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## Machine Learning Tutorial !(#) Machine Learning is a branch of Artificial Intelligence (AI) that enables computer systems to automatically learn and improve their performance by using data and algorithms. Machine Learning allows machines to make decisions and predictions based on experience (data). Machine Learning has been widely applied in many fields, including recommendation systems, image recognition, speech recognition, financial analysis, and more. For example, through Machine Learning, cars can learn how to recognize traffic signs, pedestrians, and obstacles, enabling self-driving capabilities. * * * ## The Difference Between Machine Learning and Traditional Programming | Traditional Programming | Machine Learning | | --- | --- | | Programmers write explicit rules | Computers learn rules from data | | Suitable for problems with clear definitions and explicit rules | Suitable for complex problems where rules are hard to define | | Example: Writing a calculator program | Example: Writing a spam email detection program | !(#) * * * ## The Three Key Components of Machine Learning Machine Learning consists of three basic components: ### 1. Data Data is the fuel for Machine Learning. The higher the quality and quantity of data, the better the model usually learns. * **Training Data**: Data used to teach the model * **Test Data**: Data used to evaluate the model's performance * **Real Data**: New data the model encounters in real-world applications ### 2. Algorithm An algorithm is the method by which Machine Learning learns. Different algorithms are suited to different types of problems. * **Supervised Learning**: Learning with labeled data * **Unsupervised Learning**: Learning without labeled data—finding patterns on its own * **Reinforcement Learning**: Learning through trial and error and rewards ### 3. Model A model is the result of learning, just like knowledge acquired by a student. * **Training Process**: The algorithm learns patterns from the data * **Inference Process**: Using the learned patterns to make predictions * * * ## Example Next, let’s understand the basic workflow of Machine Learning through a simple example. We’ll use Python to create a simple linear regression model to predict house prices. ## Example # Import necessary libraries import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression import seaborn as sns # Set the chart style to make it look nicer sns.set_style("whitegrid") # -------------------------- Set Chinese font start -------------------------- plt.rcParams['font.sans-serif']=[ # Windows priority 'SimHei','Microsoft YaHei', # macOS priority 'PingFang SC','Heiti TC', # Linux priority 'WenQuanYi Micro Hei','DejaVu Sans' ] # Fix the issue of negative signs displayed as squares plt.rcParams['axes.unicode_minus']=False # -------------------------- Set Chinese font end -------------------------- # 1. Prepare data # Assume we have data on house area and corresponding prices # House area (square meters) house_sizes = np.array([50,60,70,80,90,100,110,120]).reshape(-1,1) # House price (ten thousand yuan) house_prices = np.array([150,180,210,240,270,300,330,360]) # 2. Create and train the model # Create a linear regression model model = LinearRegression() # Train the model using the data (learning the relationship between area and price) model.fit(house_sizes, house_prices) # 3. Use the model for prediction # Predict the price of an 85-square-meter house predicted_price = model.predict([]) print(f"Predicted price for an 85-square-meter house: {predicted_price:.2f} ten thousand yuan") # 4. Visualize the results plt.scatter(house_sizes, house_prices, color='blue', label='Actual data') plt.plot(house_sizes, model.predict(house_sizes), color='red', label='Prediction line') plt.scatter(, predicted_price, color='green', s=100, label='Prediction point') plt.xlabel('House Area (square meters)') plt.ylabel('House Price (ten thousand yuan)') plt.title(' Machine Learning Test -- Relationship Between House Area and Price') plt.legend() plt.grid(True) plt.show() **Output Result:** Predicted price for an 85-square-meter house: 255.00 ten thousand yuan This example demonstrates the basic workflow of Machine Learning: 1. Prepare data (house area and price) 2. Choose an algorithm (linear regression) 3. Train the model (let the computer learn the relationship between area and price) 4. Use the model for prediction (predict the price of a new area) The output graph is as follows: !(#)