Foundations of Machine Learning
Designed for absolute beginners, this machine learning course demystifies AI. You'll learn core concepts like training, classification, and regression, then build your first predictive model with Python and scikit-learn.
Created by Route Radiant
@routeradiant · view creatorPreviewing this curriculum
Enroll free to unlock every interactive mission, your progress tracker, and a verified certificate when you finish.
Full Curriculum
1What is Machine Learning?
Establish a foundational vocabulary and map the landscape of machine learning tasks and tools.
5 missions
What is Machine Learning?
Establish a foundational vocabulary and map the landscape of machine learning tasks and tools.
2Core Concepts of Model Training
Dive into the mechanics of how a machine learning model actually learns from data.
5 missions
Core Concepts of Model Training
Dive into the mechanics of how a machine learning model actually learns from data.
3Your First Model: Regression
Build and evaluate your first machine learning model to predict continuous numerical values.
5 missions
Your First Model: Regression
Build and evaluate your first machine learning model to predict continuous numerical values.
4Your Second Model: Classification
Learn to build models that predict categories and evaluate their performance with new metrics.
5 missions
Your Second Model: Classification
Learn to build models that predict categories and evaluate their performance with new metrics.
5Project: Predicting Customer Churn
Apply your skills to a complete project, building a classifier to predict customer churn from a real-world dataset.
5 missions
Project: Predicting Customer Churn
Apply your skills to a complete project, building a classifier to predict customer churn from a real-world dataset.
6Improving and Generalizing Your Model
Learn crucial techniques for diagnosing model problems and improving their real-world performance.
5 missions
Improving and Generalizing Your Model
Learn crucial techniques for diagnosing model problems and improving their real-world performance.
What You'll Learn
- Distinguish AI, ML, and Deep Learning
- Identify ML Task Types
- Map the Machine Learning Workflow
- Set Up Your Python Environment
- Load a Dataset with Pandas
- Define Features and Labels
- Explain the Concept of a 'Model'
- Split Data for Training and Testing
- Understand the Role of a Loss Function
- Visualize the Training Process
- Frame a Regression Problem
- Train a Linear Regression Model
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Mastery Awaits
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