able to:
- Understand the basic concepts of machine learning and how they apply to finance.
- Implement various machine learning algorithms for financial tasks.
- Evaluate the performance of machine learning models.
- Apply machine learning to solve real-world financial problems.
This training has been designed by experienced Data Scientists who will help you to understand the WHYs and HOWs of Algorithm Engineering.
Experts from the field of Maths, BFSI, IT, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia
This course aims to provide participants with a strong foundation in applying machine learning techniques to financial markets, preparing them to tackle real-world problems in financial domain.
Who this course is for:
- Bank Professionals
- People seeking technical jobs in BFSI Domain
- Software Engineers
- Computer Science undergraduates
- Data Scientists
- Designers and Analysts
- Team Leaders
- Basic understanding of statistics and probability
- Programming experience in Python (NumPy, Pandas libraries)
- Knowledge of know-how of Finance Domain
- Introduction to Financial Markets
- Introduction to Machine Learning
- Ethical considerations in financial machine learning
- Data Preprocessing for financial datasets
- Feature engineering techniques specific to financial data
- Time Series Analysis and Forecasting
- Understanding time series data in finance
- Time series modeling using ARIMA, GARCH, and other techniques
- Forecasting stock prices and volatility using time series models
- Predictive modeling for stock price movement
- Risk assessment and portfolio optimization using supervised learning
- Anomaly detection and its applications in fraud detection
- Clustering techniques for market segmentation
- Natural Language Processing (NLP) for sentiment analysis in finance
- Model evaluation, validation, and deployment in financial settings
- Case Studies
- Final project: Design and implement a financial machine learning model
I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.
The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.