HOT
blank

Clustering And Dimensionality Reduction – Deep Dive

Development

Rating4.9
Enroll Now

Clustering And Dimensionality Reduction – Deep Dive, available at $54.99, has an average rating of 4.85, with 203 lectures, based on 57 reviews, and has 861 subscribers.

You will learn about Unsupervised learning & data mining in python Cluster analysis and dimensionality reduction 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) PCA dimensionality reduction UMAP dimensionality reduction Algorithms pros & cons General guidelines for algorithms Multiple approaches for preprocessing data for cluster analysis & 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 This course is ideal for individuals who are Beginner/aspiring data professionals wanting to learn about unsupervised learning & data mining. or Intermediate/advanced data professionals wanting to improve their knowledge of unsupervised learning & data mining. It is particularly useful for Beginner/aspiring data professionals wanting to learn about unsupervised learning & data mining. or Intermediate/advanced data professionals wanting to improve their knowledge of unsupervised learning & data mining.

Enroll now: Clustering And Dimensionality Reduction – Deep Dive

Summary

Title: Clustering And Dimensionality Reduction – Deep Dive

Price: $54.99

Average Rating: 4.85

Number of Lectures: 203

Number of Published Lectures: 203

Number of Curriculum Items: 203

Number of Published Curriculum Objects: 203

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • Unsupervised learning & data mining in python
  • Cluster analysis and dimensionality reduction
  • 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)
  • PCA dimensionality reduction
  • UMAP dimensionality reduction
  • Algorithms pros & cons
  • General guidelines for algorithms
  • Multiple approaches for preprocessing data for cluster analysis & 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

Who Should Attend

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

Target Audiences

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

Welcome to the in-depth course on unsupervised machine learning, one of the crucial aspects in the field of data science. Unsupervised learning is immensely important because it allows us to find hidden patterns and structures in data (a process also known as data mining) 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: cluster analysis 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.

Cluster analysis and dimensionality reduction have widespread applications in data mining 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 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 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Unsupervised machine learning

Lecture 3: Course contents

Lecture 4: Learning tips

Lecture 5: Jupyter notebook overview

Chapter 2: Python basics

Lecture 1: Chapter agenda

Lecture 2: —- Part 1 – basic python data types —-

Lecture 3: Numerical data types

Lecture 4: Boolean data type

Lecture 5: String data type

Lecture 6: Python lists – part 1

Lecture 7: Python lists – part 2

Lecture 8: Sets and tuples

Lecture 9: Dictionaries and "None"

Lecture 10: Truthiness

Lecture 11: —- Part 2 – basic python functionalities —-

Lecture 12: Copying in python (deep & shallow copying)

Lecture 13: Unpacking iterable data types

Lecture 14: Python functions, *args and **kwargs

Lecture 15: Python functions – demo

Lecture 16: Lambda functions, scopes and decorators

Lecture 17: Python classes

Lecture 18: Python classes – demo

Lecture 19: While loops and loop control statements

Lecture 20: Comprehensions in python

Lecture 21: Chapter summary

Chapter 3: Python data science libraries

Lecture 1: Chapter agenda

Lecture 2: —- Part 1 – Numpy —-

Lecture 3: Indexing & slicing in numpy

Lecture 4: Indexing & slicing in numpy – demo

Lecture 5: Operations on single numpy arrays

Lecture 6: Operations on single numpy arrays – demo

Lecture 7: Operations between numpy arrays & broadcasting

Lecture 8: Operations between numpy arrays & broadcasting – demo

Lecture 9: Merging numpy arrays

Lecture 10: Data types in numpy

Lecture 11: Matrix operations in numpy

Lecture 12: —- Part 2 – pandas —-

Lecture 13: Pandas indexing and slicing

Lecture 14: Creating data frames

Lecture 15: Pandas indexing and slicing – demo

Lecture 16: Operations on single data frames/series

Lecture 17: Operations on single data frames/series – demo

Lecture 18: Operations between data frames/series

Lecture 19: Operations between data frames/series – demo

Lecture 20: Other useful pandas functionalities

Lecture 21: Pandas data types

Lecture 22: Pandas data types – demo

Lecture 23: Pandas group by statement

Lecture 24: Pandas group by statement – demo

Lecture 25: —- Part 3 – Data visualisations —-

Lecture 26: Matplotlib basics

Lecture 27: Seaborn basics

Lecture 28: Chapter summary

Chapter 4: K-means clustering – part 1

Lecture 1: Chapter agenda

Lecture 2: K-means clustering algorithm

Lecture 3: Avoiding suboptimal solutions

Lecture 4: Demo: Implementing k-means clustering algorithm from scratch – part 1

Lecture 5: Demo: Implementing k-means clustering algorithm from scratch – part 2

Lecture 6: K-means in sklearn

Lecture 7: Data preprocessing for K-means

Lecture 8: Adjusted rand index

Lecture 9: Demo: Data preprocessing, k-means & adjusted rand index

Lecture 10: Inferring number of clusters with inertia knee method

Lecture 11: Silhouette scores (inferring number of clusters & analyzing cluster quality)

Lecture 12: Demo: inertia knee method & silhouette scores – part 1

Lecture 13: Demo: inertia knee method & silhouette scores – part 2

Lecture 14: Chapter summary

Chapter 5: Principal component analysis (PCA)

Lecture 1: Chapter agenda

Lecture 2: Feature cordinate systems

Lecture 3: PCA and feature corinate systems

Lecture 4: Intuition behind Principal component analysis

Lecture 5: PCA as a linear transformation of the data – introduction

Lecture 6: Linear transformations

Lecture 7: Eigenvectors and eigenvalues

Lecture 8: Change of basis

Lecture 9: Variance and covariance

Lecture 10: PCA from eigendecomposition perspective

Lecture 11: Principal component analysis for dimensionality reduction

Lecture 12: Demo: Performing PCA by using eigendecomposition

Lecture 13: Principal component analysis in sklearn

Lecture 14: Demo: PCA in sklearn (artificial data)

Lecture 15: Demo: PCA in sklearn (real data)

Lecture 16: Guidelines for choosing number of principal component

Lecture 17: Demo : Choosing number of principal components

Lecture 18: Chapter summary

Chapter 6: Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP)

Lecture 1: Chapter agenda

Lecture 2: Graph theory basics

Lecture 3: UMAP introduction

Lecture 4: Fuzzy set basics

Lecture 5: Gradient descent & stochastic gradient descent

Lecture 6: Sparse matrices with SciPy

Lecture 7: UMAP theory – part 1

Lecture 8: Demo: Implementing UMAP from scratch – part 1

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 7 votes
  • 5 stars: 48 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Top 10 Ethical AI Courses to Learn in November 2024

Top 10 Game Development Courses to Learn in November 2024

Top 10 Software Testing Courses to Learn in November 2024