Cluster Analysis and Unsupervised Machine Learning in Python

  • Introduction to Unsupervised Learning
  • Introduction
Introduction
05:03
Course Outline
04:34
What is unsupervised learning used for?
05:31
Why Use Clustering?
09:20
Where to get the code
04:36
How to Succeed in this Course
03:04
An Easy Introduction to K-Means Clustering
07:07
Hard K-Means: Exercise Prompt 1
09:13
Hard K-Means: Exercise 1 Solution
11:09
Hard K-Means: Exercise Prompt 2
05:04
Hard K-Means: Exercise 2 Solution
07:08
Hard K-Means: Exercise Prompt 3
06:55
Hard K-Means: Exercise 3 Solution
16:22
Hard K-Means Objective: Theory
13:01
Hard K-Means Objective: Code
05:13
Visual Walkthrough of the K-Means Clustering Algorithm (Legacy)
02:59
Soft K-Means
05:42
The K-Means Objective Function
01:40
Soft K-Means in Python Code
10:04
How to Pace Yourself
03:19
Visualizing Each Step of K-Means
02:19
Examples of where K-Means can fail
07:33
Disadvantages of K-Means Clustering
02:14
How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
06:34
Using K-Means on Real Data: MNIST
05:01
One Way to Choose K
05:16
K-Means Application: Finding Clusters of Related Words
08:39
Clustering for NLP and Computer Vision: Real-World Applications
06:58
Suggestion Box
03:10
Visual Walkthrough of Agglomerative Hierarchical Clustering
02:36
Agglomerative Clustering Options
03:39
Using Hierarchical Clustering in Python and Interpreting the Dendrogram
04:39
Application: Evolution
14:01
Application: Donald Trump vs. Hillary Clinton Tweets
18:35
Gaussian Mixture Model (GMM) Algorithm
15:31
Write a Gaussian Mixture Model in Python Code
18:54
Practical Issues with GMM / Singular Covariance
09:07
Comparison between GMM and K-Means
03:55
Kernel Density Estimation
06:24
GMM vs Bayes Classifier (pt 1)
09:28
GMM vs Bayes Classifier (pt 2)
11:30
Expectation-Maximization (pt 1)
11:45
Expectation-Maximization (pt 2)
02:24
Expectation-Maximization (pt 3)
08:09
Description of the Gaussian Mixture Model and How to Train a GMM
03:05
Comparison between GMM and K-Means
01:45
Write a Gaussian Mixture Model in Python Code
10:00
Practical Issues with GMM / Singular Covariance
02:56
Kernel Density Estimation
02:11
Expectation-Maximization
02:02
Future Unsupervised Learning Algorithms You Will Learn
01:02
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49
Introduction
05:03
Course Outline
04:34
What is unsupervised learning used for?
05:31
Why Use Clustering?
09:20
Where to get the code
04:36
How to Succeed in this Course
03:04
An Easy Introduction to K-Means Clustering
07:07
Hard K-Means: Exercise Prompt 1
09:13
Hard K-Means: Exercise 1 Solution
11:09
Hard K-Means: Exercise Prompt 2
05:04
Hard K-Means: Exercise 2 Solution
07:08
Hard K-Means: Exercise Prompt 3
06:55
Hard K-Means: Exercise 3 Solution
16:22
Hard K-Means Objective: Theory
13:01
Hard K-Means Objective: Code
05:13
Visual Walkthrough of the K-Means Clustering Algorithm (Legacy)
02:59
Soft K-Means
05:42
The K-Means Objective Function
01:40
Soft K-Means in Python Code
10:04
How to Pace Yourself
03:19
Visualizing Each Step of K-Means
02:19
Examples of where K-Means can fail
07:33
Disadvantages of K-Means Clustering
02:14
How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
06:34
Using K-Means on Real Data: MNIST
05:01
One Way to Choose K
05:16
K-Means Application: Finding Clusters of Related Words
08:39
Clustering for NLP and Computer Vision: Real-World Applications
06:58
Suggestion Box
03:10
Visual Walkthrough of Agglomerative Hierarchical Clustering
02:36
Agglomerative Clustering Options
03:39
Using Hierarchical Clustering in Python and Interpreting the Dendrogram
04:39
Application: Evolution
14:01
Application: Donald Trump vs. Hillary Clinton Tweets
18:35
Gaussian Mixture Model (GMM) Algorithm
15:31
Write a Gaussian Mixture Model in Python Code
18:54
Practical Issues with GMM / Singular Covariance
09:07
Comparison between GMM and K-Means
03:55
Kernel Density Estimation
06:24
GMM vs Bayes Classifier (pt 1)
09:28
GMM vs Bayes Classifier (pt 2)
11:30
Expectation-Maximization (pt 1)
11:45
Expectation-Maximization (pt 2)
02:24
Expectation-Maximization (pt 3)
08:09
Description of the Gaussian Mixture Model and How to Train a GMM
03:05
Comparison between GMM and K-Means
01:45
Write a Gaussian Mixture Model in Python Code
10:00
Practical Issues with GMM / Singular Covariance
02:56
Kernel Density Estimation
02:11
Expectation-Maximization
02:02
Future Unsupervised Learning Algorithms You Will Learn
01:02
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49

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