Course Outline
Pre-Requisites
Professional Credits:
8 NASBA CPEs
Lessons
- 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
Part 1: Introduction
- Working definitions: AI, Machine Learning, Deep Learning, Data Science & Big DataÂ
- 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
- 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
- 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
- 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
- Review
- Charting Your Course
- Establishing a timeline
- Open Discussion
Cancellation Policy
If a change needs to be made to your public course registration (cancel, transfer, or substitution) ASPE must receive written notice via email at customerservice@aspeinc.com or fax at 919-816-1710. If a cancel or transfer request is made less than 15 business days prior to the class start date, payment will still be due, no refunds will be issued and you will be charged a $200 change fee. Your paid tuition will be available for one year to be used as a credit towards another course of equal value; only one reenrollment opportunity is allowed. Failure to attend the course without written notification will result in forfeiture of the full course price. Student substitutions may be made at any time prior to the start of class free of charge. If ASPE is forced to cancel a course for any reason, liability is limited to the registration fee only.
Training Location
Virtual
Your Address
Your City,
Your Province
Your Country