Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. (Wikipedia)
Machine learning [https://gum.co/pGjwd] is changing the world. Google uses machine learning to suggest search results to users. Netflix uses it to recommend movies for you to watch. Facebook uses machine learning to suggest people you may know. Machine learning has never been more important. At the same time, understanding machine learning is hard. The field is full of jargon. And the number of different ML algorithms grows each year. This article will introduce you to the fundamental concepts
Hello guys, you may know that Machine Learning and Artificial Intelligence have become more and more important in this increasingly digital world. They are now providing a competitive edge to businesses like NetFlix's Movie recommendations. If you have just started in this field and looking for what to learn then I am going to share 5 essential Machine learning algorithms you can learn as a beginner. These essential algorithms form the basis of most common Machine learning projects and having a good knowledge of them will not only help you to understand the project and model quickly but also to change them as per your need. Machine learning by a simple word is the science or the field of making the computer learn like a human by feeding it with the data and without being programmed and it separate into two categories the first one is classification problems which the machine needs to classify between two objects or more like between human and animal and the second is regression problems which the machine need to produce an output based on a previous data.
Natural Language Processing (NLP) is the area of research in Artificial Intelligence focused on processing and using Text and Speech data to create smart machines and create insights. One of nowadays most interesting NLP application is creating machines able to discuss with humans about complex topics. IBM Project Debater represents so far one of the most successful approaches in this area. All of these preprocessing techniques can be easily applied to different types of texts using standard Python NLP libraries such as NLTK and Spacy. Additionally, in order to extrapolate the language syntax and structure of our text, we can make use of techniques such as Parts of Speech (POS) Tagging and Shallow Parsing (Figure 1).
Though there are many library implementations of the k-means algorithm in Python, I decided to use only Numpy in order to provide an instructive approach. Numpy is a popular library in Python used for numerical computations. We first create a class called Kmeans and pass a single constructor argumentk to it. This argument is a hyperparameter. Hyperparameters are parameters that are set by the user before training the machine learning algorithm.
You've developed a platform that's gaining significant customer traction and enabling you to collect vast amounts of transaction and user data. Word gets out about your software, you acquire more users and feature requests start rolling in. As you develop and deliver those new features, you engage more users and collect even more data! There's tremendous value in that data, but limited thinking may be limiting your ability to mine it for the insights you need to further improve your product or even develop new ones that better meet the needs of your user base. Perhaps you've only gotten as far as creating simple plots and histograms around events, fault detection and other simple rules-based alerting and reporting.
Let’s say you want to classify hundreds (or thousands) of documents based on their content and topics, or you wish to group together different images for some reason. Or what’s even more, let’s think you have that same data already classified but you want to challenge that labeling. You want to know if that data categorization makes sense or not, or can be improved.
Outlier Detection is also known as anomaly detection, noise detection, deviation detection, or exception mining. There is no universally accepted definition. An early definition by (Grubbs, 1969) is: An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs. An observation which appears to be inconsistent with the remainder of that set of data. A list of applications that utilize outlier detection according to (Hodge, V.J. and Austin, J., 2014) is: This is analogous to unsupervised clustering.
Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. Many kinds of research have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is K-Means clustering algorithm. Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is.
This article the idea of building a new, data driven classification of companies based on their financials, instead of the type of business they do. The premise of this study is based on the idea that great stocks to buy would be superior among their peers. In this context, I define a peer group as a group of companies that have similar financial structure (e.g. If you haven't read an introduction, I suggest you read my preface to this study here: In the previous article, I've selected 5 dimensions to define financial structure of a company: I've also made sure that these dimensions are independent. This is an important consideration since I want to measure how a company is doing at each independent front regardless of its performance elsewhere.