# Unsupervised Machine Learning with Python

# 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 *

**Category : IT & Software, Other IT & Software, Unsupervised Machine Learning**

**Instructor : Satish Reddy**

**Redeem On Udemy**

## 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

## 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

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