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Unsupervised Machine Learning with Python

Name: Unsupervised Machine Learning with Python
Rating: 4.2 (34 reviews)

Unsupervised Machine Learning Clustering and Dimension Reduction Algorithms with Python Implementation and Applications

Created bySatish Reddy

Last updated 12/2022

English

What you'll learn

Clustering Algorithms: Hierarchical, DBSCAN, K Means, Gaussian Mixture Model
Dimensions Reduction: Principal Component Analysis (PCA)
Implementation of clustering algorithms and principal component analysis in Python
Applications of clustering and PCA using real world data

Course content

12 sections • 70 lectures • 9h 37m total length

Section 1.1: Introduction8:23
Introduction to Unsupervised Machine Learning with Python Course
Section 1.2: About this Course2:57
Information about course audience, prerequisites, and how to get most from course
Section 1.3: Course Resources and Set Up14:10
Information about course Github site and resources, installing Anaconda distribution if required, installing python packages, and testing set up

Section 2.0: Python Demos1:42
This brief section gives an overview of the demos in Section 2
Section 2.1: Numpy Basic Demos23:02
Jupyter notebook demo of basic numpy functionality used in the course
Section 2.1: Exercises0:37
Exercises for Section 2.1
Section 2.2: Numpy Matrix Operations Demo9:41
Jupyter notebook demos of numpy matrix operations functionality used in the course
Section 2.2: Exercises0:37
Exercises for Section 2.2
Section 2.3: Matplotlib Basic Demo9:45
Jupyter notebook demos of basic matplotlib plotting functionality used in this course
Section 2.3: Exercises0:37
Exercises for Section 2.3
Section 2.4: Matplotlib Cluster Plot and Animation Demo20:39
Jupyter notebook demos of matplotlib colormesh, scatter plot, and animation functionality used in this course
Section 2.4: Exercises0:37
Exercises for Section 2.4
Section 2.5: Pandas Demo6:36
Jupyter notebook demo of basic pandas functionality for reading data from csv files
Section 2.5: Exercises0:37
Exercises for Section 2.5
Section 2.6: Sklearn Datasets Demo5:47
Jupyter notebook demo of generating dataset using sklearn datasets functionality

Section 3.0: Review of Mathematical Concepts1:47
Review of what is covered in Section 3
Section 3.1: What is Data in Unsupervised Learning15:33
Description of data for Unsupervised Machine Learning and demo of using sklearn and wordcloud to process and visualize text. Students will be able set up datasets for their applications and be able to use basic sklearn functionality to convert text to feature matrices.
Section 3.1: Exercises0:37
Exercises for Section 3.1
Section 3.2: Computational Complexity13:35
Review of computational complexity and relevance to algorithms with demos using numpy package. Student will be able to estimate complexity power using numpy.
Section 3.2: Exercises0:37
Exercises for Section 3.2
Section 3.3: Distance Measures9:51
Description of distance measures and now to compute them using the numpy package functionality. Students will be able to compute distances between vectors using numpy.
Section 3.3: Exercises0:37
Exercises for Section 3.3
Section 3.4: Singular Value Decomposition15:26
Description of singular value decomposition and demo of how to compute svd using numpy. Students will understand what the singular value decomposition is, how to compute it, and how it will be used in the course.
Section 3.4: Exercises0:37
Exercises for Section 3.4
Section 3.5: Mean, Variance, and Covariance12:26
Review of mean, variance, and covariance, which are used in various unsupervised machine learning algorithms. Demo shows how to use numpy functions to compute mean, variance, and covariance.
Section 3.5: Exercises0:37
Exercises for Section 3.5

Section 4.1: Hierarchical Clustering Algorithm9:22
Description of Hierarchical Clustering Algorithm. Students will be able to understand algorithm, its complexity, and strengths and weaknesses.
Section 4.1: Exercises0:37
Exercises for Section 4.1
Section 4.2: Hierarchical Clustering Code Design14:33
Description of course code design for the Hierarchical Clustering Algorithm. Given this code design, students will be able to implement the algorithm using Python.
Section 4.3: Hierarchical Clustering Code Walkthrough21:17
Walkthrough of course Hierarchical Clustering code. Students will be able to understand and use the course Hierarchical Clustering code.
Section 4.3: Exercises0:37
Exercises for Section 4.3

Section 6.1: K Means Algorithm16:39
Description of K Means Clustering Algorithm. Students will be able to understand algorithm, its complexity, and strengths and weaknesses.
Section 6.1: Exercises0:37
Exercises for Section 6.1
Section 6.2: K Means Code Design12:59
Review of course K Means code design
Section 6.3: K Means Code Walkthrough17:33
Walkthrough of course K Means code
Section 6.3: Exercises0:37
Exercises for Section 6.3

