Machine Learning Institute Certificate in Finance (MLI)
The MLI is the world’s most comprehensive professional machine learning certificate in quantitative finance. The seven-month part-time course comprises 2 levels, 3 Primers, 8 modules, 36 lectures, practical examples, case studies & exercises, module tests, a practical final project and a final examination. You can take the full course or break it up by taking level 1 first and then level 2 (for pricing please check FAQs).
The MLI is the world’s most comprehensive professional machine learning certificate in quantitative finance. The seven-month part-time course comprises 2 levels, 3 Primers, 8 modules, 36 lectures, practical examples, case studies & exercises, module tests, a practical final project and a final examination. You can take the full course or break it up by taking level 1 first and then level 2 (for pricing please check FAQs).
500+ already enrolled
Only For India
Global Course Fee:
₹ 9,32,200 (£8,950)
Exclusive Discounted Wright Offer:
₹ 3,95,000 ₹9,32,200
58% OFF
Time Commitment:
2 Hours Live Lectures Weekly | 36 Weeks
Start Date:12th May, 2025
Evaluation:
Module Tests + Final Exam + Final Project
Contact Us:
enquiries@mlinstitute.org
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Support your colleagues learning journey, in their current and future company roles
Over 500 candidates have now taken the MLI from some of the world’s most prestigious financial institutions including: JP Morgan, BNP Paribas, Mitsubishi UFJ, Bank for International Settlements, Emirates NBD, Deloitte, UniCredit Bank AG, Credit Suisse, Abu Dhabi Investment Authority, CIBC.
The rationale is simple – efficient, scalable models with consistent and testable performance. The aim is to use a scientific approach which can generate extremely fast responses to market events.
While some exchange traded markets (liquid futures, equities, ETFs) and some highly liquid OTC markets (FX, US Treasuries) have been dominated by algorithmic trading for some time, more recent developments include increasing algo presence in less liquid markets such as non-liquid energy futures (energy futures outside of WTI, Brent, and standard energy complex), OTC rates markets (USD and EUR swaps), highly illiquid corporate bonds and even in areas once fully dominated by voice-trading such as EUR government bonds. Some of this evolution is the quest for new markets in which to apply what has been successfully developed elsewhere but much of it is just the natural diffusion of talent into areas which are both more risky and possibly more lucrative. Irrespective of whether it be market-making in banks and speciality prop shops or finding and executing on alphas, balancing portfolios and optimising execution in asset managers, today’s traders, quants and managers must know about systematic trading.
The goal of this class is to provide students with a strong foundation in algorithmic trading as well as the tools and techniques used in the industry. The class will cover everything from basic programming concepts to advanced trading strategies and methods for research into new alpha sources. Students will have the opportunity to apply what they learn in hands-on projects throughout the course.
World Class Machine Learning, AI and Financial Certificate
The MLI is a graduate-level professional certificate, internationally renowned and a solid demonstration of individual commitment to career development.
- Lifelong Learning: All MLI certified students will have access to any updated syllabus, as part of MLI’s lifelong learning commitment.
- Employee Retention: Employee talent attraction & retention can be enhanced and loyalty fostered via the MLI’s cutting edge topics and world class faculty.
- Direct Workplace Knowledge Transference: The highly practical nature of the certificate allows employees to directly use their newly obtained knowledge in your workplace environment. The MLI is a career-enhancing professional certificate.
Qualify from anywhere in the world
- Seven-month part-time global programme delivered twice a year.
- All lectures streamed live over the Internet and recorded. Lectures can be viewed at any time.
- Study while working: career-enhancing certificate that can be taken worldwide.
Practitioner Orientated
The MLI delivers learning of practical value, developed and taught by highly experienced practitioners.
Expert teaching and support
The MLI Faculty is an acclaimed team of instructors combining respected academics and renowned practitioners, all specialists in the field of Machine Learning, Data Science and Artificial Intelligence .The Faculty provides mentoring and support during the course and all members are accessible by email or via the online MLI Forum.
Stay at the cutting edge of quantitative finance throughout your career
At the MLI we make Lifelong learning simple. At any point going forward all Alumni can access the next cohort. You will be able to join the new lectures live with the current crop of students.
Benefit from over 300 hours, exploring the current trends within quantitative finance
Prior to the start of the MLI students are given access to world class quantitative finance online resource, in preparation for the certificate and to enhance your future learning. This resource offers over 300 hours of the latest research and cutting edge techniques.
