with Director of Data Science
and AI Elite at IBM
21 JUL 2021 - 30 AUG 2021
Duration:
6 WEEKS
MONDAYS & WEDNESDAYS
4 PM PST / 7 PM EST
Using AI in business is no longer about getting a competitive advantage. Now, it's about staying in the game.
This leading-edge course will teach you fundamental AI skills that you can put to work right away solving challenges in your business, product, or field — no previous coding experience required.
Bring Value to Your Role
Now, the ability to understand and use advanced technologies is the expectation for a growing number of non-technical positions. In live classes and workshops, IBM's Director of Data Science and AI Elite will teach you practical AI skills and empower you to leverage data dynamically and own innovation in your role.
Live and Practical
This course is packed with hands-on practice, live labs, and feedback sessions. Why? Because your business doesn't run on "theory". You'll learn and practice converting your data into a source of AI and hear from two accomplished guest speakers on how they innovate and implement AI projects in business.
the new language
of business
Learn how to build an AI-ready culture at your organization, from the man bringing AI to businesses globally as a head of IBM's AI Elite. Armand will coach you on how to work effectively with Machine Learning Engineers and provide clarity into practical concerns when hiring for these roles.
AI projects
of your own
Armand will get you comfortable with common development tools and concepts used in data analytics and AI-assisted products. You'll train your first basic neural network, master the art of visualizing and interpreting data, and get access to insights and cheat sheets for bringing your own AI project to production.
an AI strategy
for your organization
You'll ideate a use case for your own organization and prepare a strategy for a 3-month AI project. Define your project scope, success markers, and more, working from a template used by world-class organizations. Then, you'll refine your strategy with valuable feedback from your peers and Armand.
RUIZ LinkedIn Profile
- Brings complex AI innovations to solve for everyday business problems as Director of Data Science & AI Elite at IBM
- Led product management of Watson Studio platform, an IBM product that scales AI across any cloud for thousands of business users
- Founder of AI products and communities such as NoCode.ai, a site where he brings together practitioners and explains how to approach and simplify AI projects
- Over a decade of experience helping businesses put data science, machine learning, and AI to work in roles at IBM and as founder of two successful products
COURSE INTRODUCTION
Everybody wants to do AI but nobody knows where to start. Today, we're starting. You will learn about what AI is, its business applications and use cases, and set terminology and definitions.
- Business applications and wider use cases
- Data-driven organizations and transformation
- Difference between AI, machine learning, deep learning, and data science
- What is not AI
Assignment 01: Give three use cases of AI implementation for your current industry. How would you explain its value to your boss or other decision makers to justify the investment, without using technical terms?
The foundation of AI is simple: data. In this class, you will learn how to get data ready for use - training AI. I will give you a lay of the land in terms of big data, management, and infrastructure, and then we will prepare a dataset together using a free online tool.
- Data management and infrastructure
- Extracting intelligence from data
- Understanding data features and correlations
- Lab: How to prepare a dataset for AI
Assignment 02: In the provided use case, you will have to access a retail dataset and get it ready to train a model. You will be asked to connect to the dataset, extract and understand important features, and merge it with other related datasets that will be provided for the exercise.
We will start to get our hands dirty by training a linear regression model. But first, I will introduce you to three simple but powerful machine learning techniques and share insights into choosing the best one for a project.
- Techniques: Linear regression, logistic regression, and clustering
- How to select the best technique and not over-complicate your project
- Lab: How to train a linear regression and do customer segmentation
Assignment 03: We will train an AI model with the dataset that you prepared in Assignment 2 and get predictions using an online tool.
Today, we're getting technical. In this class, you will learn core concepts about deep learning & neural networks, which will serve you as you coordinate projects with Data Scientists. Then, we will train our first neural network together in class.
- Introduction to deep learning and neural networks
- Natural language processing (NLP), computer vision, and transfer learning
- Disrupting AI: Adversarial machine learning
- Lab: How to train your first neural network
Assignment 04: In this assignment, you will learn how to create a deep learning model to classify cars by brand. Then, you will do the same with a model that classifies text reviews as positive, neutral, or negative.
Storytelling is as important in data as in any other business pitch. In this class, I'll show you how to choose the most effective visuals for your data set, how to shape data into meaningful narratives, and I'll demo a data visualization tool you can use on your own team.
- How to interpret data to create meaning
- The art of storytelling
- Intro to data visualization tools (and how to choose)
- Demo: Exploring a data set and creating a dashboard with the data and the predictions of the previous assignments
Assignment 05: Record a short, compelling video telling a story about the results in the dashboard we built in class (3 min max).
What kind of AI tools will serve you best in a given project? In this class, I'll give you an AI Tools Cheat Sheet and help you understand how to choose the best one for your use case and skills. Then, we'll use IBM Watson Studio to solve a use case end-to-end.
- AI Tools Cheat Sheet: No-code through advanced tools
- Lightening tour of several popular AI tools
- Simplifying and making sense of advanced terms
- Lab: Solving a use case end to end with IBM Watson Studio: From data discovery, to data preparation, model selection, and deployment.
Assignment 06: Continue writing queries on your own which modify existing data by renaming variables and changing data types. Perform within-query calculations to create new variables.
In this class, I will show you how to put everything you're learning into production and into life. We will talk about why most AI systems never make it to production, what makes it so difficult, and how to overcome those obstacles. Then, we will deploy our first AI model using the data and systems we've developed so far.
- ModelOps and fundamentals
- Challenges and hacks of productionizing AI
- Lab: How to put models into production in real-time and batch
Assignment 07: Build on the model we've moved towards production in today's workshop. Create an end-point that can be integrated into an application to make it smarter.
If we're building something that's going to be impacting many people, we need to make sure it's ethical and it's something we can trust. This class focuses on designing AI projects to be ethical, transparent, and trustful.
- Opening the black box: Privacy and legal concerns
- Ethical and unethical data use cases
- Detecting and mitigating bias
- AI model drift, model accuracy, and explainability of AI algorithms and predictions
- Lab: Introduction to IBM Watson OpenScale for AI trust and transparency
Assignment 08: Continue using OpenScale and to trust the model built in previous assignments.
How can you enable the culture shift needed for AI in your current business role? In this class, we'll take a look at use cases from several industries in which organizations were able to successfully make that shift.
- Building an AI-ready culture: Why and how
- Define an AI strategy for your company
- The AI Maturity Assessment for your business
- Guest speaker: John Thomas, Distinguished Engineer and Director at IBM, on the most innovative AI use cases in finance, marketing, sales, healthcare
Assignment 09: Knowledge check quiz.
Carrying out an AI project is a lot like any other kind of project planning. It's vital to create order, to understand the investment, and to know who is who and the responsibilities of each role. We will learn holistic strategies for managing these questions and I will share with you a template you can use to set your own implementation plan and success metrics.
- How to evaluate AI investments
- Roles and responsibilities in an AI project
- A leader's methodology for solving business problems with AI
- Templates: Implementation plan and success metrics
Assignment 10: Select a use case for your industry and plan its execution and success, using the project template.
When you're ready to bring Data Scientists on board, what do you need to know to maximize your investment? In this class, we'll learn about data talent management from hiring to retention and effective communication.
- How to communicate with Machine Learning Engineers effectively
- Hiring Data Scientists: Where to look, how to attract, and salary expectations
- Influencers and blogs to follow to stay up to date
- Guest speaker: Andre Violante, Data Science Manager at IBM, on managing phenomenal teams
Assignment 11: Using the template you received in class, deliver an AI strategy for your role that is achievable within 90 days.
Our last class is training for one of the most important parts of successful projects: giving feedback. I will share tips for how to be precise in your feedback. Then, you will present your 90-day AI plan and get feedback from your peers and me.
- Presentations of 90-day plans
- Template: Giving and receiving feedback