Cluster Analysis and Unsupervised Machine Learning in Python Online Course

Cluster Analysis and Unsupervised Machine Learning in Python Online Course

Unlock the power of unsupervised machine learning with this hands-on course in cluster analysis using Python. Starting with K-Means, you’ll progress through theory and coding exercises before exploring hierarchical clustering with agglomerative methods, dendrogram interpretation, and real-world case studies like evolutionary and tweet analysis. The course concludes with Gaussian Mixture Models (GMMs), covering the Expectation-Maximization algorithm, comparisons with K-Means, and practical challenges. Alongside Python setup and learning strategies, you’ll gain the skills to confidently apply clustering techniques to complex datasets and advance your expertise in unsupervised learning.

Who should take this Course?

The Cluster Analysis and Unsupervised Machine Learning in Python Online Course is ideal for data scientists, analysts, machine learning enthusiasts, and Python programmers who want to gain practical skills in uncovering hidden patterns and structures within data. It is also well-suited for students, researchers, and professionals in fields like business, finance, marketing, and healthcare who wish to apply clustering techniques and unsupervised learning methods to real-world datasets for smarter decision-making.

What you will learn

• Implement clustering algorithms in Python.
• Analyze the strengths and weaknesses of different clustering techniques.
• Apply clustering methods to real-world datasets.
• Understand the theoretical foundations of K-Means, Hierarchical Clustering, and GMMs.
• Evaluate clustering results using metrics like purity and Davies-Bouldin Index
• Visualize the steps and results of clustering algorithms for deeper insights

Course Outline 

Welcome

• Introduction
• Course Outline
• Special Offer

Getting Set Up

• Where to get the code

Unsupervised Learning

• What is unsupervised learning used for?
• Why Use Clustering?

K-Means Clustering

• An Easy Introduction to K-Means Clustering
• Hard K-Means: Exercise Prompt 1
• Hard K-Means: Exercise 1 Solution
• Hard K-Means: Exercise Prompt 2
• Hard K-Means: Exercise 2 Solution
• Hard K-Means: Exercise Prompt 3
• Hard K-Means: Exercise 3 Solution
• Hard K-Means Objective: Theory
• Hard K-Means Objective: Code
• Visual Walkthrough of the K-Means Clustering Algorithm (Legacy)
• Soft K-Means
• The K-Means Objective Function
• Soft K-Means in Python Code
• How to Pace Yourself
• Visualizing Each Step of K-Means
• Examples of where K-Means can fail
• Disadvantages of K-Means Clustering
• How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
• Using K-Means on Real Data: MNIST
• One Way to Choose K
• K-Means Application: Finding Clusters of Related Words
• Clustering for NLP and Computer Vision: Real-World Applications
• Suggestion Box

Hierarchical Clustering

• Visual Walkthrough of Agglomerative Hierarchical Clustering
• Agglomerative Clustering Options
• Using Hierarchical Clustering in Python and Interpreting the Dendrogram
• Application: Evolution
• Application: Donald Trump vs. Hillary Clinton Tweets

Gaussian Mixture Models (GMMs)

• Gaussian Mixture Model (GMM) Algorithm
• Write a Gaussian Mixture Model in Python Code
• Practical Issues with GMM
• Comparison between GMM and K-Means
• Kernel Density Estimation
• GMM vs Bayes Classifier (pt 1)
• GMM vs Bayes Classifier (pt 2)
• Expectation-Maximization (pt 1)
• Expectation-Maximization (pt 2)
• Expectation-Maximization (pt 3)

Setting Up Your Environment (Appendix)

• Pre-Installation Check
• Anaconda Environment Setup
• How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow

Extra Help With Python Coding for Beginners (Appendix)

• How to Code Yourself (part 1)
• How to Code Yourself (part 2)
• Proof that using Jupyter Notebook is the same as not using it
• How to use Github & Extra Coding Tips (Optional)

Effective Learning Strategies for Machine Learning (Appendix)

• How to Succeed in this Course (Long Version)
• Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
• What order should I take your courses in? (part 1)
• What order should I take your courses in? (part 2)