Welcome to Day 14 of #DailyAIWizard, where we’re classifying data with the magic of Decision Trees and Random Forests! I’m Anastasia, your thrilled AI guide, and today we’ll explore these powerful ML techniques for classification tasks like predicting customer churn. Sophia joins me with a magical demo using Python and scikit-learn to build a Random Forest—it’s spellbinding! Whether you’re new to AI or following along from Days 1–13, this 27-minute lesson will ignite your curiosity. Let’s make AI magic together!
Task of the Day: Build a Random Forest model using Python (like in the demo) and share your accuracy in the comments! Let’s see your magical results!
Subscribe for Daily Lessons: Don’t miss Day 15, where we’ll explore Support Vector Machines Basics. Hit the bell to stay updated!
Watch Previous Lessons:
Day 1: What is AI?
Day 2: Types of AI
Day 3: Machine Learning vs. Deep Learning vs. AI
Day 4: How Does Machine Learning Work?
Day 5: Supervised Learning Explained
Day 6: Unsupervised Learning Explained
Day 7: Reinforcement Learning Basics
Day 8: Data in AI: Why It Matters
Day 9: Features and Labels in Machine Learning
Day 10: Training, Testing, and Validation Data
Day 11: Algorithms in Machine Learning (Overview)
Day 12: Linear Regression Basics
Day 13: Logistic Regression for Classification
#AIForBeginners #DecisionTrees #RandomForests #MachineLearning #WisdomAcademyAI #PythonDemo #ScikitLearnDemo #treemagic #dailyaiwizard
