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
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
Module 1 - Supervised Machine Learning Algorithms
Topics Covered:
- Linear & Logistic Regression
- Decision Trees & Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Model evaluation metrics
- Hyperparameter tuning
- Building end-to-end ML pipelines
Module 2 - Unsupervised Learning & Pattern Recognition
Topics Covered:
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction (PCA)
- Anomaly detection
- Association rules
- Customer segmentation (use case)
- Evaluating unsupervised models
- Visualization for cluster insights
Module 3 - Deep Learning & Neural Networks (DL Basics)
Topics Covered:
- Understanding neural network architecture
- Forward & backward propagation
- Activation functions
- Loss functions & optimizers
- Creating neural networks with TensorFlow/Keras
- Underfitting vs overfitting
- Model regularization
- DL experimentation workflows
Module 4 - Computer Vision (CV) Fundamentals
Topics Covered:
- Image processing basics
- Convolutional Neural Networks (CNNs)
- Pooling, padding, filters
- Image classification
- Data augmentation
- Transfer learning (VGG, ResNet)
- Building CV projects using Keras
- Model deployment
Module 5 - Natural Language Processing (NLP)
Topics Covered:
- Text preprocessing: tokenization, stemming, lemmatization
- Bag of Words & TF-IDF
- Sentiment analysis
- Intro to word embeddings
- Text classification use cases
- Building NLP models with Scikit-learn
- Deploying NLP models
- Real-world NLP workflows
Module 1 - Product Thinking for AI & ML Solutions
Topics Covered:
- Problem identification using ML
- Understanding business metrics
- Mapping data to product features
- Building user-centric AI solutions
- Figma for AI product UI
- Model lifecycle in products
- Documentation & ML requirements
Module 2 - Startup Innovation Using AI & ML
Topics Covered:
- AI product idea generation
- Competitive analysis
- Building MVPs for ML products
- AI monetization strategies
- Creating pitch decks
- Using AI tools for product research
- Preparing go-to-market (GTM) plans
Module 3 - Professional Development for ML Careers
Topics Covered:
- Resume building for AI/ML profiles
- LinkedIn optimization
- Writing ML case studies
- GitHub portfolio for ML projects
- Communication skills for data storytelling
- Interview preparation (ML + coding)
Mini Projects
- Sales prediction using Regression
- Iris dataset classifier
- Customer segmentation using K-means
- Sentiment analysis on social media data
Concepts Covered:
- EDA
- ML basics
- Clustering
- Classification
Intermediate Projects
- Image classification with CNN
- Spam detection (NLP)
- Credit card fraud detection
- Housing price prediction
Concepts Covered:
- Deep learning
- NLP
- Evaluation metrics
- Optimization
Capstone Projects
- End-to-End ML Pipeline & Deployment
- AI Customer Insights Platform
- NLP-powered Recommendation System
- Deep Learning Visual Recognition System
Concepts Covered:
- Architecture design
- Deployment
- Experimentation
- Documentation
Tools & Softwares
Program Outcomes
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