Machine Learning : A Beginner's Basic Introduction

Learn Machine Learning Basics with a Practical Example

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

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.

• 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.