Machine Learning - Fundamentals

Python Foundations & Classification Models using Data Sets
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Machine Learning - Fundamentals.

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Course Outline

A comprehensive list of all sections & lectures for this course can be found below.

Intro to Course

What is Machine Learning? - 07:47

Basics of Machine Learning - 06:34

Installing Anaconda / Python Environment - 07:26

Downloading / Setting Up Atom and Plugins - 09:02

Variables in Python - 08:24

Functions, Conditionals, and Loops in Python - 09:50

Arrays and Tuples in Python - 13:52

Importing Modules in Python - 05:22

What is scikit-learn? Why use it? - 03:52

Installing scikit-learn and scipy with Anaconda - 03:28

Intro to the Iris Dataset - 03:28

Datasets: Features and Labels Explained - 07:39

Loading the Iris Dataset / Examining and Preparing Data - 09:27

Creating / Training a KNeighborsClassifier - 09:43

Testing Prediction Accuracy with Test Data - 12:08

Building Our Own KNeighborsClassifier - 18:00

Next Course: Analyze Visual Data with Machine Learning - 18:00

Course Description

This course offers an insightful introduction into the world of Machine Learning. Here, students learn the foundational concepts, starting with installing a Python Environment, using Anaconda. Students then progress to learning Python basics including variables, arrays, and modules. 

From there, students explore beginner level classification models using Scikit-learn and Spicy with Anaconda. This course also offers a great introduction to working with Data Sets and formulating predictions using KNeighborsClassifier.

What is Machine Learning?

"Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks." - Wikipedia

Machine Learning - Fundamentals

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Teaching 93 Courses

youaccel is currently teaching 93 courses. All courses are currently open for enrollment.

574,171 Enrollments

youaccel currently has 574,171 global enrollments across 93 courses that are active on the platform.

4.5 Star Rating

youaccel has an average rating of 4.5/5 stars, across 93 courses.

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