Machine Learning : A Beginner's Basic Introduction

What you'll learn

Install environment to test Machine learning
Understand basic machine learning vocabulary
Exposure to Machine Learning Frameworks
Understand Supervised Machine Learning
Create a basic home estimator calculator
Load a Dataset
Make Predictions from dataset

Requirements

You should be able to use a PC at beginner level
Basic knowledge of Python would help but not mandatory

Description

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions.

Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics.
In this course, we will use machine learning to build a value estimation system that can deduce the value of a home. Although the tool we will build in this course focuses on real estate, you can use the same approach to solve any kind of value estimation.
What you'll learn include:

Basic concepts in machine learning
Supervised versus Unsupervised learning
Machine learning frameworks
Machine learning using Python and scikit-learn
Loading sample dataset
Making predictions based on dataset
Setting up the development environment
Building a simple home value estimator

The examples in this course are basic but should give you a solid understanding of the power of machine learning and how it works.

Who this course is for:

Absolute beginners to Machine Learning