| 0 | 33.3 KB | ||
| 1. Section 1.1 Introduction.mp4 | 43.1 MB | ||
| 1 | 17.6 KB | ||
| 1. Section 10.1 Clustering Quality Metrics.mp4 | 112.9 MB | ||
| 1. Section 11.1 Concluding Remarks and Thank You.mp4 | 44.6 MB | ||
| 1. Section 12.1 Optional Lecture.mp4 | 7.9 MB | ||
| 1. Section 2.0 Python Demos.mp4 | 11.4 MB | ||
| 1. Section 3.0 Review of Mathematical Concepts.mp4 | 11.5 MB | ||
| 1. Section 4.1 Hierarchical Clustering Algorithm.mp4 | 48.3 MB | ||
| 1. Section 5.1 DBSCAN Algorithm.mp4 | 70.1 MB | ||
| 1. Section 6.1 K Means Algorithm.mp4 | 87 MB | ||
| 1. Section 7.1 Normal Distribution Probability Density Function.mp4 | 124.5 MB | ||
| 1. Section 8.1 Metrics for Measuring Quality of Clustering.mp4 | 146.3 MB | ||
| 1. Section 9.0 Dimension Reduction Overview.mp4 | 16.1 MB | ||
| 1.1 Link to Introduction to Machine Learning course on Udemy.html | 204.8 B | ||
| 1.1 UnsupervisedML_GMM.pdf | 285.6 KB | ||
| 1.2 Link to What is Machine Learning free course on Udemy.html | 102.4 B | ||
| 10. Section 2.5 Pandas Demo.mp4 | 43.6 MB | ||
| 10. Section 3.5 Mean, Variance, and Covariance.mp4 | 71.3 MB | ||
| 11. Section 2.5 Exercises.mp4 | 3.2 MB | ||
| 11. Section 3.5 Exercises.mp4 | 3.2 MB | ||
| 11.1 UnsupervisedML_Exercises_Section2.5.pdf | 150.2 KB | ||
| 11.1 UnsupervisedML_Exercises_Section3.5.pdf | 108.1 KB | ||
| 12. Section 2.6 Sklearn Datasets Demo.mp4 | 40.3 MB | ||
| 2. Section 1.2 About this Course.mp4 | 12.1 MB | ||
| 2. Section 10.2 Clustering for Iris Flower Dataset.mp4 | 177.8 MB | ||
| 2 | 7.7 KB | ||
| 2. Section 2.1 Numpy Basic Demos.mp4 | 186.3 MB | ||
| 2. Section 3.1 What is Data in Unsupervised Learning.mp4 | 100.8 MB | ||
| 2. Section 4.1 Exercises.mp4 | 3.2 MB | ||
| 2. Section 5.1 Exercises.mp4 | 3.2 MB | ||
| 2. Section 6.1 Exercises.mp4 | 3.2 MB | ||
| 2. Section 7.1 Exercises.mp4 | 3.2 MB | ||
| 2. Section 8.1 Exercises.mp4 | 3.2 MB | ||
| 2. Section 9.1 Principal Component Analysis Algorithm.mp4 | 141.2 MB | ||
| 2.1 UnsupervisedML_Exercises_Section4.1.pdf | 122.1 KB | ||
| 2.1 UnsupervisedML_Exercises_Section5.1.pdf | 51.4 KB | ||
| 2.1 UnsupervisedML_Exercises_Section6.1.pdf | 136 KB | ||
| 2.1 UnsupervisedML_Exercises_Section7.1.pdf | 145.4 KB | ||
| 2.1 UnsupervisedML_Exercises_Section8.1.pdf | 51.9 KB | ||
| 3. Section 1.3 Course Resources and Set Up.mp4 | 98.6 MB | ||
| 3. Section 10.2 Exercises.mp4 | 3.2 MB | ||
| 3. Section 2.1 Exercises.mp4 | 3.2 MB | ||
| 3 | 23.5 KB | ||
| 3. Section 3.1 Exercises.mp4 | 3.2 MB | ||
| 3. Section 4.2 Hierarchical Clustering Code Design.mp4 | 98.2 MB | ||
| 3. Section 5.2 DBSCAN Code Design.mp4 | 41.8 MB | ||
| 3. Section 6.2 K Means Code Design.mp4 | 82.7 MB | ||
| 3. Section 7.2 Gaussian Mixture Model Algorithm.mp4 | 98 MB | ||
| 3. Section 8.2 Comparison of Algorithms.mp4 | 114.7 MB | ||
| 3. Section 9.1 Exercises.mp4 | 3.2 MB | ||
| 3.1 UnsupervisedML_Exercises_Section10.2.pdf | 37 KB | ||
| 3.1 UnsupervisedML_Exercises_Section2.1.pdf | 116.2 KB | ||
| 3.1 UnsupervisedML_Exercises_Section3.1.pdf | 195.4 KB | ||
| 3.1 UnsupervisedML_Exercises_Section9.1.pdf | 129.7 KB | ||
| 3.1 UnsupervisedML_GMM.pdf | 285.6 KB | ||
| 3.1 UnsupervisedML_Resources.pdf | 178.7 KB | ||
| 3.2 Course Github site.html | 102.4 B | ||
| 4. Section 10.3 Clustering for MNIST Digits Dataset.mp4 | 125.7 MB | ||
| 4. Section 2.2 Numpy Matrix Operations Demo.mp4 | 70.5 MB | ||
| 4. Section 3.2 Computational Complexity.mp4 | 94.5 MB | ||
| 4. Section 4.3 Hierarchical Clustering Code Walkthrough.mp4 | 236 MB | ||
| 4. Section 5.3 DBSCAN Code Walkthrough.mp4 | 174.7 MB | ||
| 4. Section 6.3 K Means Code Walkthrough.mp4 | 198.3 MB | ||
| 4. Section 7.2 Exercises.mp4 | 3.2 MB | ||
| 4. Section 9.2 Principal Component Analysis Code Design.mp4 | 22.7 MB | ||
| 4 | 18.7 KB | ||
| 4.1 UnsupervisedML_Exercises_Section7.2.pdf | 142.6 KB | ||
| 5. Section 3.2 Exercises.mp4 | 3.2 MB | ||
| 5. Section 4.3 Exercises.mp4 | 3.2 MB | ||
| 5 | 32.5 KB | ||