MLI Online Primer Resource:
- Maths Primer Refresher Material: For each topic (Linear Algebra, Optimization, Probability & Statistics), there will be a specific quiz to test initial background knowledge. We recommend that you first attempt answering the quiz exercises without looking at any material.
- Python Primer for Data Science: Presented by Nikolaos Aletras, Lecturer at The University of Sheffield
- Recorded Primer: Python for Data Science and Artificial Intelligence. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
- Recorded Primer: Advanced Python Techniques. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
Additional MLI Learning Resource:
- Big Data, High-Frequency Data, and Machine Learning with kdb+/q Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas.
- Big Data and High-Frequency Data with kdb+/q Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas.
You will be able to receive up to 400 CPD points for completing this course
The CPD Certification Service was established in 1996 as the independent CPD accreditation institution operating across industry sectors to complement the CPD policies of professional and academic bodies. The CPD Certification Service provides recognised independent CPD accreditation compatible with global CPD principles. www.cpduk.co.uk
The MLI is now comprised of 2 levels 8 modules over 36 lectures
Dedicated Faculty Support is available every step of the way, for the Primers and weekly lectures via the student forum. Students who require extra help can schedule calls directly with the MLI faculty.
End of Module Online Tests (Students get two attempts at each module test). Practical final project and a final exam which can be taken from any global location online using MLI’s live invigilation platform.
Level 1: Machine Learning Institute Certificate in Finance (MLI)
In this module, the concepts related to algorithmically learning from data are introduced. The candidates are given an early taste of a supervised machine learning application before going through the fundamental building blocks starting from linear regression and classification models to kernels and the theory underpinning support vector machines and then to the powerful techniques of ensemble learning.
- Lecture 1: Statistical estimation theory and introduction to machine learning
- Lecture 2: Linear regression
- Lecture 3: Regularized linear regression and ensemble methods
- Lecture 4: Introduction to Bayesian modelling
Mock Module Test:
Module 1 deals with the fundamentals of machine learning. Practical exercises, examples and projects will follow in later modules. Module 1 will include a mock test, to assist students with the end of module online tests.- Lecture 5: Machine learning: Origins and Challenges
- Lecture 6: Neural Networks
- Lecture 8: Deep learning, machine intelligence and consciousness
- Lecture 9: Deep learning volatility
Module 2 Optional Practical Mini Projects:
- Simulate a “Conscious” Computer
The main intent is to see if students can simulate a conscious computer and have it respond to a few basic questions. More complicated functions are welcome.
- Predict Corporate Bond Spread Changes
The purpose of this project is to provide students with intuition regarding linear regression, non-linear regression and neural networks.
- Lecture 10: Introduction and dimensionality reduction
- Lecture 11: Clustering algorithms
- Lecture 12: PCA and autoencoders
- Lecture 13: Alternative data
- Lecture 14:Explainable AI and accelerated computing in portfolio construction
- Lecture 15: Reproducibility and Deployment of Data Science Workflows
- Lecture 16: Feature engineering and model tuning
- Lecture 17: Differential machine learning
Level 2: Machine Learning Institute Certificate in Finance (MLI)
Dates:
- Level 2 Starts: 12th May, 2025
- Lectures Start at 18.00 UK Time
- Lecture 18: Reinforcement Learning: introduction
- Lecture 19: Reinforcement Learning: implementation
- Lecture 20: Reinforcement learning for market making and wealth management
- Lecture 21: Reinforcement learning for optimal order execution
- Lecture 22: Financial time series data
- Lecture 23: Time series analysis
- Lecture 24: Practical lab session
- Lecture 25: Introduction to time series signatures
- Lecture 26: Training LLMs for Quant Finance
- Lecture 27: Generative Modeling with Normalizing Flows: Foundations and Applications
- Lecture 28: Deep learning for text
- Lecture 29: Foundation NLP Models for ESG data extraction
- Lecture 30: Attention, transformers, and BERT
- Lecture 31: Generative modelling, variational autoencoders, and GANs
- Lecture 32: A data-driven market simulator for small data environments
- Lecture 33: Introduction to quantum computing
- Lecture 34: Variational circuits as machine learning methods
- Lecture 35: Quantum models as kernel methods
- Lecture 36: Potential quantum advantages
Examination & Final Project
Candidates will sit a formal examination on a computer. The exam is taken online by students globally.
