Certificate Program in Machine Learning for Finance (CPMLF)

Build your career in Quant Finance with Machine Learning

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

  • Exhaustive Primers & Preliminaries - Basic Familiarity (Scratch-up coverage in primers & preliminary modules)
  • Extensive 200 hours of personalized interactive lectures
  • Expertized teaching faculty from BFSI quants & risk practitioners & academic researchers
  • Self learning evaluation modules & milestones
  • 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 this course

  • Personalized live lectures from ML academicians & SME industry practitioners

  • Full blown pragmatic learning exposure

About The CPMLF Course

Prerequisites (Good-To-Have)

  • 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

Imparted Skill sets

  • 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

Industry scope

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

CPMLF Course Calendar

  • 5 Compulsory Modules
  • 4 Primers
BatchStart DateFeeModeTime

CPMLF Course Learning Journey Tracks

Learning Journey Track 1 - ML programming

Preparatory Primers

ML programming Preliminaries (Accessibility upon enrollment)

Mandatory Topics

  • NumPy -> General Purpose Array Processing
  • Pandas -> Data Analysis & Manipulation
  • Matplotlib & Seaborn -> Data Visualization
  • Scikit Learn -> Predictive Data Analysis and Data Modeling
  • Keras -> Neural Networks
  • TensorFlow -> Reinforcement Learning

Electives Topics

  • ML Algorithm Computational Efficiency Scalability & Optimizations:
  • Data Structures -> Binary Trees, Hashing, Heap, Stack etc
  • Dynamic Programming
  • Randomized & Sublinear Algorithm,
  • Graphs, Gradient/Stochastic Descents
  • Primal-Dual methods

Practitioner Hands-On

  • Coding problem solving on mandatory topics
  • Live conceptual deep-dive & clarification sessions on all topics

Learning Journey Track 2 - ML Mathematics

Preparatory Primers

ML Mathematical Preliminaries (Accessibility upon enrollment)

Mandatory Topics

  • Linear Algebra -> Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Matrix Operations, Matrix Eigen Decomposition, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms
  • Multivariate Calculus -> Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

Electives Topics

  • Linear Algebra -> LU Decomposition, QR Decomposition or Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization
  • Real and Complex Analysis -> Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits, Cauchy Kernel, Fourier Transforms.
  • Information Theory -> Entropy, Information Gain Function Spaces and Manifolds

Practitioner Hands-On

  • Numerical problem solving on mandatory topics
  • Live conceptual deep-dive & clarification sessions on all topics

Learning Journey Track 3 - ML Probability & Statistics

Preparatory Primers

ML Programming Preliminaries (Accessibility upon enrollment)

Mandatory Topics

Python Labs

  • Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance & Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian)

Electives Topics

  • Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

Practitioner Hands-On

  • Numerical problem solving on mandatory topics
  • Live conceptual clarification sessions on all topics

Learning Journey Track 4 - ML Methodology

Preparatory Primers

ML Techniques & Methods

Mandatory Topics

  • - Supervised Learning
  • - Deep Learning
  • - Unsupervised Learning

Electives Topics

  • - Speech and Language Processing
  • - Reinforcement Learning

Practitioner Hands-On

  • 8 hours weekly live conceptual & algorithmic implementation sessions to exhaustively cover all ML techniques & methods along with the financial use case implementation in Python

CPMLF Course Placement

Students successfully completing best Financial Engineering online courses will get placement assistance subject to fulfillment of applicable conditions. " to "Students successfully completing this Machine Learning for Finance course will get placement assistance subject to fulfillment of applicable conditions.

Admission Process in Financial Enginneering 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.

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
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