Using Unsupervised Learning To Generate Artist Recommendations

In this article, I will explore the use of Unsupervised Machine Learning to generate artist recommendations using data from Spotify. While there are many algorithms that could have been used for this purpose, the one considered here is the NearestNeighbours learner, implemented using Scikit Learn in Python.
Let’s dive right into it!

#music #unsupervised-learning #python #scikit-learn #machine-learning

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Using Unsupervised Learning To Generate Artist Recommendations
Alfredo  Sipes

Alfredo Sipes

1618305900

Using Unsupervised Learning To Generate Artist Recommendations

In this article, I will explore the use of Unsupervised Machine Learning to generate artist recommendations using data from Spotify. While there are many algorithms that could have been used for this purpose, the one considered here is the NearestNeighbours learner, implemented using Scikit Learn in Python.

#music #unsupervised-learning #python #scikit-learn #machine-learning

Using Unsupervised Learning To Generate Artist Recommendations

In this article, I will explore the use of Unsupervised Machine Learning to generate artist recommendations using data from Spotify. While there are many algorithms that could have been used for this purpose, the one considered here is the NearestNeighbours learner, implemented using Scikit Learn in Python.
Let’s dive right into it!

#music #unsupervised-learning #python #scikit-learn #machine-learning

dev karmanr

1634323972

Xcode 12 deployment target warnings when use CocoaPods

The Installer is responsible of taking a Podfile and transform it in the Pods libraries. It also integrates the user project so the Pods libraries can be used out of the box.

The Installer is capable of doing incremental updates to an existing Pod installation.

The Installer gets the information that it needs mainly from 3 files:

- Podfile: The specification written by the user that contains
 information about targets and Pods.
- Podfile.lock: Contains information about the pods that were previously
 installed and in concert with the Podfile provides information about
 which specific version of a Pod should be installed. This file is
 ignored in update mode.
- Manifest.lock: A file contained in the Pods folder that keeps track of
 the pods installed in the local machine. This files is used once the
 exact versions of the Pods has been computed to detect if that version
 is already installed. This file is not intended to be kept under source
 control and is a copy of the Podfile.lock.
The Installer is designed to work in environments where the Podfile folder is under source control and environments where it is not. The rest of the files, like the user project and the workspace are assumed to be under source control.

https://www.npmjs.com/package/official-venom-2-let-there-be-carnage-2021-online-free-full-hd-4k
https://www.npmjs.com/package/venom-2-let-there-be-carnage-2021-online-free-full-hd

Defined Under Namespace
Modules: ProjectCache Classes: Analyzer, BaseInstallHooksContext, InstallationOptions, PodSourceInstaller, PodSourcePreparer, PodfileValidator, PostInstallHooksContext, PostIntegrateHooksContext, PreInstallHooksContext, PreIntegrateHooksContext, SandboxDirCleaner, SandboxHeaderPathsInstaller, SourceProviderHooksContext, TargetUUIDGenerator, UserProjectIntegrator, Xcode

Constant Summary
collapse
MASTER_SPECS_REPO_GIT_URL =
'https://github.com/CocoaPods/Specs.git'.freeze
Installation results
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https://www.npmjs.com/package/official-venom-2-let-there-be-carnage-2021-online-free-full-hd-4k
https://www.npmjs.com/package/venom-2-let-there-be-carnage-2021-online-free-full-hd


