AI

Best AI & ML Courses for Banking & Finance (2020)

In FinTech by Gaurav SharmaUpdated On:

AI

AI represents the next epoch in terms of how work is done. Millions of applications already use AI with varying degrees of complexity to perform mundane or repetitive tasks. Banks, FinTech startups and even government regulators use AI for applications as diverse as fraud detection, investment advisory, real-time transaction monitoring, optimizing lending decisions and so on.

Why you need to learn AI?

As a banker with a background in computer engineering, AI was something that always excited me. Now as a FinTech product consultant, I continue to see new and remarkable things being done by smart developers when it comes to AI.

However, the truth is that there is a massive shortage of talent when it comes to AI product development, coding and implementation. Banks with huge budgets are shopping around for smaller companies or developers with a proven track record. And in many cases – the packages that they are offering are bigger than what Investment Banking analysts can expect. Exciting times!

This is my list of some of the best courses when it comes to AI for the financial services industry. I will focus mostly on course quality, but also on branding as that will have an impact on how your CV looks to potential recruiters. Here are the top courses, followed by detailed reviews below:

Best Artificial Intelligence & Machine Learning CoursesLearn More
1. Big Data, AI & ML in Financial Services from Alqami & Delta CapitaPreview/ Enroll Now
2. Artificial Intelligence for Trading from UdacityPreview/ Enroll Now
3. Machine Learning from Stanford OnlinePreview/ Enroll Now
4. Professional Certificate in Computer Science for Artificial Intelligence from HarvardPreview/ Enroll Now
5. Deep Learning SpecializationPreview/ Enroll Now
6. MicroMasters® Program in Artificial Intelligence from ColumbiaPreview/ Enroll Now

1. Big Data, AI & ML in Financial Services from Alqami & Delta Capita

Who should take this?

Best suited for corporate and investment bankers, middle management, business leaders, business owners.

Why take this course?

  1. If you need an AI and machine Learning course specifically for the banking and financial services industry, then this is as good as it gets. This course has been created by Alqami and Delta Capita using their vast experience of dealing with real world AI and ML implementations. This is not just a theoretical course but rather provides real world insights into what AI, ML and big data means for the finance world.
  2. The curriculum covers everything at the strategic level – from data governance to framework, tools, infrastructure, privacy issues and even quantum computing! There is a big focus on ethics, trusts and privacy which are topics that should definitely be on the mind of any business leader.
  3. This is a strategic level course that has been designed for business leaders, managers, bankers and others who are not on the development side. It is meant to provide you with the understanding to have a conversation about these topics and to lead your team or organization down a path to using these technologies to maximize your business efficiency.
  4. There are plenty of quizzes, white papers and case studies and I really enjoyed going through them. They really take you out of that academia zone and into real world territory and you feel like you are dealing with real world challenges and real world solutions to those problems. That is perhaps the biggest strength of this course.

Summary

  • Time to Complete: Should take about 10-12 hours for most people.
  • Available fully online and on-demand. Complete at your own pace.
  • The best top-down view on this subject. Ideal for managers and investment/ corporate/ retail bankers who are on the business side of things.
  • Created by industry practitioners using real world insights.

Preview Course/ Enroll Now


2. Artificial Intelligence for Trading from Udacity

Who should take this?

Recommended for traders, quants, wealth managers, portfolio/ assets managers, developers.

Why take this course?

  1. A course dedicated to using AI for quantitative trading applications. There is a focus on portfolio optimization, natural language processing for sentiment analysis, using trading signals for your algorithms, back-testing and so on. In short, this course is laser focused on quantitative trading and it does that rather well.
  2. There are a lot of mini projects that you must complete along the way which reinforces the learning rather well. Think of it as a Nanodegree program (which is what its officially called). It’s not just a course, its a more comprehensive and focused experience that requires greater commitment from the learner. However, the extra effort is proportionally rewarded with a superior understanding of the concepts which I would equate to completing a full time degree on the subject.
  3. For example, your first project is to develop a momentum trading strategy after learning quantitative trading and generating signals from stock data. Then you move on to portfolio optimization, financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs. Crucial trading concepts like factor investing, alpha research, sentiment analysis, and trade simulation is also covered in great detail. There is also a massive section on using natural language processing to generate trading signals! This is just top notch stuff if you are even mildly interested in trading.
  4. Udacity also provides a lot of value-added services to students and this is not just a hands-off undertaking. You do real world projects and receive feedback. You have mentor support for technical issues and also get access to a career coach. This is not just a bunch of online videos being marketed as a course.

Summary

  • Time to Complete: About 6 months with 10 hours per week.
  • Available fully online and on-demand. Complete at your own pace.
  • Well suited for quantitative traders/ developers. Udacity provides additional technical and career mentor support.

 Preview Course/ Enroll Now


3. Machine Learning from Stanford Online

Who should take this?

Recommended for machine learning enthusiasts and developers. This is a quant heavy course.

Why take this course?

