Explore Machine Learning

This three day course takes a pragmatic approach to machine learning by focusing on practical examples in order to accomplish specific goals. Students will explore different techniques, tools and frameworks while simultaneously building proper mental models to use when tackling machine learning problems.

Description

Interest in machine learning is growing at an incredible rate. In 2022, it is estimated that the machine learning market was valued at $21 billion. By 2029, that number is expected to be over $200 billion.While the field is exciting and vast, it is heavily driven by complex academic research that is filled with PhD level mathematics, unapproachable equations, and a seemingly never endingvocabulary of confusing new terms. At times it can feel that those in the machine learning field intentionally want to keep the club exclusive! To say that the learning curve can be prohibitively steep would be an understatement. 

This three-day course helps you open your eyes to the possibilities right in front of you. It takes a pragmatic approach to machine learning by using practical examples to show how you can use machine learning tools to accomplish specific goals. 

Students will explore different techniques, tools and frameworks while simultaneously building proper mental models to use when tackling machine learning problems. No previous experience with machine learning or complex math is assumed or expected. Upon completion of the course, students will be able to converse fluently about machine learning, learn how to leverage existing machine learning solutions, and build foundational knowledge about model building. 

Syllabus:

Syllabus

Part 1: Demystifying Machine Learning 

In this section, you learn the basics of machine learning and begin to understand what’s important in the world of machine learning. You learn what the ‘magic’ of machine learning is so that you can converse fluently about it. 

  • Define what machine learning is and the types of problems that it is good at solving.

  • Learn what factors should be considered when incorporating machine learning into an existing software project.

Part 2: Leveraging Existing ML Solutions for Specific Cases 

This section is all about standing on the shoulders of giants. There are a plethora of machine learning services available. Learn how to leverage them to solve your own product and development problems. 

  • Explore platform APIs and frameworks to solve common machine learning problems.

  • Explore machine learning web service offerings and learn how to incorporate them.

  • Explore prebuilt models and learn how to integrate them.

  • Learn about data collection and key factors to consider.

  • Gain experience collecting and manually labeling data.

  • Learn about the power of transfer learning, and use it to build various models including object detection.

Part 3: Getting Started with Model Building 

Here, you learn how to get started on a machine learning project. You have seen the fundamental building blocks and learned how to leverage existing systems. Now get a taste of what it’s like to build your own simple model. 

  • Learn python programming fundamentals necessary for machine learning.

  • Gain experience with and an appreciation for web-based tools such Jupyter Notebooks.

  • Gain exposure to core python libraries used by data scientists—NumPy, pandas and Matplotlib.

  • Gain a basic understanding of a neural network and gain experience building one using modern frameworks.

  • Receive guidance on where to go from here.

How to prepare:

Prerequisites

  • A desire to improve your fluency with machine learning concepts and to discover how you can leverage this powerful tool in your work and your creative process.

  • If you are already familiar with basic programming concepts (variables, statements, functions, arrays, data structures, etc.) you will be able to get more out of the exercises, but no prior coding experience is necessary.

  • All course exercises will be done via cloud services. Prior to the first day of class, the list of services will be provided, and accounts must be created. (All services will have free options, which will be sufficient.)

  • Course exercises will use JavaScript and Python, but you do not need prior experience with either.

Who should take this class?

Who Should Take This Course

  • Anyone considering machine learning solutions for their business or product.

  • Executives and practitioners who want to become fluent in the language of machine learning.

  • Developers of all levels and backgrounds who are eager to dive into machine learning. 

  • Developer adjacent individuals, such as project managers, product owners, designers, and engineering managers, who want to gain an understanding of the power of machine learning.