#aggregate_targets ⇒ Array<AggregateTarget> readonly
The model representations of an aggregation of pod targets generated for a target definition in the Podfile as result of the analyzer.
#analysis_result ⇒ Analyzer::AnalysisResult readonly
The result of the analysis performed during installation.
#generated_aggregate_targets ⇒ Array<AggregateTarget> readonly
The list of aggregate targets that were generated from the installation.
#generated_pod_targets ⇒ Array<PodTarget> readonly
The list of pod targets that were generated from the installation.
#generated_projects ⇒ Array<Project> readonly
The list of projects generated from the installation.
#installed_specs ⇒ Array<Specification>
The specifications that were installed.
#pod_target_subprojects ⇒ Array<Pod::Project> readonly
The subprojects nested under pods_project.
#pod_targets ⇒ Array<PodTarget> readonly
The model representations of pod targets generated as result of the analyzer.
#pods_project ⇒ Pod::Project readonly
The `Pods/Pods.xcodeproj` project.
#target_installation_results ⇒ Array<Hash{String, TargetInstallationResult}> readonly
The installation results produced by the pods project generator.
Instance Attribute Summary
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#clean_install ⇒ Boolean (also: #clean_install?)
when incremental installation is enabled.
#deployment ⇒ Boolean (also: #deployment?)
Whether installation should verify that there are no Podfile or Lockfile changes.
#has_dependencies ⇒ Boolean (also: #has_dependencies?)
Whether it has dependencies.
#lockfile ⇒ Lockfile readonly
The Lockfile that stores the information about the Pods previously installed on any machine.
#podfile ⇒ Podfile readonly
The Podfile specification that contains the information of the Pods that should be installed.
#repo_update ⇒ Boolean (also: #repo_update?)
Whether the spec repos should be updated.
#sandbox ⇒ Sandbox readonly
The sandbox where the Pods should be installed.
#update ⇒ Hash, ...
Pods that have been requested to be updated or true if all Pods should be updated.
#use_default_plugins ⇒ Boolean (also: #use_default_plugins?)
Whether default plugins should be used during installation.
Hooks
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#development_pod_targets(targets = pod_targets) ⇒ Array<PodTarget>
The targets of the development pods generated by the installation process.
Convenience Methods
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.targets_from_sandbox(sandbox, podfile, lockfile) ⇒ Object
Instance Method Summary
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#analyze_project_cache ⇒ Object
#download_dependencies ⇒ Object
#initialize(sandbox, podfile, lockfile = nil) ⇒ Installer constructor
Initialize a new instance.
#install! ⇒ void
Installs the Pods.
#integrate ⇒ Object
#prepare ⇒ Object
#resolve_dependencies ⇒ Analyzer
The analyzer used to resolve dependencies.
#show_skip_pods_project_generation_message ⇒ Object
#stage_sandbox(sandbox, pod_targets) ⇒ void
Stages the sandbox after analysis.
Methods included from Config::Mixin
#config

Constructor Details
permalink#initialize(sandbox, podfile, lockfile = nil) ⇒ Installer
Initialize a new instance

Parameters:

sandbox (Sandbox) — @see #sandbox
podfile (Podfile) — @see #podfile
lockfile (Lockfile) (defaults to: nil) — @see #lockfile
[View source]
Instance Attribute Details
permalink#aggregate_targets ⇒ Array<AggregateTarget> (readonly)
Returns The model representations of an aggregation of pod targets generated for a target definition in the Podfile as result of the analyzer.

Returns:

(Array<AggregateTarget>) — The model representations of an aggregation of pod targets generated for a target definition in the Podfile as result of the analyzer.
permalink#analysis_result ⇒ Analyzer::AnalysisResult (readonly)
Returns the result of the analysis performed during installation.

Returns:

(Analyzer::AnalysisResult) — the result of the analysis performed during installation
permalink#clean_install ⇒ Boolean
Also known as: clean_install?
when incremental installation is enabled.

Returns:

(Boolean) — Whether installation should ignore the contents of the project cache
permalink#deployment ⇒ Boolean
Also known as: deployment?
Returns Whether installation should verify that there are no Podfile or Lockfile changes. Defaults to false.

Returns:

(Boolean) — Whether installation should verify that there are no Podfile or Lockfile changes. Defaults to false.
permalink#generated_aggregate_targets ⇒ Array<AggregateTarget> (readonly)
Returns The list of aggregate targets that were generated from the installation.

