Artificial Intelligence (AI) Overview for the Enterprise
COURSE OUTLINE:
Artificial Intelligence Overview for the Enterprise is a technical primer on the foundations of AI, introducing each sub-field of AI and how they can be practically exploited in the modern business sense.
Audience
This course is ideally suited for a wide variety of technical learners who need a fast paced, hands-on introduction to the core skills, concepts and technologies related to AI programming and machine learning.� Attendees might include:
- Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
- Analytics Managers who are leading a team of analysts�
- Business Analysts who want to understand data science techniques
- Information Architects who want to gain expertise in Machine Learning algorithms�
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in Data Science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insight about customers
Prerequisites
Students attending this class should have a grounding in Enterprise computing. While there�s no particular class to offer as a prerequisite, students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.
Learning Objectives
This course introduces AI from a practical applied business perspective.�
Working in a hands-on learning environment, led by our expert AI course leader, students will explore and learn:
- What AI is and what it isn�t
- The different types and sub-fields of AI
- The differences between Machine Learning, Expert Systems, and Neural Networks
- The latest in applied theory
- How AI is used in processing language, images, audio, and the web
- The current generation of tools used in the marketplace
- What�s next in applied AI for businesses
Lesson: Artificial Intelligence
- Definitions of AI
- Types of AI
- Mathematics in AI
- Deep and Wide learning
- AI and SciFi
- AI in the Modern Age
Lesson: Machine Learning
- Supervised vs. Unsupervised
- Classification
- Regression
- Clustering
- Dimensionality Regression
- Ensemble Methods
Lesson: Expert Systems
- Rules Systems
- Feedback loops
- RETE and beyond
- Expert Systems in practice
Lesson: Neural Networks
- Neural Networks
- Recurrent Neural Networks
- Long-Short Term Memory Networks
- Applying Neural Networks
Lesson: Natural Language Processing
- Language and Semantic Meaning
- Bigrams, Trigrams, and n-Grams
- Root stemming and branching
- NLP in the world
Lesson: Image, Video, and Audio Processing
- Image processing and Identification
- Facial Analysis
- Audio Processing
- Analyzing Streaming Video
- Real-world AV processing
Lesson: Sentiment Analysis
- Sentiment: The beginnings of emotional understanding
- Sentiment indicators
- Sentiment Sampling
- Algorithmic Trading on Sentiment
- Predicting Elections
Lesson: Current Tools of the Trade
- Python, NumPy, Pandas, SciKit
- Hadoop and Spark
- NoSQL Databases
- TensorFlow, Keras, and NLTK
- Drools
Lesson: What�s Next in AI
- Current Developments
- Gazing the Crystal Ball