Unsupervised Machine Learning with Python

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

Python Demos

Review of Mathematical Concepts

Hierarchical Clustering

DBSCAN Clustering

K Means Clustering

Gaussian Mixture Model Clustering

Comparison of Clustering Algorithms

Dimension Reduction

Case Studies

Concluding Remarks and Thank You

Optional

69

Section 11.1: Concluding Remarks and Thank You

Video lesson

Summary of algorithms, listing of software packages for clustering and PCA, and thank you

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!

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