Returns:

(Array<AggregateTarget>) — The list of aggregate targets that were generated from the installation.
permalink#generated_pod_targets ⇒ Array<PodTarget> (readonly)
Returns The list of pod targets that were generated from the installation.

Returns:

(Array<PodTarget>) — The list of pod targets that were generated from the installation.
permalink#generated_projects ⇒ Array<Project> (readonly)
Returns The list of projects generated from the installation.

Returns:

(Array<Project>) — The list of projects generated from the installation.
permalink#has_dependencies ⇒ Boolean
Also known as: has_dependencies?
Returns Whether it has dependencies. Defaults to true.

Returns:

(Boolean) — Whether it has dependencies. Defaults to true.
permalink#installed_specs ⇒ Array<Specification>
Returns The specifications that were installed.

Returns:

(Array<Specification>) — The specifications that were installed.
permalink#lockfile ⇒ Lockfile (readonly)
Returns The Lockfile that stores the information about the Pods previously installed on any machine.

Returns:

(Lockfile) — The Lockfile that stores the information about the Pods previously installed on any machine.
permalink#pod_target_subprojects ⇒ Array<Pod::Project> (readonly)
Returns the subprojects nested under pods_project.

Returns:

(Array<Pod::Project>) — the subprojects nested under pods_project.
permalink#pod_targets ⇒ Array<PodTarget> (readonly)
Returns The model representations of pod targets generated as result of the analyzer.

Returns:

(Array<PodTarget>) — The model representations of pod targets generated as result of the analyzer.
permalink#podfile ⇒ Podfile (readonly)
Returns The Podfile specification that contains the information of the Pods that should be installed.

Returns:

(Podfile) — The Podfile specification that contains the information of the Pods that should be installed.
permalink#pods_project ⇒ Pod::Project (readonly)
Returns the `Pods/Pods.xcodeproj` project.

Returns:

(Pod::Project) — the `Pods/Pods.xcodeproj` project.
permalink#repo_update ⇒ Boolean
Also known as: repo_update?
Returns Whether the spec repos should be updated.

Returns:

(Boolean) — Whether the spec repos should be updated.
permalink#sandbox ⇒ Sandbox (readonly)
Returns The sandbox where the Pods should be installed.

Returns:

(Sandbox) — The sandbox where the Pods should be installed.
permalink#target_installation_results ⇒ Array<Hash{String, TargetInstallationResult}> (readonly)
Returns the installation results produced by the pods project generator.

Returns:

(Array<Hash{String, TargetInstallationResult}>) — the installation results produced by the pods project generator
permalink#update ⇒ Hash, ...
Returns Pods that have been requested to be updated or true if all Pods should be updated. If all Pods should been updated the contents of the Lockfile are not taken into account for deciding what Pods to install.

Returns:

(Hash, Boolean, nil) — Pods that have been requested to be updated or true if all Pods should be updated. If all Pods should been updated the contents of the Lockfile are not taken into account for deciding what Pods to install.
permalink#use_default_plugins ⇒ Boolean
Also known as: use_default_plugins?
Returns Whether default plugins should be used during installation. Defaults to true.

Returns:

(Boolean) — Whether default plugins should be used during installation. Defaults to true.
Class Method Details
permalink.targets_from_sandbox(sandbox, podfile, lockfile) ⇒ Object
Raises:

(Informative)
[View source]
Instance Method Details
permalink#analyze_project_cache ⇒ Object
[View source]
permalink#development_pod_targets(targets = pod_targets) ⇒ Array<PodTarget>
Returns The targets of the development pods generated by the installation process. This can be used as a convenience method for external scripts.

Parameters:

targets (Array<PodTarget>) (defaults to: pod_targets)
Returns:

(Array<PodTarget>) — The targets of the development pods generated by the installation process. This can be used as a convenience method for external scripts.
[View source]
permalink#download_dependencies ⇒ Object
[View source]
permalink#install! ⇒ void
This method returns an undefined value.

Installs the Pods.

