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Python is one of the most popular languages for Machine Learning (ML) due to its simplicity and powerful libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. Below is a simple example of training a Linear Regression model using scikit-learn.
# Import required librariesimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegression# Sample datasetdata = {'Experience': [1, 2, 3, 4, 5],'Salary': [45000, 50000, 60000, 65000, 70000]}df = pd.DataFrame(data)# Features and targetX = df[['Experience']]y = df['Salary']# Split into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Create and train the modelmodel = LinearRegression()model.fit(X_train, y_train)# Predict on test datapredictions = model.predict(X_test)print("Predicted Salaries:", predictions)Copied!✕CopyKey Steps in Python ML Syntax
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Python Machine Learning - W3Schools
Learn the basics of machine learning with Python, a step into artificial intelligence. Study statistics, data types, and how to predict outcomes from data sets.
Machine Learning with Python: The Complete Tutorial Hub
Learn machine learning with Python from scratch. Covers NumPy, Pandas, Scikit-learn, TensorFlow & real projects. Beginner to advanced tutorials in one place.
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