Cluster Analysis and Unsupervised Machine Learning in Python free download paid course from google drive. You will learn Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE in this complete course.
- Understand the regular K-Means algorithm
- Understand and enumerate the disadvantages of K-Means Clustering
- Understand the soft or fuzzy K-Means Clustering algorithm
- Implement Soft K-Means Clustering in Code
- Understand Hierarchical Clustering
- Explain algorithmically how Hierarchical Agglomerative Clustering works
- Apply Scipy’s Hierarchical Clustering library to data
- Understand how to read a dendrogram
- Understand the different distance metrics used in clustering
- Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA
- Understand the Gaussian mixture model and how to use it for density estimation
- Write a GMM in Python code
- Explain when GMM is equivalent to K-Means Clustering
- Explain the expectation-maximization algorithm
- Understand how GMM overcomes some disadvantages of K-Means
- Understand the Singular Covariance problem and how to fix it
Cluster Analysis and Unsupervised Machine Learning in Python Course Requirements
- Know how to code in Python and Numpy
- Install Numpy and Scipy
- Matrix arithmetic, probability
Cluster Analysis and Unsupervised Machine Learning in Python Course Description
Group examination is a staple of solo AI and information science.
It is exceptionally helpful for information mining and huge information since it consequently discovers designs in the information, without the requirement for names, dissimilar to managed AI.
In a certifiable climate, you can envision that a robot or a man-made reasoning won’t generally approach the ideal answer, or perhaps there is definitely not an ideal right answer. You’d need that robot to have the option to investigate the world all alone, and learn things just by searching for designs.
Do you actually think about how we get the information that we use in our managed AI calculations?
We generally appear to have a decent CSV or a table, total with Xs and comparing Ys.
In the event that you haven’t been associated with getting information yourself, you probably won’t have contemplated this, however somebody needs to make this information!
Those “Y”s need to come from some place, and a ton of the time that includes physical work.
Now and then, you don’t approach this sort of data or it is infeasible or expensive to get.
In any case, you actually need to have some thought of the construction of the information. In case you’re doing information examination mechanizing design acknowledgment in your information would be priceless.
This is the place where unaided AI becomes possibly the most important factor.
In this course we are first going to discuss bunching. This is the place where as opposed to preparing on marks, we attempt to make our own names! We’ll do this by gathering information that appears to be indistinguishable.
There are 2 techniques for grouping we’ll discuss: k-implies bunching and progressive grouping.
Then, on the grounds that in AI we like to discuss likelihood appropriations, we’ll go into Gaussian combination models and bit thickness assessment, where we talk about how to “learn” the likelihood dispersion of a bunch of information.
One fascinating reality is that under specific conditions, Gaussian blend models and k-implies bunching are actually the equivalent! We’ll demonstrate how this is the situation.
All the calculations we’ll discuss in this course are staples in AI and information science, so in the event that you need to realize how to consequently discover designs in your information with information mining and example extraction, without requiring somebody to place in manual work to name that information, at that point this course is for you.
All the materials for this course are FREE. You can download and introduce Python, Numpy, and Scipy with basic orders on Windows, Linux, or Mac.
This course centers around “how to fabricate and comprehend”, not only “how to utilize”. Anybody can figure out how to utilize an API shortly in the wake of perusing some documentation. It’s not tied in with “recalling realities”, it’s tied in with “seeing with your own eyes” through experimentation. It will show you how to picture what’s going on in the model inside. On the off chance that you need something other than a shallow glance at AI models, this course is for you.
“On the off chance that you can’t actualize it, you don’t get it”
Or on the other hand as the incredible physicist Richard Feynman stated: “What I can’t make, I don’t comprehend”.
My courses are the ONLY courses where you will figure out how to actualize AI calculations without any preparation
Different courses will show you how to connect your information into a library, yet do you truly require help with 3 lines of code?
In the wake of doing likewise with 10 datasets, you understand you didn’t learn 10 things. You learned 1 thing, and just rehashed similar 3 lines of code multiple times…
framework expansion, augmentation
Python coding: if/else, circles, records, dicts, sets
Numpy coding: framework and vector activities, stacking a CSV document
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Look at the talk “AI and AI Prerequisite Roadmap” (accessible in the FAQ of any of my courses, including the free Numpy course)
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Who this course is for:
- Understudies and experts keen on AI and information science
- Individuals who need a prologue to unaided AI and bunch investigation
- Individuals who need to realize how to compose their own grouping code
- Experts intrigued by information mining enormous informational collections to search for designs consequently
Cluster Analysis and Unsupervised Machine Learning in Python Course Download Now