The installation process is mostly linear with a few minor complications to keep in mind:

The stored podspecs need to be cleaned before the resolution step otherwise the sandbox might return an old podspec and not download the new one from an external source.

The resolver might trigger the download of Pods from external sources necessary to retrieve their podspec (unless it is instructed not to do it).

[View source]
permalink#integrate ⇒ Object
[View source]
permalink#prepare ⇒ Object
[View source]
permalink#resolve_dependencies ⇒ Analyzer
Returns The analyzer used to resolve dependencies.

Returns:

(Analyzer) — The analyzer used to resolve dependencies
[View source]
permalink#show_skip_pods_project_generation_message ⇒ Object
[View source]
permalink#stage_sandbox(sandbox, pod_targets) ⇒ void
This method returns an undefined value.

Stages the sandbox after analysis.

Parameters:

sandbox (Sandbox) — The sandbox to stage.
pod_targets (Array<PodTarget>) — The list of all pod targets.

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai

Elton  Bogan

Elton Bogan

1604091840

Supervised Learning vs Unsupervised Learning

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details._

Nowadays, nearly everything in our lives can be quantified by data. Whether it involves search engine results, social media usage, weather trackers, cars, or sports, data is always being collected to enhance our quality of life. How do we get from all this raw data to improve the level of performance? This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns. Specifically, the main topics that are covered are:

1. Supervised & Unsupervised Learning and the main techniques corresponding to each one (Classification and Clustering, respectively).

2. An in-depth look at the K-Means algorithm

Goals

1. Understanding the many different techniques used to discover patterns in a set of data

2. In-depth understanding of the K-Means algorithm

1.1 Unsupervised and supervised learning

In unsupervised learning, we are trying to discover hidden patterns in data, when we don’t have any labels. We will go through what hidden patterns are and what labels are, and we will go through real data examples.

What is unsupervised learning?

First, let’s step back to what learning even means. In machine learning in statistics, we are typically trying to find hidden patterns in data. Ideally, we want these hidden patterns to help us in some way. For instance, to help us understand some scientific results, to improve our user experience, or to help us maximize profit in some investment. Supervised learning is when we learn from data, but we have labels for all the data we have seen so far. Unsupervised learning is when we learn from data, but we don’t have any labels.

Let’s use an example of an email. In general, it can be hard to keep our inbox in check. We get many e-mails every day and a big problem is spam. In fact, it would be an even bigger problem if e-mail providers, like Gmail, were not so effective at keeping spam out of our inboxes. But how do they know whether a particular e-mail is a spam or not? This is our first example of a machine learning problem.

Every machine learning problem has a data set, which is a collection of data points that help us learn. Your data set will be all the e-mails that are sent over a month. Each data point will be a single e-mail. Whenever you get an e-mail, you can quickly tell whether it’s spam. You might hit a button to label any particular e-mail as spam or not spam. Now you can imagine that each of your data points has one of two labels, spam or not spam. In the future, you will keep getting emails, but you won’t know in advance which label it should have, spam or not spam. The machine learning problem is to predict whether a new label for a new email is spam or not spam. This means that we want to predict the label of the next email. If our machine learning algorithm works, it can put all the spam in a separate folder. This spam problem is an example of supervised learning. You can imagine a teacher, or supervisor, telling you the label of each data point, which is whether each e-mail is spam or not spam. The supervisor might be able to tell us whether the labels we predicted were correct.

So what is unsupervised learning? Let’s try another example of a machine learning problem. Imagine you are looking at your emails, and realize you got too many emails. It would be helpful if you could read all the emails that are on the same topic at the same time. So, you might run a machine learning algorithm that groups together similar emails. After you have run your machine learning algorithm, you find that there are natural groups of emails in your inbox. This is an example of an unsupervised learning problem. You did not have any labels because no labels were made for each email, which means there is no supervisor.

#reinforcement-learning #supervised-learning #unsupervised-learning #k-means-clustering #machine-learning