Tia  Gottlieb

Tia Gottlieb

1595647020

Learning the Clustering Algorithms through Hands-on

Clustering has significant use-cases across application domains to understand the overall variability structure of the data. Be it clustering of customers, students, genes, ECG Signals, images, prices of stock. We have used this successfully in feature selection and the optimization of the portfolio in some of our research articles, but more on those stories later. Clustering falls under the unsupervised category and by definition, it tries to find natural groups among the data.

Even in the era of deep learning, clustering can give you invaluable insight into the distribution of the data, which can affect your design decision.

In this article, we give you some overview of five clustering algorithms and the Kaggle notebook links where we have done some experiments. The purpose is to give you a quick summary and get you started on the application.

K- Means:

It is the simplest of all algorithms works based on distances. It’s a partitioning algorithm. Starts with k random points as the cluster centers and then for the rest of the points assign them to the closest cluster center. Once this assignment is done, recompute the cluster center. This process continues until there is no much change in the cluster assignment.

Some of the issues are:

a) we need to know the value of ‘k’

b) it is affected by outliers.

c) Depends on initialization

Some experiments are done in the following Kaggle notebook: https://www.kaggle.com/saptarsi/kmeans-dbs-sg

We start by explaining how k-means work, then with example describe why scaling is required. We also discuss how the Sum of Squared Error plotted using elbow plot is applied to find the optimal value of k. A simple example follows with ‘iris’

Corresponding Videos:

Theory: https://www.youtube.com/watch?v=FFhmNy0W4tE

Hands-on: https://www.youtube.com/watch?v=w0CTqS_KFjYK-Medoid:

K-Means is not robust against outliers. We move to the median and when we have more than one attribute and we want to find an overall median, which is called medoid. Some experiments are done using the following notebooks. To demonstrate the issue of outlying observations, we have done a small experiment of adding three outlying observations. As you know iris has three classes ( Setosa, Verginca, and Versicolor) and they are being plotted with two features. The addition of these features which are marked by the blue circle completely distorts the clustering. The original three classes are merged into two clusters and the outliers are in one cluster.

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Learning the Clustering Algorithms through Hands-on

Hand Sanitizer in bulk - Get your effective hand sanitizer here

With the spread of various harmful virus globally causing immense distress and fatalities to human mankind, it has become absolutely essential for people to ensure proper and acute hygiene and cleanliness is maintained. To further add to the perennial hardship to save lives of people the recent pandemic of Covid-19 affected globally created the worst nightmare for people of all walks of life. Looking at the present crisis, it has become imperative for human beings to be encouraged to tackle this challenge with an everlasting strength to help protect oneself and their loved ones against the devastating effects of the virus. One thing that stands up between keeping all safe and vulnerable is by making sure that everybody attentively Hand wash periodically to help physically remove germs from the skin and getting rid of the live microbes.

The essence of apposite handwashing is based around time invested in washing and the amount of soap and water used. Technically, washing hands without soap is much less effective anyway. But incase a proper handwashing support system doesn’t become possible around, the usage of Effective Hand Sanitizer will certainly help fight to reduce the number of microbes on the surface of hands efficiently, eliminating most variants of harmful bacteria to settle.

The need has come about for Hand Sanitizer in bulk to save your daily life aptly maintaining a minimum of 60% alcohol - as per the CDC recommendations and approved by USFDA for its greater effectiveness. With the growing demand of people on the move the demand for easy to carry, small, and travel size worthy pouches that are also refillable once the product runs out is the need of the hour. To further make sure that human lives are well protected from these external viruses, it is mandatory for producer of effective Hand Sanitizer to evolve products circumspectly with ingredients that produce not just saving lives but with multiple benefits for people of all ages.

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Lina  Biyinzika

Lina Biyinzika

1624970880

Reptile: OpenAI’s Latest Meta-Learning Algorithm

As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning models have become paramount to optimise outcomes. OpenAI’s meta-learning algorithm, Reptile, is one such model designed to perform a wide array of tasks.

For those unaware, meta-learning refers to the idea of ‘learning to learn by solving multiple tasks, like how humans learn. Using meta-learning, you can design models that can learn new skills or adapt to new environments rapidly with a few training examples.

In the recent past, the meta-learning algorithm has had a fair bit of success as it can learn with limited quantities of data. Unlike other learning models like reinforcement learning, which uses reward mechanisms for each action, meta-learning can generalise to different scenarios by separating a specified task into two functions.

The first function often gives a quick response within a specific task, while the second function includes the extraction of information learned from previous tasks. It is similar to how humans behave, where they often gain knowledge from previous unrelated tasks or experiences.

Typically, there are three common approaches to meta-learning.

  1. Metric-based: Learn an efficient distance metric
  2. Model-based: Use (recurrent) network with external or internal memory
  3. Optimisation-based: Optimise the model parameters explicitly for fast learning

For instance, the above image depicts the model-agnostic meta-learning algorithm (MAML) developed by researchers at the University of California, Berkeley, in partnership with OpenAI. The MAML optimises for a representation θ that can quickly adapt to new tasks.