  1. If there was such a thing as a “famous course”,  then this one would probably qualify. Its one of the most popular courses in the computer science world and a quintessential entry for the topic of machine learning.
  2. It provides a broad introduction to machine learning including applications like data mining and statistical pattern recognition. Because of that focus on statistics, it does require basic math skills. The author is a well known name in the field and Stanford is as good as it gets when it comes to brand value.
  3. The course covers areas such as supervised learning, best practices in machine learning and AI along with a number of case studies to apply those concepts. You will learn about parametric algorithms, support vector machines, kernels, neural networks, dimensionality reduction, bias theory, innovation process in machine learning etc.

Summary

  • Time to Complete: Should take around 60 hours for most.
  • Available fully online via Coursera’s eLearning platform.
  • Excellent course focused on machine learning and statistical pattern recognition for various applications.

Preview Course/ Enroll Now


4. Professional Certificate in Computer Science for Artificial Intelligence from Harvard

Why take this course?

  1. This course uses elements from Harvard’s legendary CS50 course (Introduction to Computer Science). Which makes it the best introductory course for beginners.
  2. Part 1 covers general comp science concepts like algorithms, data structures, encapsulation, resource management, security, software engineering and so on. Part 2 is all about AI and machine learning and provides a good introduction to the field.
  3. This is a great course if you want to just explore the field of AI before fully committing. It touches on a lot of aspects and real-world applications which should really serve to excite you or at least guide your path into the filed of AI and ML.

Summary

  • Time to Complete: Should take around 50-100 hours for most.
  • Available fully online via edX – a non-profit formed by Harvard and MIT.
  • A good introductory course for AI. Excellent brand value.

Preview Course/ Enroll Now


5. Deep Learning Specialization

Who should take this?

Best suited for developers and techies.

Why take this course?

  1. This is a pretty well-known course in the AI community offered by Andrew Ng who is a bit of a legend himself. Stanford Professor, former Baidu Chief Scientist, founding lead of Google Brain – who better to teach you AI than this guy? The quality of the course and the teaching methodology itself is excellent which have contributed to the popularity of this course.
  2. There are five modules covering several aspects of machine learning with an emphasis on neural networks. Deep learning and neural networks are especially important from a finance and FinTech perspective as they have wide applications in the industry.
  3. While I recommend this course for beginners, it does not mean it does not cover advanced topics. You have everything from Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization and other ML topics to sink your teeth into.
  4. Other topic coverage includes improving deep neural networks,
    structuring machine learning Projects, convolutional neural networks and sequence models. You will learn how to build successful Machine Learning projects.
  5. It also comes packed with several case studies that go into applying these concepts in real-world scenarios for different industries. It looks at AI implementation in healthcare, autonomous driving, sign language reading, music generation, and natural language processing etc. There is something for everyone here.

Summary

  • Time to Complete: Should take about 80 hours for most people.
  • Available fully online and on-demand. Complete at your own pace.
  • You will be learning from one of the best instructors in the field.
  • Recommended for beginners who want to be on the tech side of things including developers and tech leads.

Preview Course/ Enroll Now


6. MicroMasters® Program in Artificial Intelligence from Columbia

Why take this course?

  1. This AI program is offered by Columbia University and is probably the most expansive program on this list. It represents 25% of the coursework toward a Master’s degree in Computer Science at Columbia – so you can probably get an idea about what we are talking about here.
  2. Both Machine Learning and AI are focused on in this course, including a lot of mathematical models that will be helpful in finance especially.
  3. Columbia University is a very strong brand name to have on your Resume. If you are looking to get hired by a bank or a FinTech company for an AI role, that is certainly a consideration that you should have at the top of your mind.

Here is the official video summary of the course:

Summary

  • Time to Complete: About a year.
  • Available fully online via edX – a non-profit formed by Harvard and MIT.
  • Top brand, very comprehensive, significant time commitment.

 Preview Course/ Enroll Now


7. Machine Learning for Trading Specialization

Why take this course?

  1. This is a course custom built for traders who want to take their AI and automation game to the next level. Its been custom built for traders, wealth managers, portfolio and asset managers, hedge fund analysts and others involved in market trades.
  2. The focus is on building algorithms for quantitative trading strategies that can be implemented and then trained to self-update using reinforcement learning concepts.
  3. You need to posses a working knowledge of financial instruments and mathematical concepts. This would be considered an intermediate course.

Summary

  • Time to Complete: Should take around 60 hours for most people.
  • Available fully online and on-demand. Complete at your own pace.
  • Course has been created by Google and NYIF in collaboration. That’s a good combo for FinTech.

 Preview Course/ Enroll Now


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About the Author

Gaurav Sharma

Gaurav started his career as a Corporate and Investment Banking intern at Citi in 2009 and eventually ended up as an Associate Director at Standard Chartered Bank’s Wholesale Banking division a few years later. By 2016, Gaurav was consulting FinTech start-ups in London with product development in the institutional banking space. He also advises mid-market Private Equity/ Asset Management firms and Banks in North America and Europe with investments in the financial services and FinTech sector. Gaurav writes on topics ranging from European Union banking regulations and FinTech to Blockchain startups and the inevitable rise of our AI overlords! He has an Engineering degree in Computer Science and an MBA with a double major in Finance and Marketing. He is also a Certified Financial Risk Manager.