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House Price Prediction
Use historical property data to build a model that estimates house prices.
Machine Learning
Teach computers.
Let data reveal patterns.
Understand the fundamentals of machine learning, how models learn from data and how these techniques are used to solve real-world problems.
The Fundamentals
What is Machine Learning?
Machine learning is a branch of artificial intelligence where computers learn patterns from data and use those patterns to make predictions, classifications or decisions.
Instead of explicitly programming every possible rule, developers provide data and an appropriate learning algorithm so a model can identify useful relationships.
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A simple example
Input Data
House size, location, bedrooms and previous prices
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Machine Learning Model
Learns relationships between the data
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Output
Predicts the price of a new house
Explore Machine Learning
Major areas to understand
Machine learning contains several approaches and techniques. These concepts form a useful foundation for further study.
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Supervised Learning
Learn how machine learning models use labeled examples to make predictions or classifications.
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Unsupervised Learning
Understand how algorithms can discover patterns, groups and structures in data without labeled answers.
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Neural Networks
Explore the basic idea behind neural networks and how they are used for complex learning tasks.
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Prediction
Understand how machine learning can use historical data to estimate future outcomes.
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Classification
Learn how models can assign data to categories such as spam or not spam.
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Model Training
Understand how data, algorithms and evaluation work together to create useful machine learning models.
Key Vocabulary
Concepts you will hear everywhere.
Before moving into advanced machine learning, become comfortable with the terminology used to describe data, models and predictions.
✦Dataset
✦Features
✦Labels
✦Training Data
✦Testing Data
✦Model
✦Prediction
✦Accuracy
Real-World Applications
Where is machine learning used?
Machine learning is already used in many systems people interact with every day.
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Spam Detection
Train a model to distinguish between unwanted messages and legitimate messages.
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Recommendation Systems
Use user behavior and preferences to recommend products, movies or other content.
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Fraud Detection
Analyze transaction patterns to identify potentially unusual or fraudulent activity.
Machine Learning Roadmap
From beginner to practical projects.
Machine learning becomes easier when you build the foundation step by step instead of trying to learn every algorithm at once.
01
Learn Python
Build a foundation in Python because it is widely used for data science and machine learning.
02
Understand Data
Learn how to collect, clean, explore and prepare datasets.
03
Learn the Fundamentals
Understand concepts such as features, labels, training, testing and model evaluation.
04
Study Algorithms
Explore regression, classification, clustering, decision trees and other fundamental algorithms.
05
Build Projects
Apply your knowledge to real datasets and create practical machine learning projects.
06
Move to Advanced AI
Gradually explore neural networks, deep learning, natural language processing and computer vision.
Understand the Relationship
AI, Machine Learning and Deep Learning
These terms are related but they are not interchangeable.
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Artificial Intelligence
The broad field of creating systems that can perform tasks associated with intelligent behavior.
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Machine Learning
A major approach within AI where systems learn patterns from data.
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Deep Learning
A specialized area of machine learning that commonly uses multi-layer neural networks.
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Good models need good data.
Machine learning systems depend heavily on the quality and suitability of their data. Understanding data quality, evaluation and potential bias is just as important as understanding algorithms.