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Cluster Analysis & Unsupervised Machine Learning in R

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Cluster Analysis & Unsupervised Machine Learning in R
English | Size: 1.71 GB
Category: Tutorial

Cluster analysis is one of the most used techniques to segment data in a multivariate analysis. It is an example of unsupervised machine learning and has widespread application in business analytics. Cluster analysis is a method of grouping a set of objects similar to each other. Precisely, it tries to identify homogeneous groups of cases such as observations, participants, and respondents. In this post, I will take you through the two most important clustering techniques using R. These are: [Read more…]

Packt Hands-On Unsupervised Learning with TensorFlow 2.0

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Packt Hands-On Unsupervised Learning with TensorFlow 2.0 PROPER-XQZT
English | Size: 367.38 MB
Category: Tutorial


Learn unsupervised learning in Python with hands-on practical applications for each learning model

Nowadays, machine learning is becoming increasingly important to businesses. It is used to solve various business problems using supervised and unsupervised algorithms. In unsupervised learning, Artificial Intelligence systems try to categorize unlabeled and unsorted data based on the similarities and differences that exist among data. In this case, the capabilities of unsupervised learning methods to generate a model based on data make it possible to deal with complex and more difficult problems in comparison with the capabilities of supervised learning. In this course, we examine different unsupervised learning methods and solve practical problems using the TensorFlow platform. Solving examples of real-world problems using TensorFlow is more inspiring and compelling and will enhance your practical skills. [Read more…]

Unsupervised Deep Learning in Python

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Unsupervised Deep Learning in Python
English | Size: 556.36 MB
Category: CBTs


This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!

In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).
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Packt Publishing – Supervised and Unsupervised Learning with Python

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Packt Publishing – Supervised and Unsupervised Learning with Python
English | Size: 407.28 MB
Category: Tutorial


This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers. [Read more…]

Pluralsight – Building Unsupervised Learning Models with TensorFlow

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Pluralsight – Building Unsupervised Learning Models with TensorFlow
English | Size: 340.58 MB
Category: Tutorial;


Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you’ll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering. First, you’ll dive into building a k-means clustering model in TensorFlow. Next, you’ll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning. Finally, you’ll explore encodings or representation of data for dimensionality reduction of problems. By the end of this course, you’ll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques. [Read more…]

O’Reilly – Clustering and Unsupervised Learning

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O’Reilly – Clustering and Unsupervised Learning
English | Size: 163.25 MB
Category: Tutorioal


This course introduces clustering, a common technique used widely in unsupervised machine learning. The course begins by defining what clustering means through graphical explanations, and describes the common applications of clustering. Next, it explores k-means clustering in detail, including the concepts of distance functions and k-modes; illustrates hierarchical clustering through visual examples of dendrograms, and discusses different types of clustering algorithms. The course ends with a comparison of the performance of different algorithms. An understanding of basic algebra is required and some knowledge of linear algebra will be helpful. [Read more…]