Section 7.1: Normal Distribution Probability Density Function17:00
Description of the Normal Distribution Probability Density Function for one dimension and multiple dimensions.
Section 7.1: Exercises0:37
Exercises for Section 7.1
Section 7.2: Gaussian Mixture Model Algorithm17:50
Description of Gaussian Mixture Model Clustering Algorithm. Students will be able to understand algorithm, its complexity, and strengths and weaknesses.
Section 7.2: Exercises0:37
Exercises for Section 7.2
Section 7.3: Gaussian Mixture Model Code Design12:31
Review of course Gaussian Mixture Model code design
Section 7.4: Gaussian Mixture Model Code Walkthrough24:00
Walkthrough of course Gaussian Mixture Model code
Section 7.4: Exercises0:37
Exercises for Section 7.4

Section 9.0: Dimension Reduction Overview3:22
Overview of the dimension reduction algorithms
Section 9.1: Principal Component Analysis Algorithm22:30
Description of the Principal Component Analysis Algorithm and Jupyter Notebook demo.
Section 9.1: Exercises0:37
Exercises for Section 9.1
Section 9.2: Principal Component Analysis Code Design3:00
Review of design for Principal Component Analysis code.
Section 9.3: Principal Component Analysis Code Walkthrough9:33
Walkthrough of Principal Component Analysis code.
Section 9.3: Exercises0:37
Exercises for Section 9.3
Section 9.4: PCA Applied to MNIST Digits Dataset18:37
Application of Principal Component Analysis to MNIST Digits Dataset.
Section 9.4: Exercises0:37
Exercises for Section 9.4
Section 9.5: Autoencoders7:51
Description of how Autoencoders can be used for dimension reduction.
Section 9.6: Autoencoder Demo (Optional)13:32
This optional section has demo on using autoencoders for dimension reduction.

Section 10.1: Clustering Quality Metrics14:30
Description of the Purity and Bar Chart metrics for measuring quality of clustering plus demo and code walkthrough of Python implementation
Section 10.2: Clustering for Iris Flower Dataset21:17
Discussion of using clustering algorithms and PCA to reduce dimension to find clusters in the Iris Flower Dataset
Section 10.2: Exercises0:37
Exercises for Section 10.2
Section 10.3: Clustering for MNIST Digits Dataset17:04
Discuss of using clustering algorithms and PCA to reduce dimension to find clusters in the MNIST Digits Dataset
Exercises for Section 10.30:37
Exercises for Section 10.3
Section 10.4: Clustering for BBC Text Dataset19:57
Discussion of using clustering algorithms and PCA to reduce dimension to group articles for the BBC Text dataset
Section 10.4: Exercises0:37
Exercises for Section 10.4

Requirements

Basic knowledge of Linear Algebra including vectors, matrices, transpose, matrix multiplications, linear spaces
Basic knowledge of Probability and Statistics including mean, covariance, and normal distributions
Ability to program in Python 3
Ability to run Python 3 programs on local machine in Jupyter notebooks and command window

Description

Course Outcome:

After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets.

Course Topics and Approach:

Unsupervised Machine Learning involves finding patterns in datasets. The core of this course involves study of the following algorithms:

Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model

Dimension Reduction: Principal Component Analysis

Unlike many other courses, this course:

Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition.
Has a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorization
Has questions (programming and theory) and solutions that allow learners to get practice with the course material

The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).

Course Audience:

This course is designed for:

Scientists, engineers, and programmers and others interested in machine learning/data science
No prior experience with machine learning is needed
Students should have knowledge of
- Basic linear algebra (vectors, transpose, matrices, matrix multiplication, inverses, determinants, linear spaces)
- Basic probability and statistics (mean, covariance matrices, normal distributions)
- Python 3 programming

Students should have a Python installation, such as the Anaconda platform, on their machine with the ability to run programs in the command window and in Jupyter Notebooks

Teaching Style and Resources:

Course includes many examples with plots and animations used to help students get a better understanding of the material
Course has many exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practice
All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

2021.08.28 Update:

Section 9.5: added Autoencoder example
Section 9.6: added this new section with an Autoencoder Demo

2021.11.02 Update:

Sections 2.3, 2.4, 3.4, 4.3: updates so codes can run in more recent versions of python and matplotlib and updates to presentations to point out the changes

2021.11.02 Update:

Added English captions to the course videos

Who this course is for:

Scientists, engineers and programmers interested in data science/machine learning

Unsupervised Machine Learning with Python

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 26min

Python Demos12 lectures • 1hr 20min

Review of Mathematical Concepts11 lectures • 1hr 12min

Hierarchical Clustering5 lectures • 46min

DBSCAN Clustering5 lectures • 36min

K Means Clustering5 lectures • 48min

Gaussian Mixture Model Clustering7 lectures • 1hr 13min

Comparison of Clustering Algorithms3 lectures • 34min

Dimension Reduction10 lectures • 1hr 20min

Case Studies7 lectures • 1hr 15min

Requirements

Description

Who this course is for: