This course runs for a duration of 1 day.
The class will run daily from 8:30 am CST to 4:30 pm CST.
Class Location: Boston - Boston, MA.
Overview
This course prepares you to strategically contribute to the adoption of machine learning and AI features in your own projects and applications.
Learn to separate reality from myth, and filter real-world applications from business media buzz. This class is a fast-paced, intensive literacy class which leaves you quickly equipped with a broad range of management tools to incorporate machine intelligence into your own business strategy. “AI” is a buzzword, but the actual technology behind machine learning and other machine intelligence services is very real. Although there is broad consensus among major management analysts that AI and machine learning are immediate disruptors to most technology services, there is still very little practical adoption when it comes to integrating these features.
In 2018, you don’t need a Ph.D. to realize value from these emergent technologies in your own business units.
The difficulties of adoption come with good reason. The data science and application engineering skills required to execute on a machine intelligence strategy and demonstrate concrete value from it are still the domain of only a few. But with tools such as Google’s open-source TensorFlow and others coming online all the time, suddenly much of the doctoral-level science of AI is already built into services that are more accessible to development teams. Even small wins on an AI strategy can move the needle, and competitive position is being grabbed by those that can execute.
This class teaches you how to navigate the machine intelligence landscape and build actual use cases for your own scenarios. You’ll learn what types of teams, roles, platforms, and tools are required for a practical adoption strategy. You’ll learn to profile good candidate projects for AI features and spot business opportunities where AI could be useful. Group exercises allow you to exchange ideas with peers and work together to arrive at your own creative examples. The level of detail covered in this workshop leaves you thoroughly informed about the state of the art in AI and machine learning, and ready to face the future on your own teams.
Immediate benefits of attending this course include:
Course Outline
Part 1: Introduction
Working definitions: AI, Machine Learning, Deep Learning, Data Science & Big Data
State of AI: summarizing major analysts’ statistics & predictions
Summarizing AI misinformation
Effects on the job market
Today’s AI use cases
Where it works well
Where it doesn’t work well
What do high profile uses have in common?
Addressing legitimate concerns & risks
Case study break: We will introduce the class to three real-world use cases – one in finance, one in health science, and one in general operations. In small groups, you will discuss implications of the cases and see if you and your peers can spot any parallel opportunities in your own business.
Part 2: The Big Data Prerequisite
Evaluating your big data practice
State of tools – understanding intelligent big data stacks
Visualization and Analytics
Computing
Storage
Distribution and Data Warehousing
Strategically restructuring enterprise data architecture for AI
Unifying data engineering practices
Datasets as learning data
Defeating Bias in your Datasets
Optimizing Information Analysis
Utilizing the IoT to amass a large amount of data
Part 3: Implementing Machine Learning
Examine pillars of a practicing AI team
Business case
Domain expertise
Data science
Algorithms
Application integration
Bettering Machine Learning Model Management
State of tools – understanding intelligent machine learning stacks
Machine Learning Methods and Algorithms
Decision Trees
Support Vector Machines
Regression
Naïve Bayes Classification
Hidden Markov Models
Random Forest
Recurrent Neural Networks
Convolutional Neural Networks
Developing Validation Sets
Developing Training Sets
Accelerating Training
Encoding Domain Expertise in Machine Learning
Automating Data Science
Deep Learning
Example: TensorFlow – We will take a look at Google’s TensorFlow as a tool for integrating machine learning features. We’ll come away from the exercise with an understanding of the programming skills needed to leverage TensorFlow and the impacts of normal application workflow.
Part 4: Creating Concrete Value
Opportunities for automation
Understanding automation vs. job displacement vs. job creation
Finding hidden opportunities through improved forecasting
Production and operations
Adding AI to the Supply Chain
Marketing and Sales Applications
Predict Customer Behavior
Target Customers Efficiently
Manage Leads
AI-powered content creation
Enhancing UX and UI
Next-Generation Workforce Management
Explaining Results
Use case breakout: Scoring the criteria for three potential applications. In groups, we’ll evaluate application use cases for machine learning: Medical imaging, electronic medical records, and genomics. We’ll grade each use case based on a scorecard for the following:
Quantity of data
Quality of data
ML techniques
Part 5: Machine intelligence as part of the customer experience
IoT and the role of machine learning
Projects based on customer & user needs
Handling customer inquiries with AI
Creating empathy-driven customer facing actions
Narrowing down intent
AI as part of your channel strategy
Part 6: Machine Intelligence & Cybersecurity
How can ML help with security?
Advance cyber security analytics
Developing defensive strategies
Automating repetitive security tasks
Close zero-day vulnerabilities
How are attackers leveraging ML and AI?
Building up trust towards automated security decisions and actions
Automated application monitoring as a security layer
Identifying Vulnerabilities
Automating Red Team/Blue Team Testing Scenarios
Modeling AI after previous security breaches
Automating and streamlining Incident Responses
How use deep learning AI to detect and prevent malware and APTs
Using natural language processing
Fraud detection
Reducing compliance testing & cost
Part 7: Filling the Internal Capability Gap
Assessing your technological and business processes
Building your AI and machine learning toolchain
Hiring the right talent
Developing talent
How to make AI more accessible to people who are not data scientists
Launching pilot projects
Part 8: Conclusion and Charting Your Course
Review
