Foundations of ML and AI for Financial Professionals

Foundations of ML and AI for Financial Professionals
NOW SELF-PACED! In this popular course, we aim to bring clarity to how AI and machine learning are revolutionizing financial services. We will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics. Python knowledge required.
 

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Note: You will leave PRMIA.org and be taken to our training partner's registration site. 

Agenda

Course Experts

CRL Credits


Schedule & Duration
Section 1: Optional Python Course-3 lessons (fee)
Section 2: Core Course-8 lessons
Section 3: Optional Capstone Project--1 lesson

Core Course Length:
8, 1.5 hour lessons
Case studies + Labs using the QuSandbox
Total instruction time, up to 14 hours

Time: On-Demand
Flexible and self-paced based on participant knowledge and time availability.

 Course Dates:
Python: Opens October 8-January 31, 2020
Core Course & Capstone Project:
Opens October 27-January 31, 2021
Program is self-paced, based on the weekly roll-out of each lesson. All lessons must be completed by January 31, 2021. 


PRMIA Special Pricing:
Receive a $100 discount at check-out use code: PRMIADISCOUNT100


Prerequisite: Basic Python skills

 


 

About This Course
 

OUR TRAINING PARTNER: QUANTUNIVERSITY

The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts, and through examples and case studies we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

Upon completion of this course, you will be able to:
  • Describe the role of Machine Learning and AI in financial services
  • Describe when ML and AI techniques are used
  • List the key machine learning methodologies
  • Choose an algorithm for a specific goal
  • Participate in case studies with fully functional code

Prerequisite: 

This course will include labs and case studies using the QuSandbox. Participants are expected to have fundamental knowledge of Python. A FREE tutorial on Python is available here

Need additional Python training? At checkout, "Add the 3-part "Just Enough Python for Data Science in Finance" course for just $150 ($349 value) 
Complete details and registration here

   
 
Agenda
 Lesson Opens   Topic       *** All lessons will be accessible through January 31, 2021 ****
Optional 
October 8
  Just Enough Python for Data Science in Finance
- 3-part Python course
- Separate Registration required ($150 with purchase of ML/AI Course)
- Sessions are pre-recorded and available on-demand October 8- January 31, 2021
Lesson 1
October 27

  Machine Learning and AI: An Intuitive Introduction
  • Machine Learning and Statistics
  • A Tour of ML and AI Methods
  • Key Drivers Influencing the Adoption of ML and AI
  • Key Applications
  • Key Players
Lesson 2
November 3

  Exploratory Data Analysis
  • Exploring and Visualizing Large Datasets
Lesson 3
November 10

  Core Methods and Applications
  • Dimension Reduction and Visualizing Datasets Using PCA, T-SNE
Lesson 4
November 17
 
   Case Study + Lab
  • Segmentation Analysis for Equities
  • Using K-means for Automatic Clustering of Stocks
Lesson 5 
November 24
 
   Core Methods and Applications 
  • How Does Supervised ML Work?
  • Evaluating ML Algorithms
Lesson 6
December 1
 
   Case Study + Lab 
  • Machine Learning for Credit
  • Predicting Interest Rates and Credit Risk Using Alternative Datasets
Lesson 7
December 8
   Working with Text 
  • Making Sense of Text and Natural Language Processing
  • Sentiment Analysis
Lesson 8
December 15
   Frontier Topics 
  • Key Issues in Adopting AI and AL into Investment Workflows
  • How ML and AI Will Change the Investment Industry
  • Frontier Topics
Lesson 9
December 22

   Optional Capstone Project
- Included in registration of the core course
 January 31, 2021    Courses close


Who Should Attend
  • Fundamental and quantitative analysts, risk and investment professionals, portfolio managers new to data science and machine learning
  • Financial professionals new to data-driven methodologies
  • Machine learning enthusiasts interested in use cases in Fintech and financial organizations


About Our Expert

  
  Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity.com, a data and Quantitative Analysis Company, and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca, MathWorks, and with more than 25 customers in the financial services and energy industries. He has trained more than 1,000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School. Sri earned an MS in Computer Systems Engineering, an MS in Computer Science, both from Northeastern University, and an MBA with a focus on Investments from Babson College.

 
QuantUniversity  is a quantitative analytics and machine learning advisory based in Boston, Massachusetts. QuantUniversity runs various data science and machine learning workshops in Boston, New York, Chicago, San Francisco and online.  


Continued Risk Learning Credits: 14

PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. Our training partner will issue a Certificate of Completion at the conclusion of the course. CRL credits are self-reporting via you PRMIA membership profile. 

Registration
 Membership Type: Public Fee
Course/Bundle-Now included!
PRMIA Network Members

 Use discount code ==>>  PRMIADISCOUNT100
 BUNDLE AND SAVE! PYTHON FOR DATA SCIENCE + FOUNDATIONS OF MACHINE LEARNING AND AI FOR FINANCIAL PROFESSIONALS $749 $649
MACHINE LEARNING AND AI FOR FINANCIAL PROFESSIONALS  $599 $499

If this is your first time accessing the PRMIA website you will need to create a short user profile to register. Save on registration by becoming a member.

 

Register Now

 Note: You will leave PRMIA.org and be taken to our training partner's registration site. 
Need support?  Contact QuantUniversity at info@qusandbox.com

QUANTUNIVERSITY REGISTRATION POLICIES

This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave PRMIA.org and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. Questions and requests should be directed to QuantUniversity at info@qusandbox.com.

Cancellations must be received by October 31, 2020, for a full refund less a $100 processing fee. Refunds will not be issued: a) once the course content has been accessed and/or b) after October 31. No shows or incompletion of the course(s)  by January 31, 2021. forfeit full payment. This cancellation policy supersedes PRMIA's posted cancellation policy.  In the event sufficient registrations are not received, PRMIA and/or QuantUniversity reserve the right to cancel this course one week prior to the start date. Full refunds will be issued if cancelled by the training partner. 

 
When
10/27/2020 - 1/31/2021
Where
Virtual Course-On-Demand
 

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