Become an Industry-Ready Machine Learning for Financial Analysis
To integrate machine learning and financial analytics, enabling learners to build predictive models, automate data-driven insights, and apply AI to investment and risk management decisions.
- Academic partner with UGC, AICTE, and NCVET alignment
- 200+ programs across domains
- 1,000+ industry partners and global collaborations
Program Objective
Key Features
Program Outcomes
Curriculum Structure
Module 1 - Financial Data Analytics & Preprocessing
Topics Covered:
Introduction to Financial Markets & Data Sources
Types of Financial Data (Stock, Forex, Crypto, Derivatives)
Data Collection using APIs
Data Cleaning & Handling Missing Values
Time-Series Data Preparation
Feature Engineering for Financial Data
Data Visualization & Exploratory Data Analysis (EDA)
Module 2 - Statistical & Machine Learning Foundations
Topics Covered:
Probability & Statistical Concepts for Finance
Hypothesis Testing
Correlation & Regression Analysis
Supervised vs Unsupervised Learning
Model Evaluation Metrics
Overfitting & Underfitting
Cross-Validation Techniques
Module 3 - ML Models for Forecasting & Prediction
Topics Covered:
Linear & Logistic Regression
Decision Trees & Random Forest
Support Vector Machines (SVM)
Time-Series Models (ARIMA Basics)
LSTM for Financial Forecasting
Model Tuning & Performance Optimization
Backtesting Strategies
Module 4 - AI Applications in Trading & Investment
Topics Covered:
Algorithmic Trading Concepts
Trading Signal Generation
Sentiment Analysis for Market Prediction
Reinforcement Learning Basics in Trading
AI-driven Investment Strategies
Automated Trading System Overview
Performance Tracking & Evaluation
Module 5 - Risk Analytics & Portfolio Optimization
Topics Covered:
Risk Measurement (Volatility, VaR, CVaR)
Portfolio Theory (Modern Portfolio Theory Basics)
Asset Allocation Strategies
Diversification Techniques
Risk-Return Optimization
Monte Carlo Simulation Basics
Stress Testing & Scenario Analysis
Module 6 - Capstone Project: Predictive Finance Model
Topics Covered:
Problem Statement & Project Planning
Financial Data Collection & Preprocessing
Feature Engineering & Model Development
Model Evaluation & Backtesting
Risk Analysis & Optimization
Deployment & Final Presentation
Module 7 - Employability & Professional Skills
Topics Covered:
Resume Building for Finance & AI Roles
GitHub/Project Portfolio Creation
Interview Preparation (Technical & Case-Based)
Communication & Presentation Skills
Industry Use Cases & Case Studies
Tools & Softwares
Salary Scale
Career Roles
- Financial Data Analyst
- Quantitative Analyst (Quant)
- Machine Learning Engineer (Finance)
- Risk Analyst
- Algorithmic Trading Specialist
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
Prediction models, CNN-based classifiers, NLP applications, and a full ML pipeline.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
CSE, ISE, AIML, ECE, EEE, Civil, Mechanical and all final-year VTU students.
Python, Scikit-learn, TensorFlow, Pandas, Kaggle workflows, cloud ML basics.
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000
Send us a Message
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
080 6945 1000