Udemy - Unsupervised Machine Learning with Python

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Unsupervised Machine Learning with Python [TutsNode.com] - Unsupervised Machine Learning with Python 7. Gaussian Mixture Model Clustering
  • 6. Section 7.4 Gaussian Mixture Model Code Walkthrough.mp4 (280.6 MB)
  • 1.1 UnsupervisedML_GMM.pdf (285.6 KB)
  • 3.1 UnsupervisedML_GMM.pdf (285.6 KB)
  • 2.1 UnsupervisedML_Exercises_Section7.1.pdf (145.4 KB)
  • 4.1 UnsupervisedML_Exercises_Section7.2.pdf (142.6 KB)
  • 7.1 UnsupervisedML_Exercises_Section7.4.pdf (130.5 KB)
  • 1. Section 7.1 Normal Distribution Probability Density Function.mp4 (124.5 MB)
  • 3. Section 7.2 Gaussian Mixture Model Algorithm.mp4 (98.0 MB)
  • 5. Section 7.3 Gaussian Mixture Model Code Design.mp4 (84.0 MB)
  • 7. Section 7.4 Exercises.mp4 (3.2 MB)
  • 4. Section 7.2 Exercises.mp4 (3.2 MB)
  • 2. Section 7.1 Exercises.mp4 (3.2 MB)
10. Case Studies
  • 5.1 UnsupervisedML_Exercises_Section10.3.pdf (72.3 KB)
  • 7.1 UnsupervisedML_Exercises_Section10.4.pdf (58.5 KB)
  • 6. Section 10.4 Clustering for BBC Text Dataset.mp4 (185.9 MB)
  • 2. Section 10.2 Clustering for Iris Flower Dataset.mp4 (177.8 MB)
  • 3.1 UnsupervisedML_Exercises_Section10.2.pdf (37.0 KB)
  • 4. Section 10.3 Clustering for MNIST Digits Dataset.mp4 (125.7 MB)
  • 1. Section 10.1 Clustering Quality Metrics.mp4 (112.9 MB)
  • 7. Section 10.4 Exercises.mp4 (3.2 MB)
  • 5. Exercises for Section 10.3.mp4 (3.2 MB)
  • 3. Section 10.2 Exercises.mp4 (3.2 MB)
12. Optional
  • 1.1 Link to Introduction to Machine Learning course on Udemy.html (0.2 KB)
  • 1.2 Link to What is Machine Learning free course on Udemy.html (0.1 KB)
  • 1. Section 12.1 Optional Lecture.mp4 (7.9 MB)
1. Introduction
  • 3.2 Course Github site.html (0.1 KB)
  • 3.1 UnsupervisedML_Resources.pdf (178.7 KB)
  • 3. Section 1.3 Course Resources and Set Up.mp4 (98.6 MB)
  • 1. Section 1.1 Introduction.mp4 (43.1 MB)
  • 2. Section 1.2 About this Course.mp4 (12.1 MB)
4. Hierarchical Clustering
  • 4. Section 4.3 Hierarchical Clustering Code Walkthrough.mp4 (236.0 MB)
  • 5.1 UnsupervisedML_Exercises_Section4.3.pdf (40.7 KB)
  • 2.1 UnsupervisedML_Exercises_Section4.1.pdf (122.1 KB)
  • 3. Section 4.2 Hierarchical Clustering Code Design.mp4 (98.2 MB)
  • 1. Section 4.1 Hierarchical Clustering Algorithm.mp4 (48.3 MB)
  • 2. Section 4.1 Exercises.mp4 (3.2 MB)
  • 5. Section 4.3 Exercises.mp4 (3.2 MB)
6. K Means Clustering
  • 4. Section 6.3 K Means Code Walkthrough.mp4 (198.3 MB)
  • 5.1 UnsupervisedML_Exercises_Section6.3.pdf (58.4 KB)
  • 2.1 UnsupervisedML_Exercises_Section6.1.pdf (136.0 KB)
  • 1. Section 6.1 K Means Algorithm.mp4 (87.0 MB)
  • 3. Section 6.2 K Means Code Design.mp4 (82.7 MB)
  • 5. Section 6.3 Exercises.mp4 (3.2 MB)
  • 2. Section 6.1 Exercises.mp4 (3.2 MB)
3. Review of Mathematical Concepts
  • 3.1 UnsupervisedML_Exercises_Section3.1.pdf (195.4 KB)
  • 5.1 UnsupervisedML_Exercises_Section3.2.pdf (114.0 KB)
  • 9.1 UnsupervisedML_Exercises_Section3.4.pdf (113.3 KB)