Examination Preparation Session: 19th November 2024
Examination Date: 3rd December 2024
At the end of the programme, candidates apply the theoretical and practical skills acquired to a real world application within the financial services industry. The assessment will take into account the quality and the originality of the work as well as the clarity of its presentation.
Final Project Hand in Date: 17th January 2025
Program Delivery
The MLI is at the forefront of interactive online learning, which enables students from anywhere in the world to enrol on the programme. We offer a high quality and comprehensive learning portal giving 24-hour access to all the lectures and study materials.
World-Renowned Machine Learning Faculty
The faculty is hand picked to offer you the best learning experience including world class academics and practitioners from institutions such as JP. Morgan, Microsoft, Natwest & Danske Bank.
Paul Bilokon
CEO, Thalesians, Visiting Professor, Imperial College , Head of Faculty
Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.
Ivan Zhdankin
Associate, Quantitative Analyst, JP Morgan Chase & Co , Deputy Head of Faculty
Ivan Zhdankin is a quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, XVA, and electronic trading across asset classes, including commodity futures and G10 and emerging market currencies. Ivan was consulting various banks in quantitative modeling and has recently joined JP Morgan as a quantitative analyst. He has become one of the first researchers to generate convincing results in electronic alpha with neural nets. He has a solid mathematical background from New Economic School and Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.
Prerequisites
Self-paced Primers & Introduction Suite
On registration students are provided access to the MLI Primers & Introduction Suite. This is a self-paced portal on the mathematics for machine learning and python techniques.
Dedicated Faculty Support is available every step of the way, for the Primers and weekly lectures via the online student forums. Students who require extra help can schedule calls directly with the MLI Faculty. Our team will monitor and respond to any queries promptly.
- Calculus
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability
- Probability Distributions
- Continuous Optimization
- Risk and Pricing
Self-paced: Mathematics for Machine Learning
Students who require extra help with the Primers can schedule calls directly with the MLI faculty. Our team will monitor and respond to any queries promptly.
Self-paced: Python for Data Science and Artificial Intelligence
Students who require extra help with the Primers can schedule calls directly with the MLI faculty. MLI team will monitor and respond to any queries promptly.
Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists. The MLI Python Primers are designed to take you from the very foundations to state-of-the-art use of modern Python libraries.
You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn to use distributed task queues (Celery), learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries (Scikit-Learn, Keras, TensorFlow, and Theano), and complete a realistic, real-life data science lab.
Syllabus:
The fundamentals of the Python programming language and Jupyter notebooks
- Jupyter notebooks
- The Python syntax
- Data types, duck typing
- Data structures: lists, sets, and dictionaries
- Data types
Advanced Python features; distributed tasks queues with Celery
- List comprehensions
- Lambdas
- Objects
- The Global Interpreter Lock (GIL)
- Multithreading and multiprocessing
- Distributed task queues with Celery
Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
- Multidimensional arrays in NumPy
- Linear algebra and optimisation with SciPy
- Data visualisation in Matplotlib
- Time series data
- Dealing with Pandas DataFrames
Machine Learning with Scikit-Learn; Deep Learning with Keras, TensorFlow, and Theano
- Overview of machine learning
- Introduction to Scikit-Learn
- Keras and TensorFlow
- Introduction to Theano
Self-paced: Advanced Python Techniques
Students who require extra help with the Primers can schedule calls directly with the MLI faculty. MLI team will monitor and respond to any queries promptly.
Advanced Python Features and Putting them to use in Practice
- Algorithmics and graph theory
- Prime numbers
- Cryptography
- Blockchain
- Distributed Computing with Python
High Performance Python Outline syllabus
- Profiling
- The use of NumPy and SciPy over pure python
- The importance of optimised NumPy and SciPy
- The use of Cython and ctypes to integrate compiled code
- Just In Time Compilation using Numba
- Distributed computing frameworks
- Numerical precision and speed
- Using specialised instead of generalised algorithms
- NAG Library for Python
Python is a superb prototyping language that allows us to develop high quality data analyses and simulations in a relatively short amount of time. The cost we pay for this is performance. Python is essentially an interpreted and single threaded language which puts severe limitations on its speed. In this session we will learn a range of techniques that will allow us to discover which parts of our Python code are slow and what we can do to speed things up.
