AI models are at the heart of modern technological advancements, powering applications from chatbots and virtual assistants to self-driving cars and medical diagnosis systems. These models enable machines to learn from data and make intelligent decisions, transforming industries by automating tasks and generating insights that were once beyond human capability. What is an AI Model? ...
In machine learning, unsupervised learning deals with finding hidden patterns or relationships within data without labeled outputs. One important technique in unsupervised learning is association rule learning, which focuses on discovering interesting relationships between variables in large datasets. A common use of association rules is in market basket analysis, where retailers analyze the purchase patterns ...
Big Data Analytics is becoming essential as organizations aim to remain competitive in a data-centric world. Businesses collect vast amounts of structured and unstructured data from multiple sources, including customer interactions, market trends, and internal operations. Analytics tools process these datasets to uncover actionable insights that drive better decision-making. With companies increasingly relying on data ...
In machine learning, classification problems are essential for making decisions, like predicting whether an email is spam or not. One of the simplest and most popular algorithms for classification is the Naive Bayes classifier. This algorithm is widely used because of its simplicity, speed, and efficiency, even when dealing with large datasets. The Naive Bayes ...
Data transformation is essential for converting raw data into actionable formats that businesses can use effectively. It ensures data consistency and usability, playing a vital role in analytics, operations, and decision-making. It ensures that data is clean and ready to be used by machine learning algorithms. In a data-driven world, transformation helps businesses unlock the ...
Reasoning is a key component of artificial intelligence (AI), allowing systems to make logical inferences and solve problems. Two essential strategies for reasoning are forward chaining and backward chaining. These approaches are commonly used in AI systems to derive conclusions or find solutions based on a set of facts or rules. Both forward and backward ...
Machine learning (ML) continues to shape industries like healthcare, finance, and e-commerce, making it a must-have skill for tech professionals. With growing demand—job postings for ML engineers increasing by over 30% annually—2025 presents vast opportunities for those mastering this field. This roadmap offers a step-by-step guide, covering everything from foundational concepts to advanced techniques. Whether ...
Statistics, a fundamental tool in data analysis, is divided into two main branches: descriptive statistics and inferential statistics. Descriptive statistics summarizes raw data through measures like mean, median, and standard deviation, offering a clear picture of what the data reveals. However, this method only describes the observed dataset without extending beyond it. Inferential statistics goes ...
Machine learning models are often used to solve supervised learning tasks, particularly classification problems, where the goal is to assign data points to specific categories or classes. However, as datasets grow larger with more features, it becomes challenging for models to process the data effectively. This is where dimensionality reduction techniques like Linear Discriminant Analysis ...
Support Vector Machine (SVM) is a widely-used supervised learning algorithm for classification and regression tasks in machine learning. Known for its robustness and ability to handle both linear and non-linear data, SVM has applications in fields ranging from healthcare to finance. Whether it’s classifying images or detecting fraud, SVM offers a powerful solution by finding ...
Mayank Gupta
Team Applied AI
Abhimanyu Saxena