training

1.0 Overview

Over the years I have taught several classes, seminars, and bootcamps. Below is a summary:

AI for Senior Leaders

This 2-hr seminar is for senior leadership. In this short course we will dispell the myths around AI and teach you the top-level information you need to assess your buisness model for adoption of AI. We will provide you the tools you need to decide how to build an AI team or upskill your existing workforce. Finally, we will provide you an overview of each of the most popular algorithms in supervised learning, unsupervised learning, and deep learning, as well as business use cases for each.

AI for Senior Technical Leaders

This 2-hr seminar is for senior technical leadership. In this short course we will dispell the myths around AI and teach you the top-level information you need to assess your buisness model for adoption of AI. We will provide you the tools you need to decide how to build an AI team or upskill your existing workforce. Finally, we will provide you an in-depth view of each of the most popular algorithms in supervised learning, unsupervised learning, and deep learning, as well as business use cases for each.

Basic-Level Data Science Bootcamp

This 2-week boot camp is for aspiring data scientists. We will take each student from an introduction to Jupyter Notebooks and Python, to the state-of-the-art in Deep Learning. Each section is organized with 30% lecture + 70% hands-on training. Each student will develop a uniquely tailored Capstone Project and present it at the end of the course.

Intermediate-Level Data Science Bootcamp

This 1-week boot camp is for aspiring data scientists. We will take each student from an introduction to simple linear regression, to the state-of-the-art in Deep Learning. Each section is organized with 30% lecture + 70% hands-on training. Each student will develop a uniquely tailored Capstone Project and present it at the end of the course.

Advanced-Level Data Science Bootcamp

This 1-week boot camp is for advanced-level data scientists. At the advanced-level, we will look at the theory and algorithms from the simple linear regression, to advanced topics, including deep-learning and Bayesian state estimation. Each section is organized with 30% lecture + 70% hands-on training. Each student will develop a uniquely tailored Capstone Project and present it at the end of the course.

2.0 The Conceptual Approach

I prefer to teach AI/ML, and more broadly Data Science, with two approaches:

2.1 "Top-Down" Approach

In the Top-Down approach, you don't need to know the math, or be a deep expert in Python. I will teach you the tools, using industry best practices and rules-of-thumb, so that you will be a solid contributing member of a Data Science team.

To get started, I recommend the self-paced tutorials by Jake VanderPlas in his text Python Data Science Handbook.

2.2 "Bottom-Up" Approach

In the Bottom-Up approach, I assume that you already have a solid foundation in math and/or computer science. I will teach you the algorithms, both how to manipulate and optimize them for your application. With these powerful skills, you will be a technical leader enabling the full potential of a Data Science team.

To get started, I recommend that you review the following:

1. ML technical notes.
2. "Math Refresher for Machine Learning".
3. "Machine Learning: A Conceptual Approach".