What is Machine Learning?
Machine learning is a field of computer science where algorithms are designed to learn from data and make predictions or decisions, improving their performance over time without being explicitly programmed for every task. Instead of following a fixed set of instructions, a machine learning algorithm identifies patterns in data and adapts its behavior based on experience. This approach enables computers to solve complex problems that are difficult or impractical to program manually, such as recognizing faces in photos or predicting customer preferences.
You encounter machine learning in many aspects of daily life. For example, recommendation systems on streaming platforms suggest movies or songs based on your past choices and those of similar users. In image recognition, machine learning enables your phone to automatically categorize photos by identifying objects, people, or scenes. Other applications include spam detection in email, language translation, and even medical diagnosis, where algorithms assist doctors by analyzing patterns in patient data. These examples illustrate how machine learning leverages vast amounts of data to deliver smarter, more personalized, and more efficient solutions.
1234567891011121314151617181920212223import matplotlib.pyplot as plt # A simple dataset: pairs of input and output values # Each tuple represents (hours studied, exam score) data = [ (2, 65), (4, 70), (6, 78), (8, 88), (10, 95) ] # Extract hours and scores into separate lists hours = [item[0] for item in data] scores = [item[1] for item in data] # Plot the data plt.plot(hours, scores, color='blue') plt.scatter(hours, scores, color='blue') plt.xlabel('Hours Studied') plt.ylabel('Exam Score') plt.title('Relationship Between Hours Studied and Exam Score') plt.show()
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Machine learning is a field of computer science where algorithms are designed to learn from data and make predictions or decisions, improving their performance over time without being explicitly programmed for every task. Instead of following a fixed set of instructions, a machine learning algorithm identifies patterns in data and adapts its behavior based on experience. This approach enables computers to solve complex problems that are difficult or impractical to program manually, such as recognizing faces in photos or predicting customer preferences.
You encounter machine learning in many aspects of daily life. For example, recommendation systems on streaming platforms suggest movies or songs based on your past choices and those of similar users. In image recognition, machine learning enables your phone to automatically categorize photos by identifying objects, people, or scenes. Other applications include spam detection in email, language translation, and even medical diagnosis, where algorithms assist doctors by analyzing patterns in patient data. These examples illustrate how machine learning leverages vast amounts of data to deliver smarter, more personalized, and more efficient solutions.
1234567891011121314151617181920212223import matplotlib.pyplot as plt # A simple dataset: pairs of input and output values # Each tuple represents (hours studied, exam score) data = [ (2, 65), (4, 70), (6, 78), (8, 88), (10, 95) ] # Extract hours and scores into separate lists hours = [item[0] for item in data] scores = [item[1] for item in data] # Plot the data plt.plot(hours, scores, color='blue') plt.scatter(hours, scores, color='blue') plt.xlabel('Hours Studied') plt.ylabel('Exam Score') plt.title('Relationship Between Hours Studied and Exam Score') plt.show()
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