Cluster Analysis and Unsupervised Machine Learning in Python
Cluster Analysis and Unsupervised Machine Learning in Python
Where does unsupervised machine learning come into play?
The basics in machine learning as well as data science are based on supervised and unsupervised algorithms. To cope up with the basics, understanding cluster analysis is significant.
Cluster analysis mines the data and dealing with big data where we intend to find patterns that could work automatically on a given dataset. Unlike supervised algorithms, cluster analysis goes well with unsupervised learning, where the system does not require any defined label.
In our world, our robots or AI-powered systems may lack optimal decisions or simply, may provide incorrect answers due to a lack of dataset guidelines. To achieve the optimum answer that is 100% accurate, we need to train them on different scenarios and let them recognize our world through their perceptions. Obviously, these perceptions are the ones we have to implement through mathematical logic for a long.
In order to train AI-based systems, we need to provide them with datasets in specialized machine-understandable formats. These formats can be a table or a nicely presented CSV sheet. You know about tables, right.
CSV is different from tables. It consists of the data with Xs and Ys coordinates separated with special symbols such as semicolon ";"
The data is sometimes we prepare ourselves while other times we get it to form other resources and convert it into CSV format. Sometimes, we don’t have access to such information or it is impracticable or costly to acquire.
However, we still want to have some idea data structure. And if we opt for data analytics on certain data automating pattern recognition seems invaluable, making it difficult for a system to learn.
This is where unsupervised machine learning comes in handy.
What will you learn in this course?
In this course, we will learn about clustering in initial phases. In clustering, we create our labels by grouping the data of categories and train our algorithm on those labels.
To train the system on algorithm through clustering, we have two methods. One is k-means and the other is hierarchical clustering. These two methods are based on probabilities. Through probability distributions in these methods, we will learn Gaussian mixture models and kernel density estimation. Also, we will talk about how to "learn" the probability distribution of a set of data, don't worry.
The interesting part? Under some conditions, Gaussian mixture models are similar to k-means clustering! We will prove them all in our course.
In this course, we will cover are the algorithms of machine learning and data science. Therefore, if you want your next algorithm trained through data mining that automatically recognizes patterns from extraction to process, the course is you! Here, our automation term refers to no manual labeling.
We assure you every material we will be providing you in this course would be free from charges. You can install, download, or repair all machine learning libraries with no fee at all. You can obtain all required libraries such as Python, Numpy, and Scipy with basic commands on Linux, Windows, or Mac.
What's more? The course is totally focused on "how to build and understand", not just "formal interaction with screen".
For instance, anyone can get hands-on API modifications in just 15 minutes after reviewing our documentation.
Our courses are never about memorizing the facts and figures, we rely on pure experimentations. Our courses are all about seeing yourself in the big picture where you can see yourself as an expert in machine learning.
From understanding the machine learning models internally to bringing up new logic and combinations of algorithms, we provide superficial expertise in machine learning algorithms/models.
Prerequisite Knowledge for this course
- Linear algebra
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a csv file
Tips & Traps (for getting through the course):
- Watch videos at 2x.
- Make handwritten notes to increase concepts retaining abilities about information.
- Write formulae, logic, and equations, if you do not! We guarantee it will just look like gibberish.
- Ask relevant questions on the discussion board. As they say, the more the better!
- Realizing practices will take only a few days or weeks to accomplish this course.
Useful course ordering:
- Linear Regression in Python
- Logistic Regression in Python
- Supervised Machine Learning in Python
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- Convolutional Neural Networks in Python
- Easy NLP
- Cluster Analysis and Unsupervised Machine Learning
- Unsupervised Deep Learning
- Hidden Markov Models
- Recurrent Neural Networks in Python
- Natural Language Processing with Deep Learning in Python
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StartAn Easy Introduction to K-Means Clustering (7:06)
StartHard K-Means Exercise Prompt 1 (9:13)
StartHard K-Means Exercise 1 Solution (11:08)
StartHard K-Means Exercise Prompt 2 (5:04)
StartHard K-Means Exercise 2 Solution (7:08)
StartHard K-Means Exercise Prompt 3 (6:54)
StartHard K-Means Exercise 3 Solution (16:22)
StartHard K-Means Objective Theory (13:01)
StartHard K-Means Objective Code (5:13)
StartSoft K-Means (2:20)
StartThe Soft K-Means Objective Function (1:39)
StartSoft K-Means in Python Code (10:03)
StartVisualizing Each Step of K-Means (2:18)
StartExamples of where K-Means can fail (7:32)
StartDisadvantages of K-Means Clustering (2:13)
StartHow to Evaluate a Clustering (Purity, Davies-Bouldin Index) (6:33)
StartUsing K-Means on Real Data MNIST (2:29)
StartOne Way to Choose K (5:15)
StartK-Means Application Finding Clusters of Related Words (8:38)
StartVisual Walkthrough of Agglomerative Hierarchical Clustering (2:35)
StartAgglomerative Clustering Options (3:38)
StartUsing Hierarchical Clustering in Python and Interpreting the Dendogram (4:38)
StartApplication Evolution (14:00)
StartApplication Donald Trump vs. Hillary Clinton Tweets (18:34)
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