Certificate Program in Machine Learning for Finance (CPMLF)
Build your career in Quant Finance with Machine Learning
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FinoQ Executive Program
Indian Institute of Quantitative Finance
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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
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Covering the essentials to enable anyone/everyone to take this course
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Personalized live lectures from ML academicians & SME industry practitioners
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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
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Why choose CPMLF Program?
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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
Faculty
Dr. Amit Ram
"Ph.D. (Statistical Physics and Computational Methods) Stanford University (USA) B. Tech. (Engineering Physics) IIT (Bombay) Over 10 years of experience working in Lehman Brothers (New York), JP Morgan Chase (New York), Standard Chartered (Singapore), Nomura (Mumbai)"
More...Dr. Samir Ranjan
"Ph.D. (Theoretical Physics) Purdue University (USA) MS (Mathematical Finance) Columbia University (New York) Over 10 years of experience working as Financial Engineer with Bonddesk Group in New Jersey (USA)"
More...Ritesh Chandra
"MBA IIM Calcutta B. Tech. IIT Kanpur and CFA (Level 3 Pass) More than 11 years of experience in Credit Risk, Corporate Finance & Technology and has worked in India, China and Canada in a variety of roles"
MoreFinancial Engineering
Modern Investment Finance is hugely dependent on the implementations of the theories and techniques of financial engineering. Financial Engineering, or Quantitative Finance as it is alternately known, is a multidisciplinary field involving the application of theories from financial economics, physics, mathematics, probability, statistics, operations research and econometrics using the methods and tools of engineering and the practice of computer programming to solve the problems of Investment Finance.
Generally the language of choice for Quant implementations traditionally has been C++ along with tools like Matlab, Mathematica, Stata, etc. However, of late the Python language has become more popular.
Financial Engineering has emerged as a very prospective career prospect for people with a strong mathematical background like those coming from engineering, mathematics, statistics, physics or econometrics background. The best of the global financial institutions like Investment Banks, Hedge Funds, etc. hire people having strong quantitative skills for “Quant” jobs. This is also a very rewarding and exciting career option for such people as there is ample scope for applying their numerical and creative skills to design new things, be it devising new investment strategies or be it structuring new financial instruments or be it finding methods to value them. They are continuously competing with their peers and some of the best minds in the market and have to out-perform them to generate superior returns, which is intellectually a very challenging work, and this makes it all the more thrilling.
Brief Financial Engineering Courses Outline
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Preparatory PrimersCeritificate Program in Machine Learning and Data Science for Quant Finance
- ML Programming Preliminaries
- ML Mathematical Preliminaries
- ML Probability & Statistical Preliminaries
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Module ICeritificate Program in Machine Learning and Data Science for Quant Finance
- Machine Learning Overview, Framework & Finance Applications
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Module IICeritificate Program in Machine Learning and Data Science for Quant Finance
- Machine Learning Supervised Learning Algorithms
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Module IIICeritificate Program in Machine Learning and Data Science for Quant Finance
- Machine Learning Unsupervised Learning Algorithms
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Module IVCeritificate Program in Machine Learning and Data Science for Quant Finance
- Machine Learning Speech and Language Processing
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Module VCeritificate Program in Machine Learning and Data Science for Quant Finance
- Machine Learning Model Evaluation Metrics and Model Validation
Financial Engineering Career Opportunities
Modern Investment Management has become very much mathematical and statistical in nature, it is now much more of science than arts, specially where investments in complex financial instruments and complex trading/investment strategies are concerned. That is the reason that high-end investment firms that invest in derivatives are opting for people who have strong quantitative skills for structuring or valuation of complex financial instruments and for devicing superior investment strategies.
This has opened up very exciting and rewarding career opportunities in the field of Quantitative Investment Management for candidates who come with academic background in engineering, mathematics, and other numerical specializations. Needless to say, that apart from the stimulating intectual challenges that careers in this field offer to the mathematically talented individuals, the compensations are quite handsome indeed.
The course prepares candidates for careers as quantitative investment managers or quantitative analysts with financial institutions like investment banks, hedge funds, private equity firms, large broking houses, investment research and analytics firms, etc.
Candidates having a strong numerical background have a very bright chance of making a very rewarding career in this field with the largest of investment banks and other financial institutions. Salaries of quantitative analysts vary depending on their experience and background. In India presently, salaries for this profile may range from 8 Lacs p.a. for freshers to 30 Lacs p.a. for candidates with a few years of experience.
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
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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 ExpertWhat 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 PreliminariesPersonalized live lectures from ML academicians & SME industry practitioners- ML Methodology
- ML Finance Use Case ImplementationFull 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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