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  Cricket isn’t just about bat and ball anymore—it’s powered by  data science  and  artificial intelligence (AI) ! From  deep insights  into player performance to  match predictions , AI is changing how teams plan their  game strategies . The  IPL (Indian Premier League)  is at the forefront of this tech revolution, using AI to enhance every aspect of the game. But how exactly does this work? Let’s dive in! Role of Data Science and AI in Modern Cricket Cricket has become a game of numbers.  Data Science in Cricket  helps teams analyze vast amounts of data, from  cricket analytics  to player fitness. Teams rely on  wearable sensors  to track player movements, heart rate, and even fatigue levels. With this data, coaches can make informed decisions, reducing injuries and improving performance. Player Performance Analysis – AI’s Winning Formula Ever wondered how teams decide which player to retain or buy? AI pla...

  Machine learning Life cycle Machine learning has given the computer systems the abilities to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project. Machine learning life cycle involves seven major steps, which are given below: Gathering Data Data preparation Data Wrangling Analyze  Data Train the model Test the model Deployment 1. Gathering Data: Data Gathering is the first step of the machine learning life cycle. The goal of this step is to identify and obtain all data-related problems. In this step, we need to identify the different data sources, as data can be collected from various sources such as  files ,  database ,  internet , or  mobile devices . It is one of ...

  Applications of Machine learning 1. Image Recognition: Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is,  Automatic friend tagging suggestion : Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's  face detection  and  recognition algorithm . It is based on the Facebook project named " Deep Face ," which is responsible for face recognition and person identification in the picture. 2. Speech Recognition While using Google, we get an option of " Search by voice ," it comes under speech recognition, and it's a popular application of machine learning. Speech recognition is a process of converting voice instructions into text,...

  Explore the Data Analytics Market Report 2025 The 2025 Data Analytics Market Report analyzes the growing sector and its role in enabling businesses to leverage data for strategic decision-making, operational efficiency, and customer insights. The demand for data-driven solutions expands across industries such as finance, healthcare, retail, and manufacturing with organizations adopting advanced analytics solutions powered by AI, ML, and big data technologies. The key trends include the rise of predictive analytics, the integration of AI-powered analytics platforms, and the growing importance of real-time data processing. Further, the adoption of self-service analytics tools democratizes data access and enables business users to derive insights without relying on IT departments. The report also explores the investment patterns, firmographic data, and innovative startups driving the evolution of data analytics. This data analytics report serves as a reference for stakeholders withi...

  Data science Applications  Top 10 Applications of Data Science Data science has permeated various sectors, bringing transformative changes and insights. Here are the top 10 applications and data science applications examples: 1) Healthcare and Medicine:  Predictive analytics aids in forecasting patient admissions, personalized medicine, disease outbreak detection, and improving patient outcomes through data-driven decision-making. 2) E-commerce and Retail:  Recommendation systems, demand forecasting, customer segmentation, and price optimization are key areas where data science enhances user experience and boosts sales. 3) Banking and Finance:  Fraud detection, risk management, algorithmic trading, credit scoring, and customer segmentation are pivotal applications that optimize financial operations and mitigate risks. 4) Manufacturing and Supply Chain:  Predictive maintenance, inventory optimization, supply chain analytics, and demand forecasting are leve...