Dimensionality reduction - Samenvattingen, Aantekeningen en Examens

Unlock the power of unsupervised learning with this in-depth course designed to guide you through the techniques used to uncover hidden patterns and structures in data. Unlike supervised learning, which relies on labeled data, unsupervised learning focuses on exploring data without predefined labels, making it essential for discovering insights and making data-driven decisions. This course covers fundamental unsupervised learning methods, including clustering, dimensionality reduction, and as...

This documents contains a summary of the first three modules/weeks for the course Data Mining for Business and Governance. The following topics are included in this summary: ⋅ What is data mining? ⋅ What are the related disciplines? ⋅ What are the applications? ⋅ What is big data? ⋅ Supervised and unsupervised learning ⋅ Examples of supervised and unsupervised learning ⋅ Workflow of supervised learning ⋅ Descriptive analysis: data visualization, exploring data dist...

As a 1st Class Machine Learning student, I've navigated through the fundamental concepts and techniques in our Machine Learning course at King's College London. The course begins with an "Introduction to Machine Learning," where we cover the basics of algorithms learning from data to make predictions without explicit programming. Key areas include "Supervised Learning" such as "Regression" and "Classification," where models learn from labeled data to predict continuous or categorical o...

DATA SOURCES This data analytical process starts with the data. In the recent decade, there has been a tremendous increase in data sources and availability1 . As with other professionals, actuaries are faced with large amounts of available information almost instantaneously. The data volume and influx are not the only challenge for practitioners. A major difficulty is that the data, which sometimes must be studied in real time, comes from diverse sources, which lead to various data ty...

This set of codes and calculations pertains to an analysis of hospital stay durations. The data was initially saved in a CSV file and then imported into the R programming environment. The dataset reveals the number of days each patient spent in the hospital. A histogram was generated to visualize the distribution, showing that most patients had relatively short stays. Summary statistics were computed, including measures like mean, median, standard deviation, quartiles, minimum, and maximum value...

Introduction: In my document, I embark on a captivating journey through the world of CSC 823, an advanced-level course in data mining and machine learning. As I delve into the intricacies of this fascinating subject, I am eager to expand my knowledge and gain valuable insights into the advanced concepts and techniques that drive modern data analysis. Exploring Advanced Machine Learning Algorithms: In CSC 823, I explore a plethora of advanced machine learning algorithms, delving deep into th...

Handwritten notes related to the Data Analytics (ITNPBD6) course at the University of Stirling. I obtained a high 1st in the module. The notes cover all the basics Machine Learning principles and techniques. The outline is as follows: Chapter 1: Introduction to Data Analytics Chapter 2: Model Accuracy Chapter 3: Classification Chapter 4: Regression Chapter 5: Clustering Chapter 6: Neural Networks and Deep Learning Chapter 7: Metaheuristics, hyper-parameters and dimensionality reduction...

Multivariate Analysis - -deals with the statistical analysis of observations where there are multiple responses on each observational unit. -Multivariate Analysis - -This essentially models reality where each situation, product, or decision involves more than a single variable. -quantum of data - -Despite the ____________________ available, the ability to obtain a clear picture of what is going on and make intelligent decisions is a challenge. -process the information in a meaningful fa...

The document provides an overview of several important concepts in data mining and statistical analysis. It begins by defining data mining as the process of extracting useful patterns and knowledge from large amounts of data. It then describes data integration and transformation as important steps in preparing data for analysis, including identifying data sources, cleaning and transforming the data, consolidating it into a single dataset, and verifying and validating the data. The document th...

Define machine learning Explain the machine learning approach List relevant terminologies that help you understand a dataset Discuss features of supervised and unsupervised learning models Explain algorithms, such as regression, classification, clustering, and dimensionality reduction