Course Description
This course provides a foundation of the two largest areas in machine learning: supervised and unsupervised learning. Instructors will demonstrate how machine learning techniques are applied to business problems, as well as how to implement these techniques using popular Python libraries. Lessons incorporate both lectures and hands-on exercises with a focus on cultivating practical skills.
Metis Senior Data Scientists have real-world business experience and will show your team how to apply machine learning concepts to daily tasks. Your team will then be able to hit the ground running, using their new skills to immediately impact their work.
We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place.
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)
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 (Learning objectives below)
DAY 3:
Introduction to Classification
Review day 2
Introduction to Classification
Ensemble-based methods
DAY 4:
Introduction to Neural Nets
Classification Metrics
Neural Net Overview
DAY 5:
Unsupervised Learning
Introduction to Unsupervised Learning
Clustering
Feature engineering for clustering
Pairing supervised and unsupervised learning
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