Book a guidance call for IIQF Programs
Email Contact Us Whatsapp

Certificate Program in Machine Learning for Finance (CPMLF)

Build your career as Machine Learning Specialist in Banking and Financial Services

Live Online Instructor-led Weekend Program
  • English

Quick Facts

  • Program Duration
  • Program Schedule
  • Program Timing
  • Program Start Date

CPMLF Program - Machine Learning for Finance Course Highlights

  • Extensive 200 hours of personalized live online instructor-led interactive lectures
  • Faculty - Industry practitioners & academic researchers from BFSI machine learning, quants & risk domain
  • Exhaustive primers & preliminaries - Basic familiarity (Scratch-up coverage in primers & preliminary modules)
  • Online learning modules with curated content & case studies
  • Focused implementation labs for end-to-end ML algorithm data, design development, deployment & debugging
  • Capstone project & research article work for final evaluation
  • BFSI ML domain filtered job search & interview prep-up

ML for Finance Course Framework

  • Covering the essentials to enable anyone/everyone to take ML for course

  • Personalized live lectures by MNC industry practitioners and ML academicians/researchers

  • Real world industry application exposure

About The Certificate Program in ML for Finance

Primers & Preparatory Modules

  • Linear Algebra - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Statistics - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Probability - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Differential & Integral Calculus - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Programming & Coding - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Exploratory Data Analysis - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Financial Datasets - Basic-to-Medium Familiarity

Key Learning Outcomes

  • Advanced Programming in analytically powerful languages like Python
  • Data Science for data manipulations, transformations & visualizations
  • Machine Learning (ML) Techniques – supervised, semi-supervised & unsupervised techniques
  • Machine Learning (ML) Applications – Finance & Risk Management
  • Machine Learning (ML) Applications – Designing Automated Decision Systems
  • Machine Learning (ML) Applications – Algorithmic Trading & Investment Strategies

Industry Scope

  • BFSI Data Scientist
  • BFSI Data Engineer
  • BFSI Machine Learning Developer
  • BFSI Machine Learning Algo Designer
  • BFSI Machine Learning Product Owner
  • BFSI Machine Learning Researcher
  • BFSI Computational Quant Researcher
  • BFSI Quantum Computing Expert
  • BFSI Data Science & Machine Learning Program/Project Manager

CPMLF Course Calendar

  • 5 Compulsory Modules
  • 3 Primers
Download Brochure Register Now
Batch Start Date Fee Mode Time
8 Months course designed to teach Machine Learning for Applications in Finance
  • Learning Journey Step 1
    10 Weeks
  • Learning Journey Step 2
    10 Weeks
  • Learning Journey Step 3
    10 Weeks
  • Application Journey Step
    4 Weeks
Module 0 - Foundation Stage

Machine Learning Preliminaries  

  • Preparatory ML Overview Primer
  • Exhaustive ML Programming Primer
  • Exhaustive Mathematical Primer
  • Milestone Timeline - 6 Weeks
  • Starting Point - CPMLF course starting time
  • Support - Comprehensive Primers, Weekly Doubt Clearing Sessions, Toy Topical Sessions, Python Labs
    • Starting Timeline
    • Module0
    • 4 Weeks
    • CPMLF
    • Course 
    • Inception
    • End Timeline
    • Module0
    • 2 weeks
Module 1 - Problem Setting & Solutionizing  Stage

Machine Learning for Financial Applications  

  • ML Paradigms & Structural Framework
    • Supervised Learning 
    • Semi-Supervised  Learning
    • Unsupervised Learning 
    • Deep Learning
    • Online/Off-Line Learning
    • Reinforcement Learning 
  • Financial Problem Settings & Use Cases 
  • ML Solution Design & Architecture  
  • ML Model Development Lifecycle
  • Milestone Timeline – 4 Weeks
  • Starting Point – Post Module 0
  • Support – ML Problem Framing, ML Problem Solving, Financial Use Case Library, ML BFSI Applications, ML BFSI System Design
    • Starting Timeline
    • Module0
    • 4 Weeks
    • CPMLF
    • Course 
    • Inception
    • End Timeline
    • Module0
    • 2 weeks
    • End Timeline
    • Module1
    • 4 weeks
  • Learning Journey Step 1
    10 Weeks
  • Learning Journey Step 2
    10 Weeks
  • Learning Journey Step 3
    10 Weeks
  • Application Journey Step
    4 Weeks
Module 2 - ML Supervised Technique Know-How  Stage

