Live Virtual Courses
October 8 & 10 | 1:00 - 3:00 PM ET
CPE Field of Study: Finance (FIN)
In today's rapidly evolving digital landscape, the significance of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) in reshaping both business and society is undeniable. However, the complexity of these technologies often appears intimidating, raising questions about the necessity of coding knowledge and proficiency in mathematics, statistics, or data science. The answer is a resounding no.
Introducing our dynamic, no-code AI course, designed for finance professionals who aspire to harness the capabilities of AI and ML in their business endeavors without delving into the intricacies of coding. This course emphasizes practical application over theoretical knowledge, making it ideal for those interested in analytics and logical reasoning to enhance their insights and process efficiency. Our approach simplifies learning by eliminating technical obstacles and focusing on business impact: formulating pertinent queries, managing data quality, evaluating algorithms, interpreting results, addressing biases, and demonstrating leadership.
Participants will engage with real-world case studies, such as predicting healthcare expenditures and detecting insurance fraud, to understand how AI, ML, and GenAI can be leveraged in various business contexts. By the end of the course, participants will confidently apply GenAI and AI solutions responsibly and inclusively, ensuring beneficial implementations for all. Join us to explore the exciting world of AI, ML, and GenAI, where technical proficiency is not a barrier to innovation and impact.
- Understand AI and Machine Learning's role in business
- Learn how to implement AI Solutions
- Develop Critical Data Skills and its with relation to AI
Earlier, he was director of performance management at Google and senior director of enterprise performance management strategy at Oracle. Prior to his high tech career, Gauthier was Remy Cointreau’s controller and in charge Remy Suisse Finance, the group internal bank. He received MBAs from EDHEC Business School.
Sneak Peek
Day 1
Let’s start on the right basis (20 min)- Get the AI basics
- AI, machine learning: what you must know to get started
- Why the need for AI?
- How do machines learn?
- What machines can’t do yet and what they are great at. Disclaimer: machine learning is not a crystal ball.
- Case in class: predict the price of a house
- How to frame the right question for AI?
- Organize your business analytics process (CRISP/DM)
- Respect the pillars of business ML processes
- How advanced visualization can be a tell-tale of a case for machine learning (correlation matrix, bubble charts)
- Discover your Machine Learning algorithmic toolbox. Learn the four families of ML algorithms and what they can do for you:
- Predict values with regression
- Predict outcomes or classes with classification
- Cluster elements by affinity
- Project future trends by learning from the past and understanding what you can anticipate.
- Case in class: Predict the level of medical spending with regressions
- Case in class: Predict fraud on insurance claims
- Cases in class: See groups of credit card clients in seconds to target your marketing
- Cases in class: Sales prediction. Where is your revenue heading over the next months
Day 2
Get better data faster natural language coding with GenAI (80 min)- Grasp the full spectrum of Data preparation and Data Quality
- The multiple aspects of bad data
- The importance of Master Data Management
- The 4 families of Data Preparation techniques
- Lean Data Quality Framework
- Apply GenAi-produced Python code to boost your data capabilities in:
- Data preparation
- Data profiling
- Case in class: Automation of Profiling to achieve data review in seconds and data fixing
- Case in class: Massive data processing in seconds with govt data
- Lead with ML to achieve impactful, sustainable, ethical, and unbiased results
- Change management with AI project
- Typical mistakes in ML projects
- Ethics and Inclusion in AI
- Cases in class:
- Biased recruiting: The Amazon case
- The anonymization of private data
- The data capture and privacy wall: the Watson case