Financial Machine Learning Training in Gurgaon, Delhi

Financial Machine Learning

Comprehensive training of Machine Learning Algorithms for Trading, Risk Management, Investment Management, Credit Scoring

Duration : 1 year Classes : 72 Days : Weekdays / Weekends

Overview
Curriculum
Pre-requisite
Review

Step into the future of quantitative finance with our cutting-edge course Financial Machine Learning. Designed for finance professionals, data scientists, and quantitative analysts, it bridges the gap between traditional financial modeling and modern ML techniques. Participants will explore how algorithms can uncover patterns in market data, optimize trading strategies, manage risk, and forecast financial outcomes. Using specialized libraries, learners shall work with real-world datasets including stock prices, economic indicators, and alternative data sources.

The training 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.

You shall 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.

The training is designed by a team of experienced Data Scientists who will help you to understand the WHYs and HOWs of FML. 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.

Target Audience:-
-Financial analysts and portfolio managers seeking to enhance their quantitative toolkit
-Data scientists and ML engineers working in fintech, banking, or hedge funds
-Graduate students in finance, economics, or data science preparing for careers in quantitative research
-Traders and quants interested in algorithmic strategy development and backtesting
-Researchers exploring the intersection of AI and financial markets

Learning Outcomes:-
-Understand the principles and challenges of applying machine learning to financial data
-Preprocess and engineer features from time-series, tick-level, and macroeconomic datasets
-Build supervised and unsupervised models for forecasting, classification, and anomaly detection
-Apply techniques such as cross-validation, walk-forward analysis, and backtesting for robust evaluation
-Use ensemble methods and deep learning architectures for predictive modeling in finance
-Implement risk management and portfolio optimization strategies using ML outputs
-Explore market microstructure, alpha generation, and execution modeling

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas

Course Prerequisites:

- Basic understanding of Statistics and Probability
- Programming experience in Python (NumPy, Pandas libraries)
- Knowledge of know-how of Finance Domain

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NB: All our trainings are always tailored to adopt to the Individual's Pace and Learning Depth.

NB: As a stepping stone, providing foundational knowledge, Bridge Courses are conducted periodically, to help students transition between different levels by closing knowledge gaps. These classes can be attended ad hoc, and are 'complimentary' for our bonafide students.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre and complete your registration.

This syllabus provides a structured, module-by-module breakdown of this comprehensive training program focused on participants overall performance, retention, and engagement, covering foundational theory, implementation, best industry practices and advanced techniques in the subject.

Module 1: Financial Data Fundamentals and Python Toolkit
✔ Introduction to Financial Markets
✔ Introduction to Machine Learning
✔ The Financial Data Landscape
✔ Handling Time Series
✔ Stationarity and Unit Roots
✔ Data Visualization for Finance

Module 2: Feature Engineering and Data Labeling for Finance
✔ Feature engineering techniques specific to financial data
✔ Classical Features
✔ Volume-Based Features sampling
✔ Fractionally Differentiated Features
✔ Labeling Techniques
✔ Feature Importance

Module 3: Time Series Models and Forecasting
✔ Autoregressive Models
✔ GARCH Models
✔ Traditional ML for Time Series constraint in training"
✔ Tree-Based Forecasting
✔ Walk-Forward Validation

Module 4: Advanced Machine Learning and Portfolio Construction
✔ Deep Learning for Sequences
✔ Risk Management and Attribution
✔ Portfolio Optimization
✔ Transaction Costs and Slippage
✔ Backtesting and Strategy Evaluation

Module 5: Use Cases Specific to FML
✔ 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

Module 6: Ethical, Regulatory, and Market Microstructure Challenges
✔ Market Microstructure
✔ Overfitting in Finance
✔ Regulatory Environment
✔ Ethical AI in Finance

Module 7: Capstone Project
✔ Design and implement a full trading strategy

NB:The curriculum is regularly subjected to updates, reflecting the latest industry trends & current technological advancements.

At Vyom Data Sciences, we aspire to provide the latest curriculum and most recent technology, as a standard component of all our trainings. Experts, with 25+ years of experience from USA, Europe and Australia, bring the best industry practices while designing and executing these trainings. All our trainers are Highly Qualified and Certified in their respective subject areas.

Kindly fill the DownloadPDF Form for the Brouchre with latest curriculum and full Training details.
Or you may Book an Appointment to collect your Brouchre.

Bhawana

Fabulous NLP + ML course

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.

Harish

Excellent ML course!

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.