18_Basic Machine Learning Introduction.md

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📘 Day 18: Basic Machine Learning Introduction and Scikit-learn Basics

Welcome to Day 18 of the 30 Days of Data Science series! Today, we explore the basics of Machine Learning and an essential library for implementing ML models in Python: Scikit-learn. This session will set the foundation for understanding ML concepts and applying them in practice.

Table of Contents

• 📘 Day 18: Basic Machine Learning Introduction and Scikit-learn Basics
  • 📌 Topics Covered
  • 1️⃣ What is Machine Learning?
    • Types of Machine Learning
  • 2️⃣ Introduction to Scikit-learn
    • Installing Scikit-learn
    • Scikit-learn Basics
  • 3️⃣ Example: Linear Regression with Scikit-learn
  • 🧠 Practice Exercises
  • 🌟 Summary

📌 Topics Covered

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
• Introduction to Scikit-learn, a machine learning library in Python.
• Example: Linear Regression using Scikit-learn.

1️⃣ What is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from data without being explicitly programmed.

Key Concepts:

• Data: ML algorithms are trained using historical data.
• Model: A mathematical representation of the problem to make predictions or decisions.
• Training: The process of feeding data into the model to learn patterns.

Types of Machine Learning

1. Supervised Learning:

   • Input data (features) and output labels (target) are provided.
   • Goal: Learn a mapping from input to output.
   • Examples: Regression, Classification.

2. Unsupervised Learning:

   • Only input data is provided, no output labels.
   • Goal: Discover hidden patterns or groupings.
   • Examples: Clustering, Dimensionality Reduction.

3. Reinforcement Learning:

   • Agents learn by interacting with the environment and receiving feedback (rewards or penalties).
   • Examples: Game playing, Robotics.

2️⃣ Introduction to Scikit-learn

Scikit-learn is a Python library for implementing machine learning algorithms. It provides simple and efficient tools for predictive data analysis.

Key Features:

• Built-in algorithms for supervised and unsupervised learning.
• Tools for model evaluation, preprocessing, and pipeline creation.
• Compatible with other Python libraries like NumPy and pandas.

Installing Scikit-learn

Before using Scikit-learn, ensure it is installed in your environment. Use the following command:

pip install scikit-learn

Scikit-learn Basics

1. Loading a Dataset: Scikit-learn comes with several built-in datasets.

   from sklearn.datasets import load_iris
   
   iris = load_iris()
   print(iris.keys())  # Output: Keys like 'data', 'target', etc.

2. Splitting Data: Use train_test_split to divide data into training and testing sets.

   from sklearn.model_selection import train_test_split
   
   X_train, X_test, y_train, y_test = train_test_split(
       iris.data, iris.target, test_size=0.2, random_state=42
   )

3. Training a Model: Fit a model using the training data.

   from sklearn.ensemble import RandomForestClassifier
   
   clf = RandomForestClassifier()
   clf.fit(X_train, y_train)

4. Making Predictions: Use the trained model to make predictions.

   predictions = clf.predict(X_test)
   print(predictions)

5. Evaluating a Model: Measure accuracy or other metrics.

   from sklearn.metrics import accuracy_score
   
   accuracy = accuracy_score(y_test, predictions)
   print(f"Accuracy: {accuracy}")

3️⃣ Example: Linear Regression with Scikit-learn

Let’s build a Linear Regression model to predict house prices.

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Load the dataset
data = fetch_california_housing()
X, y = data.data, data.target

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")

Output Example:

Mean Squared Error: 0.5401

🧠 Practice Exercises

1. Use the load_wine dataset from Scikit-learn and train a Decision Tree Classifier.
2. Build a K-Means clustering model on synthetic data using Scikit-learn.
3. Experiment with different test sizes in the train_test_split function and observe the impact on performance.

🌟 Summary

• Machine Learning enables systems to learn from data and make predictions.
• Scikit-learn simplifies the implementation of ML algorithms with its tools and datasets.
• Linear Regression is a basic but powerful algorithm to understand supervised learning.

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