Testimonials
I liked the length of the MLI program and could see that the syllabus covered many topics from supervised learning and neural networks all the way to quantum computing at the end of the program. The faculty members were very impressive in their fields; people I had heard of and knew from the industry. So, I enrolled.
Diana Enes
Head of Derivatives Valuations Unit, European Investment Bank
Certainly, you can do machine learning and AI education using popular online platforms, but what ultimately keeps you engaged is if you have good instructors, weekly lectures, homework, and, in the end, you will receive a certificate, something you can show to your employer or discuss in job interviews. Finally, I’d make note of the natural language processing lectures towards the end of the course. These days, everybody is talking about Chat GPT and it’s certainly one of the most interesting and exciting fields.
Florian Grünewald
Senior Quantitative Pension Consultant, Siemens
When I signed up for the MLI, I had been working for 11 years in the actuarial space and head a team working on internal models within an insurer in the London. My educational background was fairly quantitative, as I had an undergraduate degree in economics and actuarial… The Python primer was more than enough to get me going. Prior to that, I had used MATLAB and Visual Basic and there was a fair amount of similarity between MATLAB and Python in terms of the way code is vectorized in order to make calculations efficient. I had also done quite a bit of reading in previous years on neural nets and I was familiar with classification techniques, but this gave me a chance to put these ideas into practice.
Muhammad Amjad
Head of Internal Model, Just Group plc
Currently, I work as a portfolio manager at an asset management firm in Latin America. I have developed my career in investment management for 6 years and I believe that machine learning is a new way to improve your set of skills and also it gives you new tools to analyze financial markets. The importance that machine learning is having in the investment decision process and how these tools are changing the finance industry should motivate investment management professionals to learn these new techniques.
In this manner, I totally recommend the Machine Learning Institute Certificate because it gives you the tools to learn in a practical and easy way the several models that can be used in the finance industry. I learned from linear regression and regularization to Neural Networks and the professors are very experienced and proficient in using these tools. Finally, I will be using the knowledge and tools that I learned in my investment and research process as a portfolio manager.
Jair Daviran
Portfolio Manager,
Credicorp Capital
I highly recommend the Machine Learning Institute Certificate in Finance to anyone who wishes to effectively apply the latest tools in predictive modelling and data science. This program covered a good balance between theory and practice. The practicums provided excellent jumping off points for my own use cases. I often started with code provided by the instructors to do what I needed, especially in natural language processing and cross-validation. The material and techniques were current and taught by experienced finance industry practitioners who actually use the techniques they taught. Using the knowledge and techniques I learned in this program, I now possess the ability to explore current research in ML/AI more effectively and productively.
Cameron Wicentowich
Consultant – Advanced Quantitative Solutions
“I was searching for a course regarding the ML/DL application to finance. I have found an excellent choice, the one proposed by MLI. The course provides a global overview of the ML/DL technique applied to financial cases starting from the beginning; up to a medium depth. Participants who want to learn more about specific topics can ask instructors for additional sources and material. A previous exposition to python and ML/DL is advisable, but it is not mandatory.
Giovanni Paolinelli
Power Trader,
Falck Renewables
I liked the length of the MLI program and could see that the syllabus covered many topics from supervised learning and neural networks all the way to quantum computing at the end of the program. The faculty members were very impressive in their fields; people I had heard of and knew from the industry. So, I enrolled.
Diana Enes
Head of Derivatives Valuations Unit, European Investment Bank
Certainly, you can do machine learning and AI education using popular online platforms, but what ultimately keeps you engaged is if you have good instructors, weekly lectures, homework, and, in the end, you will receive a certificate, something you can show to your employer or discuss in job interviews. Finally, I’d make note of the natural language processing lectures towards the end of the course. These days, everybody is talking about Chat GPT and it’s certainly one of the most interesting and exciting fields.
Florian Grünewald
Senior Quantitative Pension Consultant, Siemens
When I signed up for the MLI, I had been working for 11 years in the actuarial space and head a team working on internal models within an insurer in the London. My educational background was fairly quantitative, as I had an undergraduate degree in economics and actuarial… The Python primer was more than enough to get me going. Prior to that, I had used MATLAB and Visual Basic and there was a fair amount of similarity between MATLAB and Python in terms of the way code is vectorized in order to make calculations efficient. I had also done quite a bit of reading in previous years on neural nets and I was familiar with classification techniques, but this gave me a chance to put these ideas into practice.