ML Supervised Learning Algorithms 

  • ML Driven Regression
  • ML Driven Time Series
  • ML Driven Classification
  • ML Driven Data Visualization
  • ML Driven Data Science Insights & Story Telling
  • Milestone Timeline – 6 Weeks
  • Starting Point – Post Module 1
  • Support – Weekly 8 hours of Live Lectures, Python Labs – Coding Practice,  Supervised Learning Financial Use Case Studies
    • CPMLF
    • Course
    • Inception
    • CPMLF
    • Learning
    • Journey 1
    • Learning
    • Module2
    • 6 weeks
Module 3 - ML Un-Supervised Technique Know-How Stage

ML Un-Supervised Learning Algorithms

  • ML Driven Clustering
  • ML Driven Association 
  • ML Driven Data Visualization
  • ML Driven Data Science Insights & Story Telling
  • Milestone Timeline – 4 Weeks
  • Starting Point – Post Module II
  • Support – Weekly 8 hours of Live Lectures, Python Labs – Coding Practice,  Un-Supervised Learning Financial Use Case Studies
    • CPMLF
    • Course
    • Inception
    • CPMLF
    • Learning
    • Journey 1
    • Learning
    • Module2
    • 6 weeks
    • Learning
    • Module3
    • 4 weeks
  • Learning Journey Step 1
    10 Weeks
  • Learning Journey Step 2
    10 Weeks
  • Learning Journey Step 3
    10 Weeks
  • Application Journey Step
    4 Weeks
Module 4 - ML Speech & Language Processing Technique Know-How  Stage

Machine Learning Speech & Language Processing

  • Unstructured Data Sets & Transformations 
  • ML Driven Textual & Speech Processing
  • ML Driven Document Classification
  • ML Driven Image Classification 
  • ML Driven Chat-bots
  • Milestone Timeline – 6 Weeks
  • Starting Point – Post Module III
  • Support – Weekly 8 hours of Live Lectures, Python Labs – Coding Practice,  Language & Image Processing Financial Use Case Studies
    • CPMLF
    • Course
    • Inception
    • Learning
    • Journey 1
    • 10 Weeks
    • Learning
    • Journey 2
    • 10 Weeks
    • Learning
    • Module4
    • 6 weeks
Module 5 - ML Evaluation Know-How  Stage

Machine Learning Evaluation & Explainability

  • ML Performance Evaluation Metrics 
  • ML Quantitative Validation Tests
  • ML Driven Model Explainability 
  • ML Driven Tuning & Optimization 
  • ML Results Showcase & Storytelling
  • Milestone Timeline – 4 Weeks
  • Starting Point – Post Module IV
  • Support – Weekly 8 hours of Live Lectures, Python Labs – Coding Practice,  ML Model Regulatory Prerequisites, Transparency & Explainibility for Financial Use Cases
    • Learning
    • Journey 1
    • 10 Weeks
    • Learning
    • Journey 2
    • 10 Weeks
    • Learning
    • Module4
    • 6 weeks
    • Learning
    • Module5
    • 4 weeks
  • Learning Journey Step 1
    10 Weeks
  • Learning Journey Step 2
    10 Weeks
  • Learning Journey Step 3
    10 Weeks
  • Application Journey Step
    4 Weeks
Module 6 - ML Implementation  Stage