On the other hand, Reptile utilises a stochastic gradient descent (SGD) to initialise the model’s parameters instead of performing several computations that are often resource-consuming. In other words, it also reduces the dependency of higher computational hardware requirements, if implemented in a machine learning project.

#developers corner #how reptile works #meta learning algorithm #meta-learning algorithm #algorithm

Rylan  Becker

Rylan Becker

1624449420

How to Learn Any Algorithm in Machine Learning

We all know Machine Learning is a rapidly expanding field and new techniques are being created, seemingly, by the minute. While it is often best to begin with the fundamentals of the field before jumping into these new, and often advanced, papers, a question often arises for those who are new to the field:

How do I learn all of the various algorithms in the field, and how do I learn them well?

If you are new to the field, the best way to develop a firm understanding of the fundamentals is quite simple. Simply put, while you are learning the algorithm, attempt to build it from scratch in your favorite programming language. Allow me to explain…

When learning a new concept in this highly technical field, I believe that building things from scratch not only strengthens your programming skills but also allows you to get a bottom-up and fundamental understanding of how the algorithm actually works. One thing to remember is that in Machine Learning, everything we do is inherently mathematical. If you do not understand the mathematics behind the algorithm, you will not be able to efficiently deliver the key insights of the results to those who are non-technical — who, might I add, you deal with just as much as those who are technical.

While this should be common sense, let me raise a little disclaimer from now: you building the algorithm from scratch should not replace highly optimized libraries to do the specific task. Rather, you building out the algorithm should act as a complement to learning the mathematics and seeing it solve in real-time, step by step. If you have never built a neural network before, you will likely not be able to understand the underlying mechanisms of the API calls within the libraries of PyTorch or TensorFlow.

I truly believe that if you are learning a new algorithm, learning about how it works is a great first start, but learning how to build it yourself really allows you to get lost in the beauty of **why**it works, which in my opinion, is where most of the fun can be found in this field.

#algorithms #data-science #programming #machine-learning #algorithm in machine learning

What Is Model & Algorithm In Machine Learning | Machine Learning Tutorials | Python 04

What Is Model & Algorithm In Machine Learning | Machine Learning Tutorials | Python | Ml Python

#python #machine learning #algorithm #model & algorithm #machine learning tutorials

Elton  Bogan

Elton Bogan

1604091840

Supervised Learning vs Unsupervised Learning

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details._

Nowadays, nearly everything in our lives can be quantified by data. Whether it involves search engine results, social media usage, weather trackers, cars, or sports, data is always being collected to enhance our quality of life. How do we get from all this raw data to improve the level of performance? This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns. Specifically, the main topics that are covered are:

1. Supervised & Unsupervised Learning and the main techniques corresponding to each one (Classification and Clustering, respectively).

2. An in-depth look at the K-Means algorithm

Goals

1. Understanding the many different techniques used to discover patterns in a set of data

2. In-depth understanding of the K-Means algorithm

1.1 Unsupervised and supervised learning

In unsupervised learning, we are trying to discover hidden patterns in data, when we don’t have any labels. We will go through what hidden patterns are and what labels are, and we will go through real data examples.

What is unsupervised learning?

First, let’s step back to what learning even means. In machine learning in statistics, we are typically trying to find hidden patterns in data. Ideally, we want these hidden patterns to help us in some way. For instance, to help us understand some scientific results, to improve our user experience, or to help us maximize profit in some investment. Supervised learning is when we learn from data, but we have labels for all the data we have seen so far. Unsupervised learning is when we learn from data, but we don’t have any labels.

Let’s use an example of an email. In general, it can be hard to keep our inbox in check. We get many e-mails every day and a big problem is spam. In fact, it would be an even bigger problem if e-mail providers, like Gmail, were not so effective at keeping spam out of our inboxes. But how do they know whether a particular e-mail is a spam or not? This is our first example of a machine learning problem.

Every machine learning problem has a data set, which is a collection of data points that help us learn. Your data set will be all the e-mails that are sent over a month. Each data point will be a single e-mail. Whenever you get an e-mail, you can quickly tell whether it’s spam. You might hit a button to label any particular e-mail as spam or not spam. Now you can imagine that each of your data points has one of two labels, spam or not spam. In the future, you will keep getting emails, but you won’t know in advance which label it should have, spam or not spam. The machine learning problem is to predict whether a new label for a new email is spam or not spam. This means that we want to predict the label of the next email. If our machine learning algorithm works, it can put all the spam in a separate folder. This spam problem is an example of supervised learning. You can imagine a teacher, or supervisor, telling you the label of each data point, which is whether each e-mail is spam or not spam. The supervisor might be able to tell us whether the labels we predicted were correct.

So what is unsupervised learning? Let’s try another example of a machine learning problem. Imagine you are looking at your emails, and realize you got too many emails. It would be helpful if you could read all the emails that are on the same topic at the same time. So, you might run a machine learning algorithm that groups together similar emails. After you have run your machine learning algorithm, you find that there are natural groups of emails in your inbox. This is an example of an unsupervised learning problem. You did not have any labels because no labels were made for each email, which means there is no supervisor.

#reinforcement-learning #supervised-learning #unsupervised-learning #k-means-clustering #machine-learning