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Supervised Learning Demystified

A quick course in using labeled data

Topic: Data
Tobi Bosede

AI and machine learning (ML) are all the rage these days. From our emails to our phones, ML and AI are helping us detect spam and autocomplete text messages along with so much more. However, the fields are often enshrouded in mysticism. Does one need to be a rocket scientist or is it black magic? The answer to both of these questions is no!

Join AI expert, Tobi Bosede, on the journey to demystify machine learning. In this training she will focus on supervised learning. Supervised learning is a type of machine learning approach that enables us to learn from examples. This is important because it is what allows us to do predictive modeling and forecasting for planning purposes. Supervised learning is in contrast to unsupervised learning which does not learn from examples.

Attendees will gain practical skills that they can build on to create value in their workplaces whether they are a seasoned software engineer or in an adjacent role such as product management or business analysis.

What you'll learn-and how you can apply it

  • When to use a supervised model
  • How to fit a regression model
  • How to fit a classification model
  • Logistic Regression
  • K Nearest Neighbor
  • Decision Tree
  • Random Forest

This training course is for you because...

  • You are an experienced engineer looking to pivot into ML
  • You are an inexperienced engineer, data/business analyst, product manager, or executive looking to understand ML


  • Familiarity with introductory statistical math concept
  • Intermediate level understanding of python

Course Set-up:

Recommended Preparation:

Recommended Follow-up:

(Video) Hands-On Unsupervised Learning with Python

About your instructor

  • Tobi is a leader in Artificial Intelligence (AI) and Machine Learning (ML); she’s also built a reputation as an expert in ML and data science. She is versed in big data tools such as Spark, AWS, Docker, Kafka as well as statistics, software engineering, and ML algorithms. She speaks on these topics frequently at local and national tech events such as PyCon, Strata, and Mavens I/O.

    With a Master’s degree in applied mathematics from Johns Hopkins, a decade of experience in technology, and a Bachelor’s degree from the University of Pennsylvania, Tobi is most passionate about leading high-impact initiatives and solving complex technical problems. This has enabled her to build predictive systems for Fortune 500 companies resulting in billions in revenue and multiple patents.

    Aside from her latest venture, Tobi has drawn on her passion for AI to found Ilekun Health, a company whose mission it is to bring transparency to health care. Ilekun Health’s proprietary technology gleans insight around provider quality, services offered, and price from a deluge of complex unstructured health data using artificial intelligence (AI). The insight produced is easy to consume and access thereby empowering patients to make sound decisions around their health.


The timeframes are only estimates and may vary according to how the class is progressing

Segment 1: Supervised Learning Definition (15 min)

  • Background and applications
  • Intuition behind algos
  • Q&A

Segment 2: Regression but Not Backwards (45 min)

  • Background and applications
  • Linear regression
  • multivar regression
  • Q&A
  • Break Length (10 min)

Segment 3: Classification: Binary and Multiclass (40 min)

  • Background and applications
  • Perceptron
  • Logistic regression
  • Q&A

Segment 4: Bi-functional: Algos that go both ways (Regression and Classification) Part I (15 min)

  • Decision Tree
  • Q&A
  • Break Length (10 min)

Segment 5: Bi-functional: Algos that go both ways (Regression and Classification) Part II (50 min)

  • Random Forrest
  • K-Nearest neighbor
  • Support Vector Machines
  • Q&A

Course wrap-up and next steps (5 min)