Muhammad Amjad
Head of Internal Model, Just Group plc
Currently, I work as a portfolio manager at an asset management firm in Latin America. I have developed my career in investment management for 6 years and I believe that machine learning is a new way to improve your set of skills and also it gives you new tools to analyze financial markets. The importance that machine learning is having in the investment decision process and how these tools are changing the finance industry should motivate investment management professionals to learn these new techniques.
In this manner, I totally recommend the Machine Learning Institute Certificate because it gives you the tools to learn in a practical and easy way the several models that can be used in the finance industry. I learned from linear regression and regularization to Neural Networks and the professors are very experienced and proficient in using these tools. Finally, I will be using the knowledge and tools that I learned in my investment and research process as a portfolio manager.
Jair Daviran
Portfolio Manager,
Credicorp Capital
I highly recommend the Machine Learning Institute Certificate in Finance to anyone who wishes to effectively apply the latest tools in predictive modelling and data science. This program covered a good balance between theory and practice. The practicums provided excellent jumping off points for my own use cases. I often started with code provided by the instructors to do what I needed, especially in natural language processing and cross-validation. The material and techniques were current and taught by experienced finance industry practitioners who actually use the techniques they taught. Using the knowledge and techniques I learned in this program, I now possess the ability to explore current research in ML/AI more effectively and productively.
Cameron Wicentowich
Consultant – Advanced Quantitative Solutions
“I was searching for a course regarding the ML/DL application to finance. I have found an excellent choice, the one proposed by MLI. The course provides a global overview of the ML/DL technique applied to financial cases starting from the beginning; up to a medium depth. Participants who want to learn more about specific topics can ask instructors for additional sources and material. A previous exposition to python and ML/DL is advisable, but it is not mandatory.
Giovanni Paolinelli
Power Trader,
Falck Renewables
FAQs
The MLI is a practitioner-oriented professional certificate that will enhance the short-term and long-term career prospects of anyone working in the following fields: Quantitative Finance, IT, Insurance, Model validation, Risk management.
Next start date 12th May, 2025.
The examined part of the course takes place over 7 months, with the examination and project taking place at the end of the course.
This course has been designed to empower individuals who work in or are seeking a career in machine learning quantitative finance.
At any stage during the MLI you may defer your education until the next cohort (one deferral is permitted). The MLI cohorts run twice per annum.
Examination Date:Tuesday 3rd December 2024
The course will take place globally online with weekly lectures on Mondays and/or Tuesdays taking approximately 2 hours. You can attend it at 10.30pm
The live streaming will be available on Cisco Webex, you will be given weekly login access details.
You will have one chance to retake the final examination.
All the lectures are filmed and are available for you on the MLI Student Portal for the duration of the course.
Yes the MLI offers flexible payment options where candidates can pay for the course by instalments.
Option 1: Pay in full
Option 2:
- Full course: Pay 50% on registration and 50% in lecture week 15
- Level 1: Pay 50% on registration and 50% in lecture week 14
- Level 2: Pay 50% on registration and 50% in lecture week 30
Fees are as follows for MLI:
- Full course: ₹ 3,95,000 a 58% discount compared to global fees of ₹ 9,32,200 (£ 8,950).
- Level 1: ₹ 2,00,000 a 60% discount compared to global fees of ₹ 4,95,000 (£ 4,750).
- Level 2: ₹ 2,00,000 a 60% discount compared to global fees of ₹ 4,95,000 (£ 4,750).
Levels 1 & 2 can be taken individually and can be billed separately, however Level 1 must be taken first.
The MLI offers two flexible study options so you can decide how to complete the course:
- Full Course: Complete the 8 modules in 7 months.
- Levels 1 & 2: Complete the 8 modules in 2 x 4 module stages. Please note that candidates must pass Level 1 and then Level 2 to become MLI certified.
It is possible for students to defer completion of the MLI to the next cohort at no extra charge (one deferral is permitted).
The current pricing of ₹ 3,95,000 is at a 58% discount compared to global fees of ₹ 9,32,200 (£ 8,950). As this is a negotiated & heavily discounted price specifically for the Wright community, there are no further discounts or early bird offers. However, we do offer volume discounts, so if 2 or more people from your institution wish to take the course please contact us and we will be happy to discuss the pricing.
In addition to this, if you are interested in taking multiple courses from the following list, then we can offer you specific discounts:
