Introduction to Machine Learning for Mere Mortals: Solving Common Business Problems with Data Science

Speaker:

Jen Underwood (Founder & Principal Consultant, Impact Analytix, LLC)

Location: Jamaica

Date: Monday, April 30

Time: 1:00pm - 5:00pm

Pass Type: All Access, Summits & Workshops

Format: Workshop

Track: Data & Analytics

Conference Journey: Business/Data Analyst

Audience: Introductory

Vault Recording: TBD

Audience Level: Introductory

Machine learning is one of the hottest topics in tech today. It is a must-have organizational competency in the data-driven era of digital transformation. Despite the unprecedented speed and ease of creating predictive models today, the human mind is still essential for generating good machine learning models. In this fast-paced introductory class, participants will be introduced to fundamental concepts and walk-through the entire machine learning lifecycle with optional hands-on exercises using open source tools. From selecting the right problem to solve to preventing algorithm bias, machine learning is still an art and a science. Participants will learn how machine learning works, how to prevent common mistakes and learn how to build and use machine learning models.

Who Should Attend: This course is designed for technical professionals that want to understand how machine learning works, how to best apply it, and how to get started.

Topics include:

  • Introduction to machine learning, including a discussion of the different areas of data science, buzzwords and hype, how machine learning works, types of analytics models, and how to solve common problems
  • The machine learning lifecycle, including selecting the right problem; preventing algorithm bias; framing questions and problems; providing adequate data; data collection and preparation; common tools and techniques; cleansing, shaping and transformation; and sampling methods
  • Machine learning model development, including popular open source tools, building models, evaluating performance, and continually improving
  • Operationalizing machine learning models, including integration techniques, using models in SQL queries, embedding with APIs, and code generation

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