Unsupervised Machine Learning with Python

🚀 Dive into Unsupervised Machine Learning with Python! 🧠💻

Course Outcome:

Discover how to master unsupervised machine learning algorithms in Python, and learn to apply these techniques to real-world datasets for insights and predictions. By the end of this course, you'll have a solid understanding of clustering and dimensionality reduction methods, and the skills to implement them effectively.

Course Topics and Approach:

This comprehensive course covers the key unsupervised machine learning algorithms that will transform your data analysis capabilities:

• Clustering Algorithms: 🤖

  • Hierarchical Clustering
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • K Means Clustering
  • Gaussian Mixture Model

• Dimension Reduction Techniques: 📊

  • Principal Component Analysis (PCA)

Get ready for an in-depth exploration into the mathematics behind these algorithms, including concepts like normal distributions, expectation maximization (EM), and singular value decomposition (SVD). We'll guide you through converting these theories into Python code with a focus on efficient implementation and vectorization techniques. Plus, practice with hands-on exercises that include programming and theoretical questions with their solutions!

Course Audience:

This course is tailored for:

• Scientists and engineers who seek to apply machine learning in their fields
• Programmers and data enthusiasts curious about the power of unsupervised learning
• Beginners with no prior experience in machine learning, as long as you have a grasp on the essentials:
  • Basic linear algebra
  • Basic probability and statistics
  • Proficiency in Python programming (Python 3)

Make sure you have Python installed on your computer, with environments like Anaconda ready for command-line operations and Jupyter Notebook usage.

Teaching Style and Resources:

We believe in a learning experience that combines theoretical knowledge with practical application. This course offers:

• Numerous examples complete with plots and animations to visually explain complex concepts.
• A variety of exercises, including theoretical, Jupyter Notebook, and programming tasks, along with their solutions for comprehensive understanding and practice.
• All resources are available for download on the course's Github site, including presentations, supplementary documents, demos, codes, and solutions to exercises.

Recent Updates:

August 28, 2021 Update:

• Added an example of an Autoencoder in Section 9.5.
• A new section with a Demo of an Autoencoder has been included in Section 9.6.

November 2, 2021 Update:

• Sections 2.3, 2.4, 3.4, and 4.3 have been updated to ensure the Python and matplotlib codes run with more recent versions, and presentations reflect the necessary changes.
• Added English captions to all course videos for a better learning experience.

Join us on this journey to unlock the secrets of Unsupervised Machine Learning with Python! 🌟

Enroll now and embark on your data analysis adventure, turning complex datasets into actionable insights. With Satish Reddy's guidance, you'll master unsupervised learning algorithms and apply them to real-world scenarios like the Iris Flowers Dataset, MNIST Digits Dataset, and BBC Text Dataset. Don't miss out on this opportunity to elevate your data science skills! 📈🔍