🔥What Is Machine Learning ? | Machine Learning Explained in 60 Seconds #Shorts #simplilearn

In this video on What Is Machine Learning, we'll explore the fascinating world of machine learning and explain it in the simplest way possible. Imagine you have a toy robot that learns from pictures of cats and dogs. Every time you show the robot a picture and tell it what animal it is, the robot starts to remember and gets better at recognizing them on its own. This process is similar to how machine learning works! Machine learning involves teaching computers to learn from data, allowing them to make predictions and decisions without being explicitly programmed for each task. By feeding the computer lots of examples, it can recognize patterns and improve over time. This technology is behind many of the smart applications we use today, from voice assistants to recommendation systems. Join us as we break down the basics of machine learning, discuss its importance, and show you how it shapes the technology we use every day. Whether you're a complete beginner or looking to refresh your understanding, this video will give you a clear and concise overview of machine learning. #AI #ML #AiEngineers #MLEngineers #ArtificialIntelligence #MachineLearning #2024 #Simplilearn #BestCourses #OnlineCourses #Shorts #YTShorts #DM #Simplilearn ✅What is meant by machine learning? Machine learning is a method of teaching computers to learn from data and make decisions or predictions without being explicitly programmed for each specific task. It involves using algorithms to identify patterns and improve performance over time. ✅What are examples of machine learning? Examples of machine learning include recommendation systems (like those used by Netflix or Amazon), voice assistants (such as Siri or Alexa), image recognition (used in facial recognition software), and autonomous vehicles (self-driving cars). ✅What are the basics of machine learning? The basics of machine learning involve data collection, data preprocessing, choosing a model, training the model with data, testing the model, and making predictions. Key concepts include supervised learning, unsupervised learning, and reinforcement learning.