  • 7.1 UnsupervisedML_Exercises_Section3.3.pdf (112.9 KB)
  • 11.1 UnsupervisedML_Exercises_Section3.5.pdf (108.1 KB)
  • 8. Section 3.4 Singular Value Decomposition.mp4 (103.8 MB)
  • 2. Section 3.1 What is Data in Unsupervised Learning.mp4 (100.8 MB)
  • 4. Section 3.2 Computational Complexity.mp4 (94.5 MB)
  • 10. Section 3.5 Mean, Variance, and Covariance.mp4 (71.3 MB)
  • 6. Section 3.3 Distance Measures.mp4 (67.0 MB)
  • 1. Section 3.0 Review of Mathematical Concepts.mp4 (11.5 MB)
  • 11. Section 3.5 Exercises.mp4 (3.2 MB)
  • 5. Section 3.2 Exercises.mp4 (3.2 MB)
  • 7. Section 3.3 Exercises.mp4 (3.2 MB)
  • 9. Section 3.4 Exercises.mp4 (3.2 MB)
  • 3. Section 3.1 Exercises.mp4 (3.2 MB)
2. Python Demos
  • 2. Section 2.1 Numpy Basic Demos.mp4 (186.3 MB)
  • 11.1 UnsupervisedML_Exercises_Section2.5.pdf (150.2 KB)
  • 8. Section 2.4 Matplotlib Cluster Plot and Animation Demo.mp4 (161.9 MB)
  • 3.1 UnsupervisedML_Exercises_Section2.1.pdf (116.2 KB)
  • 7.1 UnsupervisedML_Exercises_Section2.3.pdf (110.1 KB)
  • 5.1 UnsupervisedML_Exercises_Section2.2.pdf (110.0 KB)
  • 9.1 UnsupervisedML_Exercises_Section2.4.pdf (109.6 KB)
  • 6. Section 2.3 Matplotlib Basic Demo.mp4 (71.4 MB)
  • 4. Section 2.2 Numpy Matrix Operations Demo.mp4 (70.5 MB)
  • 10. Section 2.5 Pandas Demo.mp4 (43.6 MB)
  • 12. Section 2.6 Sklearn Datasets Demo.mp4 (40.3 MB)
  • 1. Section 2.0 Python Demos.mp4 (11.4 MB)
  • 3. Section 2.1 Exercises.mp4 (3.2 MB)
  • 5. Section 2.2 Exercises.mp4 (3.2 MB)
  • 11. Section 2.5 Exercises.mp4 (3.2 MB)
  • 7. Section 2.3 Exercises.mp4 (3.2 MB)
  • 9. Section 2.4 Exercises.mp4 (3.2 MB)
9. Dimension Reduction
  • 8.1 UnsupervisedML_Exercises_Section9.4.pdf (155.0 KB)
  • 3.1 UnsupervisedML_Exercises_Section9.1.pdf (129.7 KB)
  • 7. Section 9.4 PCA Applied to MNIST Digits Dataset.mp4 (156.7 MB)
  • 6.1 UnsupervisedML_Exercises_Section9.3.pdf (110.4 KB)
  • 2. Section 9.1 Principal Component Analysis Algorithm.mp4 (141.2 MB)
  • 5. Section 9.3 Principal Component Analysis Code Walkthrough.mp4 (102.3 MB)
  • 9. Section 9.5 Autoencoders.mp4 (35.8 MB)
  • 4. Section 9.2 Principal Component Analysis Code Design.mp4 (22.7 MB)
  • 1. Section 9.0 Dimension Reduction Overview.mp4 (16.1 MB)
  • 8. Section 9.4 Exercises.mp4 (3.2 MB)
  • 3. Section 9.1 Exercises.mp4 (3.2 MB)
  • 6. Section 9.3 Exercises.mp4 (3.2 MB)
5. DBSCAN Clustering
  • 4. Section 5.3 DBSCAN Code Walkthrough.mp4 (174.7 MB)
  • 2.1 UnsupervisedML_Exercises_Section5.1.pdf (51.4 KB)
  • 5.1 UnsupervisedML_Exercises_Section5.3.pdf (40.5 KB)
  • 1. Section 5.1 DBSCAN Algorithm.mp4 (70.1 MB)
  • 3. Section 5.2 DBSCAN Code Design.mp4 (41.8 MB)
  • 5. Section 5.3 Exercises.mp4 (3.2 MB)
  • 2. Section 5.1 Exercises.mp4 (3.2 MB)

Description


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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Udemy - Unsupervised Machine Learning with Python


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Udemy - Unsupervised Machine Learning with Python


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