Introduction to Machine Learning and Supervised Learning
Introduction to Machine Learning and Supervised Learning
Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level
Overview/Description
Machine learning includes many different fields that focus on different problems. In this course, you will learn what machine learning is and the fundamentals of supervised learning.

Target Audience
Anyone interested in understanding machine learning and learning how to use it to solve problems

Prerequisites
None

Expected Duration (hours)
0.9

Lesson Objectives Introduction to Machine Learning and Supervised Learning

start the course
define machine learning and how it can be used to solve a variety of problems
define supervised machine learning
describe the fundamentals of building machine learning models to solve a problem
describe overfitting, how it can be a problem, and how to mitigate it
evaluate machine learning models and compare them
define the linear regression model for one and multiple variable problems
describe the gradient descent algorithm for training linear regression models
describe the k-nearest neighbor model and how to learn it
describe decision tree models and how to learn decision trees using the C4.5 algorithm
set up scikit-learn for Python
import data, and perform basic tasks with scikit-learn for Python
use scikit-learn to fit a linear regression model to a dataset
use scikit-learn's k-nearest neighbor model
use scikit-learn to fit a decision tree model to a dataset
use scikit-learn and GraphViz to generate a decision tree model from a dataset
use scikit-learn to calculate the precision and the recall of different machine learning models in Python
fit a linear regression model to a dataset with scikit-learn and Python

Course Number: sd_exml_a01_it_enus

Expertise Level
Beginner