Cluster Analysis and Unsupervised Machine Learning in Python Online Course
Cluster Analysis and Unsupervised Machine Learning in Python Online Course
Unlock the potential of unsupervised machine learning with this in-depth Cluster Analysis and Unsupervised Learning course in Python. Start by mastering the fundamentals of K-Means clustering, followed by practical coding exercises to strengthen your skills. Explore hierarchical clustering, including agglomerative techniques and dendrogram interpretation, through real-world applications like political tweet analysis. The course progresses to Gaussian Mixture Models (GMMs), where you'll learn to compare GMM with K-Means and grasp the Expectation-Maximization algorithm. By the end, you’ll have a comprehensive understanding of unsupervised learning techniques and be equipped to tackle complex clustering problems in Python.
Key Benefits
- Thorough introduction to the concepts of unsupervised learning and clustering techniques.
- Detailed exploration of K-Means clustering, hierarchical clustering, and Gaussian Mixture Models (GMM).
- Hands-on coding exercises and the application of clustering methods to real-world datasets for practical learning.
Target Audience
This course is for data scientists, machine learning engineers, and analysts with a foundational understanding of Python and statistics. While prior knowledge of machine learning concepts is helpful, it is not a prerequisite. If you're aiming to strengthen your skills in data analysis and machine learning, this course is ideal for you.
Learning Objectives
- Implement various clustering algorithms using Python.
- Analyze the advantages and limitations of different clustering techniques.
- Apply clustering methods to solve real-world problems with actual datasets.
- Understand the theoretical foundations of K-Means, Hierarchical Clustering, and Gaussian Mixture Models (GMMs).
- Evaluate clustering outcomes using metrics such as purity and the Davies-Bouldin Index.
- Visualize the steps and outcomes of clustering algorithms to gain deeper insights into the results.
Course Outline
The Cluster Analysis and Unsupervised Machine Learning in Python Exam covers the following topics -
Module 1 - Getting Started
- Source Code Access
Module 2 - Introduction to Unsupervised Learning
- Applications of Unsupervised Learning
- Why Clustering is Important
Module 3 - K-Means Clustering
- Introduction to K-Means Clustering
- K-Means Algorithm Overview: Theory and Code
- Soft K-Means vs. Hard K-Means
- Visualizing K-Means Clustering Steps
- Common Pitfalls in K-Means Clustering
- Limitations of K-Means Clustering
- Evaluating Clustering Performance (Purity, Davies-Bouldin Index)
- Applying K-Means to Real Data (MNIST)
- Determining the Optimal Value of K
- K-Means for Word Clustering
- Real-World Applications of K-Means in NLP and Computer Vision
Module 4 - Hierarchical Clustering
- Visual Overview of Agglomerative Hierarchical Clustering
- Exploring Agglomerative Clustering Options
- Implementing Hierarchical Clustering in Python and Interpreting Dendrograms
- Real-World Application: Evolutionary Analysis and Analyzing Tweets from Political Figures (Donald Trump vs. Hillary Clinton)
Module 5 - Gaussian Mixture Models (GMM)
- Understanding Gaussian Mixture Model (GMM)
- Implementing GMM in Python
- Practical Challenges with GMM
- Comparing GMM and K-Means Clustering
- Introduction to Kernel Density Estimation
- GMM vs. Bayes Classifier (Part 1 & 2)
- Introduction to Expectation-Maximization (EM) Algorithm (Part 1)
- Expectation-Maximization (Part 2 & 3)
Module 6 - Environment Setup (Appendix)
- Pre-Installation Requirements
- Setting Up Anaconda Environment
- Installing Numpy, Scipy, Matplotlib, Pandas, and TensorFlow
Module 7 - Additional Help for Python Beginners (Appendix)
- Coding for Beginners (Part 1 & 2)
- Comparison of Jupyter Notebook Usage
- Using GitHub & Extra Coding Tips (Optional)
Module 8 - Effective Learning Strategies for Machine Learning (Appendix)
- Tips for Success in this Course
- Is this Course for Beginners or Experts? Practical vs. Academic Focus
- Choosing the Right Course Order (Part 1 and 2)
