What is Machine Learning?
Get a comprehensive introduction to machine learning concepts, types, and applications.
Build foundational understanding through interactive examples and real-world scenarios.
What You'll Learn:
- Machine Learning definition
- Types of ML problems
- How ML works
- AI vs ML vs Deep Learning
- Real-world applications
Mulai Belajar
Supervised vs Unsupervised Learning
Understand the fundamental differences between supervised and unsupervised learning
through interactive visualizations and practical examples.
What You'll Learn:
- Supervised learning concepts
- Unsupervised learning concepts
- Semi-supervised learning
- Interactive examples
- When to use each approach
Mulai Belajar
Understanding Overfitting
Learn about one of the most important concepts in machine learning: overfitting.
Understand how to detect and prevent it through interactive examples.
What You'll Learn:
- What is overfitting?
- Bias vs variance tradeoff
- Cross-validation techniques
- Regularization methods
- Model complexity management
Mulai Belajar
Train-Test Split
Master the fundamentals of data splitting for machine learning model evaluation.
Learn best practices for creating reliable train/validation/test sets.
What You'll Learn:
- Why split data?
- Train/validation/test sets
- Stratified sampling
- Time series considerations
- Cross-validation strategies
Mulai Belajar
Supervised Learning dengan NumPy
Practice supervised learning fundamentals using NumPy. Learn the complete workflow
from data splitting to model training and prediction with interactive visualizations.
What You'll Learn:
- Train-test split with NumPy
- Linear regression training
- Model prediction (np.poly1d)
- Model evaluation (MSE)
- Interactive visualizations
Mulai Belajar
K-Means Clustering
Learn K-Means through a hands-on interactive simulator. Add points, set the number
of clusters, run iterative updates, and observe how centroids move toward natural
groupings in data.
What You'll Learn:
- K-Means clustering intuition
- Interactive data point exploration
- Choosing the number of clusters (K)
- Centroid updates and convergence
- Clustering metrics overview
Mulai Belajar
K-Means Implementation with Python
Move from concept to code by implementing K-Means in Python. Study core functions,
evaluate clustering quality, compare with scikit-learn, and apply the workflow to a
customer segmentation example.
What You'll Learn:
- Implementing K-Means from scratch
- Centroid assignment and update logic
- Inertia (WCSS) model evaluation
- scikit-learn K-Means comparison
- Python-based customer segmentation case
Mulai Belajar
K-Means Case Study
Apply K-Means to real business problems! Analyze retail customer data to create
actionable marketing segments. See how unsupervised learning drives business
decisions.
What You'll Learn:
- Real retail customer dataset
- RFM analysis (Recency, Frequency, Monetary)
- Business-driven segmentation
- Actionable marketing insights
- ROI calculation and recommendations
Mulai Belajar
Project 2: Machine Learning
Yuk kerjakan project 2 machine learning! Terapkan semua yang sudah dipelajari dalam satu project komprehensif.
Akses Project di Google Colab