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🤖 Part 4: Introduction to Machine Learning with scikit-learn
What Is Machine Learning?
Machine Learning (ML) is a subset of AI where systems learn from data rather than being explicitly programmed. The goal is to make predictions or decisions without human intervention.
🔍 Types of Machine Learning
| Type | Description | Example |
|---|---|---|
| Supervised Learning | Learn from labeled data | Spam detection, housing price prediction |
| Unsupervised Learning | Discover patterns in unlabeled data | Customer segmentation |
| Reinforcement Learning | Learn from actions and rewards | Game playing, robotics |
🧰 Why Use scikit-learn?
scikit-learn is a powerful and beginner-friendly library for:
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Classification
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Regression
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Clustering
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Preprocessing
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Model Evaluation
🛠️ Installing scikit-learn
Install with pip:
pip install scikit-learn
📊 First Machine Learning Project: Iris Classification
📁 Step 1: Load Dataset
from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
print(df.head())
🧪 Step 2: Train/Test Split
from sklearn.model_selection import train_test_split
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
🧠 Step 3: Train a Classifier
Let’s use a Decision Tree:
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
📈 Step 4: Evaluate the Model
from sklearn.metrics import accuracy_score
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy * 100:.2f}%")
📋 Bonus: Make a Prediction
sample = [[5.1, 3.5, 1.4, 0.2]]
prediction = model.predict(sample)
print("Predicted class:", iris.target_names[prediction[0]])
🧭 Practice Challenge
Try using a Logistic Regression model instead of a Decision Tree:
from sklearn.linear_model import LogisticRegression
log_model = LogisticRegression(max_iter=200)
log_model.fit(X_train, y_train)
print("Accuracy:", log_model.score(X_test, y_test))
🎓 What You’ve Learned:
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What machine learning is and its main types
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How to load and prepare data
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Training and evaluating a simple ML model using scikit-learn
🧭 What’s Next?
In Part 5, we’ll move into Deep Learning and explore how to build Neural Networks using TensorFlow and Keras.