Machine Learning Foundations

Training overview
Corporate / Group Training
5 days
Start dates
United States of America
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Course description

Machine Learning Foundations

Metis Senior Data Scientists have the real world business experience and will show your team how to apply concepts to day-to-day tasks. Your team will hit the ground running with the ability to immediately impact their work with what they’ve learned.

Machine Learning Foundations explains the two largest areas in machine learning: supervised and unsupervised learning. A Metis instructor will show how machine learning techniques are applied in business problems, as well as how to implement these techniques with popular Python libraries. Lessons incorporate both lectures and hands-on exercises with a focus on cultivating practical skills.

Machine Learning Foundations Course Outcomes

Upon completion of the course, attendees should be able to:

  • Define “Machine Learning” and common terminology
  • Explain the different types of machine learning and the problems each can solve
  • Identify if a problem is a regression, classification, or clustering problem
  • Identify a useful metric for the business problem and optimize a model against it
  • Estimate the performance of a model on new data
  • Train and predict on messy datasets, including data that has outliers and/or missing data
  • Identify important features for the model
  • Identify the strengths and weaknesses when selecting a model for a problem
  • Apply and explain clustering (e.g. customer segmentation)
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COVID-19 Update

In light of COVID-19, this provider is now delivering some or all of their courses online. Contact them for more information!

Who should attend?


Some experience with Python (ability to write loops and use simple functions). The Python for Data Analysis course is a way to upskill a team to this point.


Data scientists, statisticians, analysts, or others in similar analytical and quantitative roles

Training content

Machine Learning Foundations Training Content

Day 1: Introduction to supervised learning and regression

  • Probability and statistics review
  • Exploratory Data Analysis (EDA)
  • Introduction to Machine Learning
  • Linear Regression (Ordinary Least Squares)
  • Polynomial Regression
  • Overfitting vs Underfitting

Day 2: Regularization

  • Review of Day 1
  • Cross-validation and measuring generalizability
  • Overfitting vs Underfitting with Regularization
  • Feature engineering
  • Logistic Regression start

Day 3: Introduction to Classification

  • Review of Day 2
  • Introduction to Classification
  • Ensemble-based methods

Day 4: Introduction to Neural Nets and Metis

  • Classification Metrics
  • Neural Net Overview

Day 5: Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Clustering
  • Feature engineering for clustering
  • Pairing supervised and unsupervised learning

About Metis



Metis accelerates data science and analytics learning for individuals, companies, and institutions. The Corporate Training team at Metis has been a trusted training partner to Fortune 500 clients around the globe and across numerous industries since 2015.  We are respected...

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623 Broadway
10012 New York New York

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