Prerequisites

  • Basic Python knowledge
  • NumPy and Pandas installed
  • Understanding of basic statistics

Step 1: Understanding Machine Learning

Machine learning is a subset of artificial intelligence that focuses on building systems that can learn from and make decisions based on data.

Types of Machine Learning

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Step 2: Your First ML Model

Let’s create a simple supervised learning model using scikit-learn:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Generate sample data
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=42)

# Split the data
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 = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate the model
score = model.score(X_test, y_test)
print(f"Model accuracy: {score:.2f}")

Step 3: Model Evaluation

Understanding how to evaluate your model is crucial:

  1. Accuracy
  2. Precision
  3. Recall
  4. F1 Score

Next Steps

  • Explore different algorithms
  • Learn about feature engineering
  • Understand model optimization
  • Practice with real-world datasets