Tutorials

Getting Started with AI: A Step-by-Step Guide Learn how to begin your journey in artificial intelligence with this comprehensive tutorial. Prerequisites Before starting, you’ll need: Basic programming knowledge Python installed on your system A code editor Step 1: Setting Up Your Environment Install Python 3.8 or higher Set up a virtual environment Install required packages python -m venv ai_env source ai_env/bin/activate # On Windows: ai_env\Scripts\activate pip install numpy pandas scikit-learn Step 2: Your First AI Program Let’s create a simple machine learning model: ...

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 Supervised Learning Unsupervised Learning 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: ...