This course covers some basics of the Supervised Machine Learning Pipeline - the steps that we take to load and clean data and make it suitable for consumption by an ML model, as well as how we tune a model to maximize its performance.
Supervised Machine Learning Pipelines
Follow the journey of data as it goes through a typical Supervised Machine Learning Pipeline
13 Tutorials
0 Exercises
Beginner Level
100% Online
Self-paced
or
Our Alumni Work At
About this course
Contributors & Instructors
T
Thom Ives, Ph.D.
Sr. Data Scientist, Echo Global Logistics
T
Teena Mary
Data Practitioner, impress.ai
L
Louis Owen
NLP Research Engineer, yellow.ai
What you will learn?
Steps in the ML Pipeline
Role of each step
Course Content
1. Introduction
2
Session Overview
The ML Pipeline
2. Features
6
Missing Values
Data Cleaning
Encoding Features
Normalization
Feature Reduction
Feature Engineering
3. Models
5
Engineering Labels
Introduction
K-Fold Cross Validation
Human Oversight
Automating the Pipeline
