jerripothula/Machine-learning

1import streamlit as st2 3def main():4    st.title("Supervised Machine Learning")5 6    st.write("""7    Supervised machine learning is a fundamental approach for machine learning and artificial intelligence. 8    It involves training a model using labeled data, where each input comes with a corresponding correct output. 9    The process is like a teacher guiding a student—hence the term “supervised” learning. In this article, we’ll explore the key components of supervised learning, 10    the different types of supervised machine learning algorithms used, and some practical examples of how it works.11    """)12 13    st.header("Key Components of Supervised Learning")14 15    st.subheader("1. Training Data")16    st.write("""17    The model is provided with a training dataset that includes input data (features) and corresponding output data (labels or target variables).18    """)19 20    st.subheader("2. Learning Process")21    st.write("""22    The algorithm processes the training data, learning the relationships between the input features and the output labels. 23    This is achieved by adjusting the model’s parameters to minimize the difference between its predictions and the actual labels.24    """)25 26    st.subheader("3. Training Phase")27    st.write("""28    Training phase involves feeding the algorithm labeled data, where each data point is paired with its correct output. 29    The algorithm learns to identify patterns and relationships between the input and output data.30    """)31 32    st.subheader("4. Testing Phase")33    st.write("""34    Testing phase involves feeding the algorithm new, unseen data and evaluating its ability to predict the correct output based on the learned patterns.35    """)36 37    st.image("./training_testing.png", caption="Supervised Learning Process", use_container_width =True)38 39if __name__ == "__main__":40    main()41