Artificial intelligence (AI) and machine learning (ML) are opening up new ways for enterprises to solve complex problems, but they will also have a profound effect on the underlying infrastructure and processes of IT. AI/ML is a dominant trend in the enterprise with the ubiquity of large amounts of observed data, the rise of distributed computing frameworks and the prevalence of large hardware-accelerated computing infrastructure became essential. There are several compute platforms designed and purpose-built server for Deep Learning. These platforms should be storage and I/O-optimized to deliver an industry-leading performance for training models which speeds up Deep Learning (DL) using neural networks and large data sets to train computers for complex tasks. The Introduction to Artificial Intelligence (AI) and Machine Learning (ML) is a 1-day instructor-led course which introduces the learner to the Artificial intelligence (AI), machine learning (ML), and Deep Learning (DL) essentials in addition to compute platforms such as Cisco UCS, through a combination of lecture and hands-on labs.
Prerequisites:
The knowledge and skills that the learner should have before attending this course are as follows:
- Understanding of server design and architecture
- General Understanding of Technology
- Interest in AI and ML
- Analytical Thinking
- No specific technical or programming skills are required
Course Objectives:
Upon successful completion of the course, learners will be able to discuss and understand:
- Introduction to AI: Essentials of AI, including its history, key concepts, applications, and ethical aspects.
- AI Applications: Overview of AI's real-world uses in various industries.
- Machine Learning Basics: Core principles of machine learning, algorithms, and data analysis.
- Machine Learning Use Cases: Diverse industry case studies in machine learning.
- Deep Learning Overview: Fundamentals of deep learning and its practical uses.
- NLP, NLU, & NLG: Introduction to language processing and generation technologies.
- Generative AI Models: Insight into models like ChatGPT, Bard, and CoPilot.
- Cisco AI Uses: Cisco's application of AI in their products and services.
- AI in Business Strategy: Key strategies for integrating AI in business.
Introduction to Artificial Intelligence
- Introduction to Artificial Intelligence
- History of Al
- Types of Al
- Importance of Al
- Al vs. Human Intelligence
Applications of Artificial Intelligence
Based on Class Desires will cover up to 6 of these:
- Marketing
- Finance – Defense & Military
- Telecommunication
- Sales
- Healthcare
- Automobile Industry
- Gaming
- E-Commerce Industry
- Social Media
- Robots
- Education Sector
- Chatbots
- Agriculture
- Supply Chain
- Navigation
- Lifestyle
- Human Resources
Core Concepts of Machine Learning
- Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Steps in Machine Learning
- Data Collection
- Data Preparation
- Choosing a Model
- Training the Model
- Evaluating the Model – Parameter Tuning – Making Predictions
- Advantages of Machine Learning
- Disadvantages of Machine Learning
- Future of Machine Learning
Use Cases of Machine Learning
- Introduction to Automatic Language Translation using ML
- Introduction to Medical Diagnosis Using ML
- Introduction to Image Recognition using ML
- Introduction to Speech Recognition using ML
Deep Learning
- Introduction to Deep Learning
- Working of Deep Learning
- Machine Learning vs. Deep Learning
- Deep Learning Process
- Advantages of Deep Learning
- Applications of Deep Learning
- Limitations of Deep Learning
Fundamentals of NLP, NLU, & NLG
- Introduction to Natural Language Processing (NLP)
- Introduction to Natural Language Generation (NLG)
- Introduction to Natural Language Understanding (NLU)
- NLP vs. NLG vs. NLU
- AI vs. ML
- ML vs. DL
- AI vs. ML vs. DL
Generative AI - ChatGPT, Bard, CoPilot
- Introduction to Generative AI
- Generative AI architecture
- AI-based techniques and technologies
- Generative Models
- Generative AI market
- Generative AI ethics
- Future Directions in Generative AI
- Economic impact of generative AI
Cisco AI Technology Use Cases
- Cisco DNA Center: AI for network optimization and issue prediction.
- Cisco Webex: AI-enhanced collaboration tools.
- Cisco Meraki: AI-driven cloud-managed IT.
- Cisco Security: AI for advanced threat detection.
- Cisco SD-WAN: AI-optimized network performance.
- Cisco AppDynamics: AI for real-time app monitoring.
- Cisco ThousandEyes: AI for network intelligence.
Essentials of Successful AI Strategy for Business
- Al Strategies for Business Outcomes
- Evaluating Current Capacities of Al
- Building an Al Strategy
- Roadmap For Building a Viable Al Strategy
- Strategy for Al Business Models
- Five-Step Implementation Plan for Al
- Al Assessment Roadmap
The primary audience for this course is as follows:
- Sales professionals transitioning into the AI space
- Sales engineers with limited AI knowledge
- Account executives looking to specialize in AI sales
- Marketing professionals supporting AI product launches