Machine Learning Prototyping

  • ML Front-To-Back Tech Stack
  • ML Design & Architecture
  • ML Prototyping
  • ML Deployment
  • ML Productionization
  • Milestone Timeline – 2 Weeks
  • Starting Point – Post Module V
  • Support – Weekly 8 hours of Live Lectures, Python Labs – Front-To-Back ML Model Development & Deployment
    • Learning
    • Journey 1
    • 10 Weeks
    • Learning
    • Journey 2
    • 10 Weeks
    • Learning
    • Journey3
    • 4 weeks
    • Learning
    • Module6
    • 2 weeks
Module 7 - ML Capstone Project Stage

Machine Learning Capstone Project

  • Quantitative Finance 
  • Derivative Pricing 
  • Risk Modelling & Management 
  • Portfolio Management
  • Algorithmic Trading 
  • Financial Time Series Forecasting
  • Milestone Timeline – 2 Weeks
  • Starting Point – Post Module VI
  • Support – Mentoring Support, Thesis Supervisor Support, Feedback Supports
    • Learning
    • Journey 1
    • 10 Weeks
    • Learning
    • Journey 2
    • 10 Weeks
    • Learning
    • Journey3
    • 4 weeks
    • Learning
    • Journey4
    • 6 weeks

Faculty

CPMLF Course Placement

Students completing Certificate Program in Machine Learning Finance online courses will get placement assistance subject to the fulfilment of applicable conditions.

Register Now

Admission Process in CMPLF Program

  • Send Your Application

  • Get on a call with a counsellor

  • Wait for Application Acceptance

  • Pay the fee & join the upcoming batch

Finance your Study

Educational Loans

We are very happy to help you progress to greater heights in your career in every way possible. Education loans available at 0% interest for full time Indian residents. Easy EMI plans available.

Student Aid

Encourages the full time students to enter this domain, benefits, if you are still pursuing formal education.

Need a Financial Aid ?
Book a guidance call Enquire Now Download Brochure Course Calendar

Get Answers

  • To whom this special program in Machine Learning (ML) is applicable ?

    This exhaustive ML program broadly caters to anyone & everyone looking for career avenues in emerging field of Machine Learning & Data Science irrespective of industry/sector yet we’ve designed the application part to specifically cover implementation use cases in below sub-fields:
    - Accounting & Finance
    - Risk Management
    - Portfolio Analytics
    - Trading & Investment Analysis
    - Regulatory & Internal Compliance
    - Computational Finance & Financial Engineering

  • What potential career avenues are available in the industry for the aspirants of this ML program?

    This ML program caters to building practitioner-level skills to broadly cater to any & all ML & Data Science driven role families with a few real examples from the job market given below :
    - Data Scientist for Financial/Accounting Decision Systems
    - Machine Learning Model Designer for Financial Forecasting & Predictive Models
    - Forensic Audit Data Scientist
    - Fraud Analytics Machine Learning Expert
    - Risk/Finance/Compliance Machine Learning Data Engineer
    - Machine Learning Modeler for Quantitative Financial Models
    - Machine Learning Driven Algorithmic Trader
    - Machine Learning Model Validation Expert

  • What skills one should possess to pursue Machine Learning & Data Science as a futuristic career path?

    - Machine Learning Technical Know-How - E.g. Supervised/Unsupervised/Reinforcement/Deep-Learning Techniques & Methods
    - Programming & Coding Skills - E.g. High Level & Powerful Languages Python, Julia, specific libraries/modules in SAS/R etc.
    - Mathematical Skills - E.g. Linear Algebra, Matrix & Vector Operations, Multivariate Calculus, Optimization Problems
    - Probability Theory & Statistical Skills - E.g. Probability Rules, Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)

  • What is the ML for Finance course structure & design?

