Hierarchical clustering is often used with heatmaps and with machine learning type stuff. It’s no big deal, though, and based on just a few simple concepts.
If you want to draw a heatmap using R : https://statquest.org/2017/06/20/stat…
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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
1. Understanding the many different techniques used to discover patterns in a set of data
2. In-depth understanding of the K-Means algorithm
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
Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources
Everyone makes mistakes, not just beginners, but even professionals. This article goes over a dozen common mistakes that Java newbies and newcomers make and how to avoid them. Have you or your colleagues made any of these common Java mistakes early in your career?
Everyone makes mistakes, not only learners or beginners but professionals. As a programming course, the CodeGym team often collects mistakes of newbies to improve our auto validator. This time we decided to interview experienced programmers about mistakes in Java they made closer to their careers start or noticed them among their young colleagues.
We collected their answers and compiled this list of dozen popular mistakes Java beginners make. The order of errors is random and does not carry any special meaning.
#java #learn-java #java-programming #beginners #beginners-to-coding #learning-to-code #learn-to-code #learn-to-code-java
Java is has been one of the most popular programming languages for decades. The number of specialists who want to become proficient in Java is rapidly growing. Because the competition is fierce, it’s no longer enough to just be a good Java developer — you need to acquire deep knowledge and get familiar with many concepts to be ahead of the competition.
If you’re the one who’s stuck asking yourself “What should I learn to stand out as a Java developer?”, this blog post can help you figure things out.
#java #learn-java #java-development-resources #learning-to-code #learn-to-code #beginners #beginners-guide #learn-to-code-java
ML is type of AI
AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.
AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to
#ml-guide-on-gcp #ml-for-beginners-on-gcp #beginner-ml-guide-on-gcp #machine-learning #machine-learning-gcp #deep learning