In machine learning, categorical data refers to variables that represent categories rather than numeric values—such as gender, education level, or product rating. While many algorithms can process numerical data effectively, they cannot inherently understand categorical text values. Feeding raw categorical data into models like decision trees, logistic regression, or neural networks often leads to errors ...
Understanding the architecture of Tableau is essential for anyone looking to leverage its full potential—whether you’re a developer creating dashboards, an analyst exploring data, or an IT administrator managing deployments. Tableau’s architecture underpins its powerful ability to connect, process, visualize, and share data across organizations. By learning how Tableau’s components interact—both on the desktop and ...
In today’s fast-paced digital world, organizations generate massive streams of data from sources like social media, IoT devices, web applications, and financial transactions. The need to analyze and act on this data in real time has given rise to powerful stream processing frameworks. One of the most prominent among them is Apache Spark Streaming. As ...
The demand for skilled data analysts is soaring as businesses increasingly rely on data-driven decision-making to gain a competitive edge. Across industries like finance, healthcare, e-commerce, and technology, organizations are seeking professionals who can interpret complex data and uncover actionable insights. To build a strong foundation in this field, a well-structured course syllabus is essential. ...
Computational Learning Theory (CLT) is a branch of machine learning and theoretical computer science that studies the mathematical principles behind learning algorithms. It focuses on defining how efficiently an algorithm can learn patterns from data and generalize to unseen inputs. CLT provides a formal framework for evaluating machine learning models, answering key questions such as: ...
Data science is a multidisciplinary field that focuses on extracting meaningful insights from structured and unstructured data. By leveraging statistics, machine learning, artificial intelligence, and data engineering, it helps organizations make data-driven decisions, optimize operations, and drive innovation. The demand for data science professionals has grown exponentially across industries such as finance, healthcare, e-commerce, and ...
The demand for data science skills is rapidly increasing as industries rely on data-driven decision-making to stay competitive. Companies across technology, healthcare, finance, and e-commerce are actively seeking data scientists, analysts, and AI specialists to extract valuable insights from vast datasets. Choosing the right data science course is crucial for skill development, career growth, and ...
Artificial Intelligence (AI) algorithms are the core mechanisms that enable machines to process information, learn patterns, and make decisions without human intervention. These algorithms form the foundation of AI systems, allowing computers to recognize speech, classify images, analyze data, and predict outcomes with accuracy and efficiency. AI algorithms play a crucial role in automation, problem-solving, ...
Data mining is the process of extracting useful patterns, relationships, and insights from large datasets using statistical techniques, machine learning algorithms, and database systems. It plays a crucial role in modern industries by helping organizations uncover hidden trends that drive data-driven decision-making. In today’s digital world, businesses generate vast amounts of data from customer interactions, ...
Box plots, also known as box-and-whisker plots, are a fundamental tool in data visualization and statistical analysis. They provide a compact summary of data distribution, helping analysts understand key aspects such as spread, central tendency, and potential outliers in a dataset. One of the biggest advantages of box plots is their ability to visualize data ...
Team Applied AI
Abhimanyu Saxena