    ML for Finance Course Design Framework

    Covering the essentials to enable anyone/everyone to take this course
    - ML Programming Preliminaries
    - ML Mathematical Preliminaries
    - ML Probability & Statistical Preliminaries
    Personalized live lectures from ML academicians & SME industry practitioners
    - ML Methodology
    - ML Finance Use Case Implementation
    Full blown pragmatic learning exposure
    - ML Python Labs with full time technical assistance
    - ML Finance Use Case Implementation
    - Capstone Project
    - Hackathon Style Contests

  • What kind of machine learning application use cases covered by CPMLF?

    - Financial Time Series (Price, Volatility, VaR, ES etc.) Forecasting using Deep Learning Neural Networks (NN) algorithms
    - Credit and counterparty credit risk modelling (PD, LGD, EAD, XVA, Margining etc.) using Ensemble Learning ML techniques
    - High Dimensionality Reduction using Principal Component Analysis (PCA)/Autoencoder
    - Derivative Pricing using Deep Learning Neural Networks (NN) algorithms
    - Investment Portfolio Construction & Optimization using Reinforcement Learning
    - Sentiment analysis on unstructured financial data using NLP algorithms
    - Many others….

  • How machine learning (ML) models disrupt the world of quant finance?

    - ML algorithms provides data-driven analytics versus assumption or theory based classical statistical models
    - ML algorithms are more proficient to handle non linearities, multicollinearities, curvature risk and tail risk
    - ML algorithms are more faster & efficient than statistical models
    - ML algorithms allows for more dynamic estimations, parameterizations and calibrations
    - ML algorithms can be made more consistent with front-office models leading to front-to-back integration
    - ML algorithms facilitates real time portfolio risk management, asset pricing and valuation
    - Many more…..

  • What machine learning allied competencies are built by CPMLF course?

    - Big Data & Processing Tech Stack
    - Data Engineering, Exploration and Exploitation
    - Data Synthetization & Simulations
    - Data Insights, Visualization & Story Telling
    - ML Devops - Design, Development, Debugging and Deployment
    - ML model fine-tuning and optimization
    - ML Research Expeditions
    - Many more…..

  • How CPMLF course is different from other such AIML course offerings?

    - Upstarter primers & preliminaries for scratch-up skill building - ML Stats, Probability, Math & Programming
    - Faculty includes - BFSI industry top senior ML practitioners & academia researchers
    - ML pragmatic hands-on learning approach – focused problem solving, BFSI case studies, ML financial use case library etc.
    - Exhaustive course coverage - ML Supervised, Semi-Supervised & Unsupervised Techniques
    - 250+ hours of live classroom lectures
    - Python prototyping labs & implementation workshops
    - ML front-to-back tech stack & deployment
    - Financial, Risk & Quants specific applications
    - ML capstone projects, research topics & white paper writing
    - Much more in-store….

  • What additional support is expected to be rendered by IIQF for CPMLF students?

    - Capstone & research project mentoring
    - BFSI industry ML core competencies awareness
    - Placement opportunities for machine learning, data science, quantitative modelling & analytics role families
    - Career guidance & coaching – skill evaluation, CV building, opportunity prospecting etc.
    - Mock-up interview preparations & grooming
    - Much more in-store…..

  • What my CPMLF learning journey will look like if I don’t posses any of the basis skills & awareness on Machine Learning careers in finance?

    - You will get lot of preparatory primers & preliminaries to get you the basic essential skills for machine learning
    - You will get personal mentoring to get you doubts clarified & get you additional resources to build in basics right
    - You will get the holistic know-how of programming & regular python labs practice sessions on coding problems
    - You will get tailor made use cases & dataset aligned to your area of interest for prototyping
    - You will get career guidance & counselling sessions to plan your aspirational career goal seeking
    - You will get the right suitable job opportunities & targeted advise on how to shape up yourself as a prospective candidate
    - Much more in-store….

Book a guidance call Enquire Now Download Brochure Course Calendar
X
Need Help?

NEED HELP?

You can call us on +91-8976993621 or email us your contact details if you would like a call back

(This service is normally available between 9.00 AM and 9.00 PM all the day. At all other times, please submit an email request.)