Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. 

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. 

This workshop provides a broad introduction to machine learning, datamining, and statistical pattern recognition. 

Topics include: 

  1. Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). 
  2. Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 
  3. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). 

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

WHAT YOU’LL LEARN

This workshop introduces AI from a practical applied business perspective. 

Working in a hands-on learning environment, led by expert AI course leaders, attendees will explore and learn:

  1. What AI is and what it isn’t
  2. The different types and sub-fields of AI
  3. The differences between Machine Learning, Expert Systems, and Neural Networks
  4. The latest in applied theory
  5. How AI is used in processing language, images, audio, and the web
  6. The current generation of tools used in the marketplace
  7. What’s next in applied AI for businesses

Who Should Attend

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

Agenda

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