This artificial intelligence 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
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:
- Able to differentiate fact from fiction on AI and machine learning topics
- Ready to have intelligent conversations about the state of AI and ML technologies
- Exposed to real-world use cases where machine learning is working well
- Ready to navigate tool and technology stacks associated with AI and ML, and communicate with your engineering team members about requirements, needs, talent and costs
- Designing or managing projects and programs which may incorporate aspects of AI and ML
- Access to answers to your questions from a senior technical expert in class
- Informed about what AI and machine learning is well suited to do, vs. what it does not do well
- Literate and informed about the scientific and mathematical components of AI and machine learning
- Back to work with a thorough understanding of the different types of machine learning
- Able to translate technical constraints and business concerns among different groups of stakeholders who may not understand the context or priorities of other parties
- Ready to build and lead teams who bring together the requisite skill sets needed for effective AI and machine learning implementation
Course Outline
- Part 1: Introduction
- Part 2: The Big Data Prerequisite
- Part 3: Implementing Machine Learning
- Part 4: Creating Concrete Value
- Part 5: Machine intelligence as part of the customer experience
- Part 6: Machine Intelligence & Cybersecurity
- Part 7: Filling the Internal Capability Gap
- Part 8: Conclusion and Charting Your Course
Who should attend
This artificial intelligence course is for anyone that strategically contributing to the adoption of machine learning and AI features into their projects and applications. Some titles that would find this course beneficial include:
- Anyone in an IT Leadership role
- CIOs / CTOs
- Product Owners and Managers
- Developers and Application Team leads
- Project and Program Managers
- DevOps & Automation Engineers
- Software Managers and Team Leads
- IT Operations Staff
Pre-Requisites
Although it is not mandatory, students who have completed the self-paced Applied Statistics for Data Scientists eLearning course have found it very helpful when completing this course.
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.
School Notes:
Vegetarian, vegan, and gluten-free dietary restrictions can be accommodated. When you purchase your tickets, please reach out to CourseHorse for any food allergies or restrictions.
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