Dimensionality reduction - Samenvattingen, Aantekeningen en Examens

This documents contains a summary of the final modules/weeks (4-7) for the course Data Mining for Business and Governance. The following topics are included in this summary: ⋅ Crisp (K-means) clustering ⋅ Fuzzy (c-means) clustering ⋅ Hierarchical clustering ⋅ Text mining ⋅ Preprocessing noisy text ⋅ Document similarity: Jaccard coefficient ⋅ Term frequency, inverse term frequency ⋅ Dimensionality reduction ⋅ Feature selection ⋅ Filtering strategy ⋅ Wrapper s...

Business Intelligence (BI) - Answer- Referring to tools and techniques for analyzing and visualizing past data Advanced Analytics (Data Science) - Answer- Refers to tools and techniques used to understand why something happened, predict future outcomes, or discover hidden patterns in large data sets Data-Driven Organizations - Answer- Make decisions that can be backed up with verifiable data Measurably more productive and profitable, can better respond to ongoing threats and opportuniti...

For data in motion. Powered by Apache NiFi. 1) real-time - add, trace, adjust; 2) integrated - common input, output, transformation; 3) secure - security rules, encryption, traceability; 4) adaptive - adapts data flow, scalable; if connection poor skinnies down data - answer-Hortonworks Data Flow (HDF) A user-driven process of searching for patterns or specific items in a data set. Data discovery applications use visual tools such as geographical maps, pivot-tables, and heat-maps to make the ...

Data preparation Time data preparation 80%, and everything else falls into about 20% GIGO garbage in, garbage out. That's a truism from computer science. The information you're going to get from your analysis is only as good as the information that you put into it Upside to In-house data It's the fastest way to start., you may actually be able to talk with the people who gathered the data in the first place. Downside to In-house data if it was an ad-hoc project, it ma...

Analyses for Data Science: Descriptive: Humans are good at finding patterns, but limited bandwidth - so we need to narrow the data. Look at the data. 1) Visualize the data - graphs, histograms, bell curve 2) Compute Univariate Descriptive Statistics: mean (average), mode (most common), median (splits into two equal halves). So ONE Value. 3) Measures of association: connection between the variables in your data. Range: high and low, Quartiles, Variance, Standard Deviation, Correlation coeffic...

The curriculum begins with Module 1: Hypothesis Testing, which lays the groundwork for statistical analysis in biomedical data. It starts with an introduction to Python, essential for the practical components of the course, followed by reviews of probability and statistics to refresh and solidify foundational knowledge. Students learn various hypothesis testing methods, including parametric and non-parametric statistics, the considerations for multiple comparisons, and resampling-based statistic...

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...

These are the notes of all 6 lectures of first week of Machine Learning Foundations course. L1 - What is Machine Leaning? l2 - Data, Models and ML Task l3 - Supervised Learning: Regression l4 - Classification l5 - Unsupervised Learning: Dimensionality Reduction l6 - Unsupervised Learning: Density Estimation

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...

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...