AI, Machine Learning & Deep Learning Essentials

Led by our expert AI / Machine Learning practitioner, students will learn about and explore:

• The What and Why of AI, Machine Learning & Deep Learning – why is this important and exciting?
• Getting the Basics: High-level skills, vocabulary and terminology
• AI, Machine Learning and Deep Learning – what are the differences and uses?
• Latest trends and research
• Who’s Using It and to What Advantage?
• How to adopt AI, ML and DL
• Hands-on Machine Learning – algorithms, neural networks, natural language processing & more
• Tools and Languages: Python, R, Spark, TensorFlow, Keras
• Deep Learning Essentials

1. Exploring Data Science – The Foundation of AI, Machine Learning & Deep Learning

• What is Data Science?
• New Ways of Thinking about and using Data
• Challenges of processing
• Technologies
• Strategies
• Where does data science fit in?
• DS ecosystem – AI, Machine Learning, Deep Learning
• Data and the Scientific Method
• Data Science vs. Data Engineering
• Sharing Results with Colleagues
• Recording experiments
• The Data Science Team members
• Data Science Infrastructure
• Current Tools, Trends & Technologies
• Applying Data Science to Your Industry

2. Understanding AI

• AI - How did we get here?
• Recent advances in data, hardware
• Cutting edge research and applications
• Getting the basics: Core terms and vocabulary

3. Understanding Machine Learning

• Who is leveraging this and why
• Overview of ML – what’s the difference?
• Related examples of ML algorithms and applications
• Surrounding tools and technologies: Python and Spark

4. Machine Learning

• Supervised vs. Unsupervised
• Classification
• Regression
• Clustering
• Dimensionality Regression
• Ensemble Methods

5. Understanding Deep Learning

• What is it, and how is this different than AI and ML?
• Who’s using Deep Learning and Why
• Deep Learning algorithms and applications
• Surrounding tools and technologies: Python, TensorFlow, Keras

6. Expert Systems

• Rules Systems
• Feedback loops
• RETE and beyond
• Expert Systems in practice

7. Neural Networks

• Neural Networks
• Recurrent Neural Networks
• Long-Short Term Memory Networks
• Applying Neural Networks

8. Natural Language Processing

• Language and Semantic Meaning
• Bigrams, Trigrams, and n-Grams
• Root stemming and branching
• NLP in the world

9. Image, Video, and Audio Processing

• Image processing and Identification
• Facial Analysis
• Audio Processing
• Analyzing Streaming Video
• Real-world AV processing

10. Sentiment Analysis

• Sentiment: The beginnings of emotional understanding
• Sentiment indicators
• Sentiment Sampling
• Algorithmic Trading on Sentiment
• Predicting Elections

11. Current Tools of the Trade - AI, ML & DL - Software Ecosystem

• Python, NumPy, Pandas, SciKit
• Hadoop and Spark
• NoSQL Databases
• TensorFlow, Keras, and NLTK
• Drools
• Libraries
• Cloud offerings

12. Making it Happen: How to Adopt AI & ML in Enterprises

• Technology stack
• Assembling an effective team
• Process – how does this all come together
• Best Practices – what do your people need to succeed

13. Resources – where to find more information

14. Time Permitting: Capstone Project

• Hands-on guided workshop utilizing skills learned throughout the course

The general pre-requisite items below would be helpful for attendees to familiarize themselves with in order to gain the most from the discussions and hands-on labs work planned for each general related skills area listed below. Students without supporting experience in certain areas can plan to follow along with labs or utilize them as demonstrations.

Some of the related useful skills

• Required - Enterprise IT / Business Knowledge: Attendees should have some familiarity with Enterprise IT as well as 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 data science, AI and machine learning.
• Recommended - Advanced Math / Statistics: Advanced math and essentials statistics knowledge is useful in understanding and working with Algorithms
• Recommended – Basic Language / Scripting Knowledge: Basic Python (or R) scripting is applicable to machine learning and deep learning. Basic Java is useful for working with some of the advanced tools such as Spark or TensorFlow.