course-machine-learning-introduction#01-basics#02-learning-techniques

course-machine-learning-introduction#01-basics#02-learning-techniques#recipe

Machine Learning Techniques

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course-machine-learning-introduction#01-basics#02-learning-techniques#section-supervised-learning

Supervised Learning

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Supervised Learning: Goal

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Quiz: Supervised Learning Facts

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Quiz: Estimated Rent Costs

course-machine-learning-introduction#01-basics#02-learning-techniques#section-unsupervised-learning

Unsupervised Learning

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Unsupervised Learning: Example

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Quiz: Unsupervised Learning Facts

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Quiz: Wine Clustering

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Quiz: Cluster New Observation

course-machine-learning-introduction#01-basics#02-learning-techniques#section-reinforcement-learning

Reinforcement Learning

course-machine-learning-introduction#01-basics#02-learning-techniques#section-reinforcement-learning-example

Reinforcement Learning: Example

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Quiz: Reinforcement Learning Facts

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Quiz: Personalized Music Playlist

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Quiz: Storage Cost Optimization

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Quiz: Alpha Go

Machine Learning Techniques: Supervised-, unsupervised- and reinforcement learning

Learn machine learning fundamentals including use cases and different learning techniques. Differentiate between regression and classification.

Machine Learning Level 1

course-machine-learning-introduction#01-basics

course-machine-learning-introduction#01-basics#01-what-is-machine-learning

course-machine-learning-introduction#01-basics#01-what-is-machine-learning#recipe

What is Machine Learning?

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Artificial Intelligence

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Machine Learning

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Deep Learning

course-machine-learning-introduction#01-basics#01-what-is-machine-learning#quiz-1

Quiz: AI, ML and Deep Learning Facts

course-machine-learning-introduction#01-basics#01-what-is-machine-learning#section-popular-use-cases

Popular Use Cases

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Use Case: Search Engines and Recommender Systems

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Use Case: Machine Translation

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Use Case: Music Identification and Speech Recognition

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Use Case: Self driving cars

course-machine-learning-introduction#01-basics#01-what-is-machine-learning#quiz-2

Quiz: Automatic Email Reply

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Quiz: Facial Recognition

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Quiz: Sentence Autocomplete

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Quiz: Password Manager

course-machine-learning-introduction#01-basics#03-regression-classification

course-machine-learning-introduction#01-basics#03-regression-classification#recipe

Supervised Learning with Regression and Classification

course-machine-learning-introduction#01-basics#03-regression-classification#section-supervised-learning-input-data

Supervised Learning: Input Data

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Predictors

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Outcome Variable

course-machine-learning-introduction#01-basics#03-regression-classification#quiz-1

Quiz: Predictors and outcome variables

course-machine-learning-introduction#01-basics#03-regression-classification#section-regression-vs.-classification

Regression vs. Classification

course-machine-learning-introduction#01-basics#03-regression-classification#section-regression-vs.-classification-example

Regression vs. Classification Example

course-machine-learning-introduction#01-basics#03-regression-classification#quiz-2

Quiz: House Price

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Quiz: Flight ticket

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Quiz: Dog Breeds

Start your Machine Learning journey and learn the most fundamental building blocks.

Machine Learning Essentials

Introduction to Machine Learning

Clustering methods are often applied as an attempt to define groups of customers who are similar. Similarity can mean matching interests, demographics, geographic location etc. This application is called market segmentation. The aim of this use case is to better understand the customer base and their needs. Clusters might be then used to provide targeted advertisements and offerings to customers according to their properties. As an example let’s take a look at the following graph:

https://cdn.quantargo.com/assets/courses/course-machine-learning-introduction/01-basics/02-learning-techniques_files/figure-html/unnamed-chunk-4-1.png

This plot compares the <code>Annual Income</code> and the <code>Spending Score</code> of some shopping mall customers. By applying a clustering algorithm and defining five groups, we can nicely separate 5 type of customers. The key clusters in this example would be the red and blue cluster, containing the customers who tend to spend the most. However, while the blue cluster contains customers with lower income, the red cluster contains customers with relatively high income. Based on this information one could adapt the marketing strategy:

<ul>
<li>Customers in the blue cluster might be an optimal target group for attractive financing and credit options.</li>
<li>Customers in the red cluster should be kept as customers as long as possible. In particular, they should be prevented from switching to competitors. Therefore, they might be an optimal target group for loyalty based rewards and offerings.</li>
</ul>

What is Machine Learning?

Machine Learning Techniques

Supervised Learning with Regression and Classification

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