Artificial Intelligence (AI) Overview for the Enterprise | Hands-on
Learn the foundations of Artificial Intelligence (AI), including its sub-fields, and how it can be applied in modern business. This course introduces AI from a practical business perspective.
WHAT YOU'LL LEARN
Join an engaging hands-on learning environment, where you’ll 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
WHO SHOULD ATTEND?
Business Analysts, Data Analysts, Developers, Administrators, Architects, Analytics Managers, and technical Executives who are new to AI.
PREREQUISITES
To gain the most from this course, you should have:
- A grounding in enterprise computing
- Familiarity with enterprise IT
- A high-level understanding of systems architecture
- Knowledge of your business drivers that could take advantage of AI
- Basic knowledge of scripting
COURSE OUTLINE
Artificial Intelligence
- Definitions of AI
- Types of AI
- Mathematics in AI
- Deep and Wide learning
- AI and SciFi
- AI in the Modern Age
Machine Learning
- Supervised vs. Unsupervised
- Classification
- Regression
- Clustering
- Dimensionality Regression
- Ensemble Methods
Expert Systems
- Rules Systems
- Feedback loops
- RETE and beyond
- Expert Systems in practice
Neural Networks
- Neural Networks
- Recurrent Neural Networks
- Long-Short Term Memory Networks
- Applying Neural Networks
Natural Language Processing
- Language and Semantic Meaning
- Bigrams, Trigrams, and n-Grams
- Root stemming and branching
- NLP in the world
Image, Video, and Audio Processing
- Image processing and Identification
- Facial Analysis
- Audio Processing
- Analyzing Streaming Video
- Real-world AV processing
Sentiment Analysis
- Sentiment: The beginnings of emotional understanding
- Sentiment indicators
- Sentiment Sampling
- Algorithmic Trading on Sentiment
- Predicting Elections
Current Tools of the Trade
- Python, NumPy, Pandas, SciKit
- Hadoop and Spark
- NoSQL Databases
- TensorFlow, Keras, and NLTK
- Drools
What’s Next in AI
- Current Developments
- Gazing at the Crystal Ball
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