Cluster analysis (or clustering) is a data analysis technique that explores and groups a set of vectors (or data points) in such a way that vectors in the same cluster are more similar to one another than to those in other clusters. Clustering algorithms are widely used in numerous applications, e.g., data analysis, pattern recognition, and image processing. This article reviews a new clustering algorithm based on the method of Projection onto Convex Sets (POCS), called POCS-based clustering algorithm. The original paper was introduced in IWIS2022 and the source code has also been released on Github. A convex set is defined as a set of data points in which a line segment connecting any two points x1 and x2 in the set is completely subsumed in this set.
Synthetic data accurately mimics real-world data. It serves as a placeholder for production data in development and testing workflows and is also used to improve the quality of machine learning algorithms. Common use cases revolve around product development/testing, machine learning, data analysis, and data privacy and security. For example, financial institutions use synthetic data to generate reliable market data for algorithmic trading and risk analysis, while healthcare providers use it to analyze patient data without compromising sensitive patient information. Additionally, synthetic data is used in machine learning algorithms to improve performance and accuracy and thus accelerate the development process.
Data Science is a rapidly growing field, and it is easy to get lost in the plethora of information available. If you are a beginner in Data Science, the learning process can be overwhelming. In this post, we will provide you with a step-by-step guide to learn data science effectively. Python is one of the most widely used programming languages in the Data Science industry. Its popularity is due to its simplicity and flexibility. Learning Python is essential for a career in Data Science.
CXOToday has engaged in an exclusive interview with Dr. Abhijit Dasgupta, SP Jain Global school of Management I have had experience as a Visiting Faculty at IIT Bombay, NIFT New Delhi, SPJIMR etc. during the last 25 years while I was having Leadership roles in Corporates in India / overseas. Since 2018, I am a full-time academic. Youthfulness and excitement to learn new things of students and the requirement to stay updated on the topics that I am teaching (among others) keeps me motivated – these are a couple of things that keeps me connected to the education sector. Till date it has been an intellectually satisfying experience for me. My first engagement with Analytics started way back in 2003, when as a CIO, the organization that I was working with during that time, invested in SAS suite of products to generate effective business intelligence.
ML-101 is designed as an intuitive introduction to Machine Learning. The aim of this course is twofold, to build a strong foundation of core machine learning concepts and to allow learners to get hands-on experience of Exploratory Data Analysis and Feature Engineering, two techniques which are undoubtedly important precursors before one even begins to think about training a model. This uniquely designed course will equip the learners with the necessary knowledge before they begin their data science journey.
Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 22,700 associates, it's the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region. Good knowledge of advanced statistical methods (automotive or manufacturing domain will be an added advantage). Mine and analyze data, applying statistical methods as necessary, pertaining to customers' discovery and viewing experiences to identify critical product insights. Ensure that necessary data is captured; analytic needs are well-defined up front and coordinate the analytic needs.
Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage. The bullish outlook is evident in the U.S. Bureau of Labor Statistics' predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations. The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.
AppsFlyer is known for its massive backend production and data pipelines. On any given day, thousands of servers are processing 200 billion events and crunching petabytes of data in our cloud. AppsFlyer runs a variety of data analytics and machine learning algorithms on those billions of mobile events to provide mission-critical information and actionable insights to its customers in a company that is known for its high standards and people-obsessed culture. We're looking for a talented individual with a pioneering spirit to join the R&D Analytics and FinOps team and help ensure R&D makes data-driven decisions. The ideal candidate will have strong technical skills, with a passion for slicing and dicing data.
Welcome to our weekly FiftyOne tips and tricks blog where we give practical pointers for using FiftyOne on topics inspired by discussions in the open source community. This week we'll cover some tips and tricks that will help you accelerate your computer vision workflows using FiftyOne. FiftyOne is an open source machine learning toolset that enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. Ok, let's dive into this week's tips and tricks! One of the great features of PyTorch is the DataLoader class, which makes it easy to efficiently load and process data.
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive. Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks in Python.