Course Overview
TOPAI & Machine Learning for the Enterprise Overview (Hands-on) is a high-level primer on the foundations of AI, introducing each sub-field of AI and how they can be practically exploited in the modern business sense. This course introduces AI from a practical applied business perspective. This course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a ‘first-look’ exposure level course that includes some light hands-on.
Scheduled Classes
TOPWhat You'll Learn
TOPThis course explores:
- 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
Outline
TOPExploring AI / Artificial Intelligence
- Definitions of AI
- Types of AI
- Mathematics in AI
- Deep and Wide learning
- AI and SciFi
- AI in the Modern Age
Exploring 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 the Crystal Ball
Prerequisites
TOPStudents 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.
- The hands-on labs in this leverage basic Python scripts as needed, but the labs can be completed in a ‘follow-along’ format, under the guidance of the instructor. Prior experience with Python can be helpful but is not necessary.
Who Should Attend
TOPThis course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a primer-level overview of these technologies, skills and related tools. 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
