Overview
The Artificial Intelligence implementation boot camp discusses the machine learning landscape and helps build actual use cases for the participants’ own scenarios. The course enables participants to understand the type of teams, roles, platforms and tools that are essential for a practical adoption strategy. During the course, participants will learn how to profile good candidate projects for artificial intelligence features and recognize business opportunities where artificial intelligence could have a major impact. The course has multiple hands-on exercises that ensure that participants get a thorough understanding of the concepts covered in the course.
What You'll Learn
- Able to differentiate fact from fiction on artificial intelligence and machine learning topics
- Exposed to real-world use cases where machine learning is working well
- Prepare to have intelligent conversations about the state of AI and ML
- Ready to navigate tool and technology stacks associated with AI and ML
- Communicate with the engineering teams about requirements, needs, talents and costs
- Designing or managing projects and programs that incorporate aspects of AI and ML
- Learn what AI and ML is best suited to do and what it does not do well
- Understand the different types of machine learning
- Learn to translate technical constraints and business concerns among different stakeholders who may not understand the context or priorities of other parties
- Ready to build and lead teams who bring together the requisite skillsets needed for effective AI and ML implementation
Curriculum
- Working definitions – AI, Machine Learning, Deep Learning, Data Science and Big data
- State of AI – Summarizing major analysts’ statistics and 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 and risks
- Evaluating your Big Data practice
- State of tools – understanding 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
- 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
- Regressions
- 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
- 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
- IoT and the role of machine learning
- Projects based on customer and user needs
- Handling customer enquiries with artificial intelligence
- Creating empathy-driven customer facing actions
- Narrowing down intent
- AI as part of your channel strategy
- How ML can help with security?
- Advanced cybersecurity 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
- Modelling AI after previous security breaches
- Automating and streamlining Incident responses
- How to use deep learning AI to detect and prevent malware and APTs
- Using natural language processing
- Fraud detection
- Reducing compliance testing and cost
- Assessing your technological and business processes
- Building your AI and ML tool chain
- Hiring the right talent
- Developing talent
- How to make AI more accessible to people who are not data scientists
- Launching pilot projects
Who should attend
This boot camp is a perfect fit for anyone who is a position to strategically contribute towards the adoption of machine learning and artificial intelligence in projects and applications in the organization. The course is highly recommended for –
- Anyone in an IT leadership role
- CIOs/CTOs
- Product owners and managers
- Developers and application team leads
- Project and program managers
- DevOps and Automation engineers
- Software managers and team leads
- IT operations staff
Prerequisites
There are no mandatory prerequisites for this course, however, completing the Applied Statistics for Data Scientists course prior to taking up this course would be beneficial.