| 5. Exercises for Section 10.3.mp4 | 3.2 MB | ||
| 5. Section 2.2 Exercises.mp4 | 3.2 MB | ||
| 5. Section 5.3 Exercises.mp4 | 3.2 MB | ||
| 5. Section 6.3 Exercises.mp4 | 3.2 MB | ||
| 5. Section 7.3 Gaussian Mixture Model Code Design.mp4 | 84 MB | ||
| 5. Section 9.3 Principal Component Analysis Code Walkthrough.mp4 | 102.3 MB | ||
| 5.1 UnsupervisedML_Exercises_Section10.3.pdf | 72.3 KB | ||
| 5.1 UnsupervisedML_Exercises_Section2.2.pdf | 110 KB | ||
| 5.1 UnsupervisedML_Exercises_Section3.2.pdf | 114 KB | ||
| 5.1 UnsupervisedML_Exercises_Section4.3.pdf | 40.7 KB | ||
| 5.1 UnsupervisedML_Exercises_Section5.3.pdf | 40.5 KB | ||
| 5.1 UnsupervisedML_Exercises_Section6.3.pdf | 58.4 KB | ||
| 6 | 7.3 KB | ||
| 6. Section 10.4 Clustering for BBC Text Dataset.mp4 | 185.9 MB | ||
| 6. Section 2.3 Matplotlib Basic Demo.mp4 | 71.4 MB | ||
| 6. Section 3.3 Distance Measures.mp4 | 67 MB | ||
| 6. Section 7.4 Gaussian Mixture Model Code Walkthrough.mp4 | 280.6 MB | ||
| 6. Section 9.3 Exercises.mp4 | 3.2 MB | ||
| 6.1 UnsupervisedML_Exercises_Section9.3.pdf | 110.4 KB | ||
| 7. Section 10.4 Exercises.mp4 | 3.2 MB | ||
| 7. Section 2.3 Exercises.mp4 | 3.2 MB | ||
| 7. Section 3.3 Exercises.mp4 | 3.2 MB | ||
| 7 | 2.2 KB | ||
| 7. Section 7.4 Exercises.mp4 | 3.2 MB | ||
| 7. Section 9.4 PCA Applied to MNIST Digits Dataset.mp4 | 156.7 MB | ||
| 7.1 UnsupervisedML_Exercises_Section10.4.pdf | 58.5 KB | ||
| 7.1 UnsupervisedML_Exercises_Section2.3.pdf | 110.1 KB | ||
| 7.1 UnsupervisedML_Exercises_Section3.3.pdf | 112.9 KB | ||
| 7.1 UnsupervisedML_Exercises_Section7.4.pdf | 130.5 KB | ||
| 8. Section 2.4 Matplotlib Cluster Plot and Animation Demo.mp4 | 161.9 MB | ||
| 8. Section 3.4 Singular Value Decomposition.mp4 | 103.8 MB | ||
| 8. Section 9.4 Exercises.mp4 | 3.2 MB | ||
| 8.1 UnsupervisedML_Exercises_Section9.4.pdf | 155 KB | ||
| 9. Section 2.4 Exercises.mp4 | 3.2 MB | ||
| 9. Section 3.4 Exercises.mp4 | 3.2 MB | ||
| 9. Section 9.5 Autoencoders.mp4 | 35.8 MB | ||
| 9.1 UnsupervisedML_Exercises_Section2.4.pdf | 109.6 KB | ||
| 9.1 UnsupervisedML_Exercises_Section3.4.pdf | 113.3 KB | ||
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| ▲ 170 total files | |||
Description
Unsupervised Machine Learning involves finding patterns in datasets.
After taking this course, students will be able to understand, implement in Python, and apply algorithms of Unsupervised Machine Learning to real-world datasets.
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
The core of this course involves detailed study of the following algorithms:
Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model
Dimension Reduction: Principal Component Analysis
The course presents the math underlying these algorithms including normal distributions, expectation maximization, and singular value decomposition. The course also presents detailed explanation of code design and implementation in Python, including use of vectorization for speed up, and metrics for measuring quality of clustering and dimension reduction.
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).
Plenty of examples are presented and plots and animations are used to help students get a better understanding of the algorithms.
Course also includes a number of exercises (theoretical, Jupyter Notebook, and programming) for students to gain additional practice.
All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.
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
Who this course is for:
Scientists, engineers and programmers interested in data science/machine learning
Requirements
Basic knowledge of Linear Algebra including vectors, matrices, transpose, matrix multiplications, linear spaces
Basic knowledge of Probability and Statistics including mean, covariance, and normal distributions
Ability to program in Python 3
Ability to run Python 3 programs on local machine in Jupyter notebooks and command window
Last Updated 4/2021
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