Machine Learning for Financial Analysis
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Understand AI Fundamentals
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Identify Business Opportunities
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Plan & Execute Projects
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Evaluate Impact & Ethics
60 Hours
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Beginner level
No Prior Knowledge Required
Flexible Learning
Available in Online, Offline, and
Hybrid Modes
60 Hours
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Program Overview
This course brings together finance and machine learning, training you to apply ML methods to real-world financial challenges like forecasting, fraud detection, credit risk, and portfolio management.
Key Features
Short-duration / modular format (ranges from 1 day to 30+ hours) for focused learning
Hybrid delivery mode with hands-on labs & projects
Beginner-friendly; minimal prerequisites (basic cybersecurity or networking background useful)
Instruction + exam prep sessions + practice questions
Real-world use cases: threat detection, phishing, malware analysis, anomaly detection, adversarial attacks, etc.
Certificate awarded on completion
Skills Covered
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Generative AI + ML models (GANs, VAEs, LLM basics) in security
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Adversarial attacks & defenses, threat modelling
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Network anomaly & intrusion detection
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Malware & phishing detection and analysis
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Incident response automation & threat intelligence forecasting
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Ethical, legal, and privacy concerns in AI security
Course Curriculum
Foundations of ML in Finance
Module 1 • 10 hour to complete
Module 1 • 10 hour to complete
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Role of ML in modern financial markets
• Financial data types: structured (prices, ratios), unstructured (news, filings)
• Time series vs cross-sectional data in finance
• Key challenges: noise, non-stationarity, volatility
• Quiz: ML & Finance Fundamentals
• Financial data types: structured (prices, ratios), unstructured (news, filings)
• Time series vs cross-sectional data in finance
• Key challenges: noise, non-stationarity, volatility
• Quiz: ML & Finance Fundamentals
Data Engineering for Financial Analysis
Module 2 • 10 hour to complete
Module 2 • 10 hour to complete
•
Collecting data from APIs (Bloomberg, Yahoo Finance, Quandl)
• Data cleaning & preprocessing (missing values, anomalies)
• Feature engineering for financial ratios & indicators
• Sentiment data from news & social media
• Hands-on: Preparing a dataset for stock/credit risk modeling
• Quiz: Financial Data Prep
• Data cleaning & preprocessing (missing values, anomalies)
• Feature engineering for financial ratios & indicators
• Sentiment data from news & social media
• Hands-on: Preparing a dataset for stock/credit risk modeling
• Quiz: Financial Data Prep
Supervised Learning for Finance
Module 3 • 10 hour to complete
Module 3 • 10 hour to complete
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Regression models for stock returns & pricing
• Classification for credit scoring & fraud detection
• Ensemble methods (Random Forest, XGBoost) in financial risk modeling
• Case study: Loan default prediction with ML
• Quiz: Supervised ML in Finance
• Classification for credit scoring & fraud detection
• Ensemble methods (Random Forest, XGBoost) in financial risk modeling
• Case study: Loan default prediction with ML
• Quiz: Supervised ML in Finance
Time Series Forecasting & Deep Learning
Module 4 • 10 hour to complete
Module 4 • 10 hour to complete
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ARIMA, SARIMA, Prophet for financial forecasting
• LSTMs & RNNs for sequential data in finance
• Attention & Transformers in time series analysis
• Hands-on: Forecasting stock price trends with LSTM
• Quiz: Time Series ML
• LSTMs & RNNs for sequential data in finance
• Attention & Transformers in time series analysis
• Hands-on: Forecasting stock price trends with LSTM
• Quiz: Time Series ML
Unsupervised & Advanced ML in Finance
Module 5 • 10 hour to complete
Module 5 • 10 hour to complete
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Clustering for portfolio diversification & segmentation
• Anomaly detection for fraud detection
• Dimensionality reduction for financial datasets
• Generative models for synthetic financial data
• Case study: Risk anomaly detection
• Quiz: Advanced ML for Finance
• Anomaly detection for fraud detection
• Dimensionality reduction for financial datasets
• Generative models for synthetic financial data
• Case study: Risk anomaly detection
• Quiz: Advanced ML for Finance
Portfolio Optimization & Risk Management
Module 6 • 10 hour to complete
Module 6 • 10 hour to complete
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Mean-variance optimization with ML
• Monte Carlo simulations with AI
• Risk-adjusted performance metrics (Sharpe, Sortino)
• AI for stress testing portfolios
• Hands-on: ML-driven portfolio allocation
• • Quiz: Portfolio & Risk ML
• Monte Carlo simulations with AI
• Risk-adjusted performance metrics (Sharpe, Sortino)
• AI for stress testing portfolios
• Hands-on: ML-driven portfolio allocation
• • Quiz: Portfolio & Risk ML
MLOps & Deployment in Finance
Module 7 • 10 hour to complete
Module 7 • 10 hour to complete
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Model validation & backtesting
• Deployment pipelines for financial ML models
• Real-time inference in trading/finance applications
• Regulatory considerations in model deployment
• Final Project: Build & backtest an ML-powered investment strategy Quiz: Portfolio & Risk ML
• Deployment pipelines for financial ML models
• Real-time inference in trading/finance applications
• Regulatory considerations in model deployment
• Final Project: Build & backtest an ML-powered investment strategy Quiz: Portfolio & Risk ML
Tools & Technologies
Completion Certificate
Job Role
- AI Security Analyst
- Cybersecurity Engineer
- Incident Response Engineer
- Threat Intelligence Specialist
- Security Operations Center (SOC) Analyst / Engineer
- Network Security Engineer
- Penetration Tester (AI & ML assisted)
- Secure DevOps / MLOps Engineer
Training Option
OFFLINE
₹7777
- 24/7 support
- 24/7 support
- 24/7 support
ONLINE
₹5555
- 24/7 support
- 24/7 support
- 24/7 support
HYBRID
₹6666
- 24/7 support
- 24/7 support
- 24/7 support
Why Join this Program
Earn a job
Receive complete job assistance tailored to your career goals. Get expert placement guidance to confidently step into the industry.
Leverage knowledge from industry experts
Learn directly from seasoned Trainers and Gain real-world insights that go beyond textbooks.
Industry-relevant Tools & Practical Learning
Get hands-on experience with the latest tools used by top companies. Hands-on learning through 200+ exercises and 10+ projects with seamless access to integrated labs.
Structured, industry-vetted curriculum
A curriculum shaped by experts to meet evolving industry demands. Structured learning ensures you're career-ready from day one.
Integrated with Gen AI Modules
The curriculum includes cutting-edge Generative AI modules designed to align with emerging tech trends.
Interview preparation & Placement assistance
Sharpen your interview skills with practical training and expert guidance. Receive complete placement support to connect with top recruiters.
FAQ
Basic Python is enough; we cover everything from scratch.
No — this course teaches ML fundamentals too.
Absolutely — this is designed for career transition.
You’ll need to put in extra effort, but many succeed.
Yes — the curriculum includes real-world LLM integration.
Yes — including techniques used by AI professionals.