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Clustering And Dimensionality Reduction - Deep Dive


Master the Key Elements of Sophisticated Unsupervised Machine Learning for Data Insights and Data Processing Clustering And Dimensionality Reduction - Deep Dive

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What you'll learn

  • Master clustering and dimensionality reduction in python
  • K-means based clustering (k-means, k-modes, k-prototypes)
  • Hierarchical (agglomerative clustering)
  • Agglomerative clustering linkages: Min, Max, Average and Wald
  • Density based clustering (DBSCAN, HDBSCAN)
  • Density based clustering validation (DBCV)
  • Graph based clustering (Louvain algorithm)
  • Dimensionality reduction (PCA, UMAP)
  • Algorithms pros & cons
  • General guidelines for algorithms
  • Multiple approaches for preprocessing data for clustering & dimensionality reduction
  • Metrics for cluster quality analysis
  • Comparing data clusterings
  • Analyzing cluster characteristics
  • Using clustering and dimensionality reduction together
  • Clustering numerical, categorical and graph data
  • Applying clustering and dimensionality reduction algorithms to complex datasets
  • Necessary python prerequisites

Requirements

  • Basic python knowledge and the ability to execute python code in a jupyter notebook.
  • Knowledge of fundamental matrix operations: addition, multiplication, transpose, ...
  • Understanding basic data analysis concepts such as mean, standard deviation or median.
  • Understanding basic math functions (e.g. square root, logarithms).
  • Basic understanding of the derivatives of elementary functions and the application of the chain rule in simple scenarios. This knowledge is only required for the UMAP chapter.
Welcome to the in-depth course on unsupervised machine learning, one of the crucial aspects in the field of data science. Unsupervised machine learning is immensely important because it allows us to find hidden patterns and structures in data without the need for pre-labeled examples. This approach is not just useful, but often essential in situations where labeling data is impractical or impossible.

In this course, our focus will be on two of the most impactful techniques in unsupervised learning: clustering and dimensionality reduction. Clustering helps us to group similar data points based on their characteristics, uncovering underlying patterns in a dataset. Dimensionality reduction, on the other hand, simplifies complex data sets, making them easier to work with and understand. Mastering these techniques is key to deriving crucial insights from data which is a vital skill in the field of data science.

Clustering and dimensionality reduction have widespread applications across various sectors. In marketing, they enable deeper customer insights and market segmentation. Healthcare professionals utilize them for analyzing patient data and identifying patterns in diseases. In the financial sector, these techniques are crucial for risk analysis and detecting fraudulent activities. They are also used in bioinformatics for interpreting genetic information. In e-commerce, these methods enhance product recommendation systems, while in social network analysis, they aid in understanding community patterns. Additionally, they're applied in urban planning for traffic analysis. Beyond these, there are numerous other applications across wide range of industries.

The aim of this course is in depth analysis of unsupervised machine learning algorithms. We’ll dissect these algorithms, explaining their inner workings, best practices, and limitations. This deep understanding is achieved not just through theory, but also by implementing the algorithms ourselves.

This course is packed with practical demonstrations and various case studies that make the concepts clear and relatable. Each case study is designed to reinforce a specific part of unsupervised learning. Additionally, the course features a comprehensive case study where we apply these methods to a complex real-life dataset - using RNA profiles to group cells. This case study serves as a great example of how these techniques can be effectively used to unravel insights from intricate data.

By the end of this course, you’ll have a solid understanding of unsupervised machine learning, and you’ll be equipped with the knowledge and skills to apply these techniques in your own projects. Whether you’re a data scientist looking to expand your skill set, or a curious learner interested in the mechanics of machine learning, this course has something to offer you.

Who this course is for:

  • Beginner/aspiring data professionals wanting to learn about unsupervised machine learning.
  • Intermediate/advanced data professionals wanting to improve their knowledge of unsupervised machine learning.

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