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Machine learning is a subfield of artificial intelligence (AI) that involves teaching machines to learn patterns and make predictions based on data without being explicitly programmed. The goal of machine learning is to enable machines to learn and adapt from experience and improve their performance over time. Machine learning has become increasingly important in everyday life, powering many applications that we use daily. Examples of these include recommendation systems on social media platforms and online retailers, speech recognition systems like Siri and Alexa, and fraud detection systems in banks and financial institutions. The main components of machine learning are data preparation, model creation, and model deployment. In the data preparation phase, data is collected, cleaned, and organized for analysis. In the model creation phase, algorithms are developed and trained using the data to make predictions or identify patterns. Finally, in the model deployment phase, the model is put into production and used to make predictions on new data. There are three main subfields of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a machine learning model using labeled data, where the correct output is known. This type of learning is used for tasks like image classification and spam filtering. Unsupervised learning involves training a model using unlabeled data, where the goal is to identify patterns or relationships in the data. This type of learning is used for tasks like clustering and anomaly detection. Reinforcement learning involves training a model to make decisions based on a reward signal. This type of learning is used for tasks like game playing and robotics. To learn more about machine learning, readers can consult introductory books like "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron and "Python Machine Learning" by Sebastian Raschka. There are also many online courses and tutorials available, such as the Machine Learning course on Coursera by Andrew Ng and the TensorFlow Tutorial on the TensorFlow website.