Machine Learning
Computer ScienceChapter 1 — Introduction to Machine Learning
Lesson 1.4 : Machine Learning Workflow
Reading & Interactive Lesson
Machine Learning Workflow
Reading Material & Notes
Machine Learning Workflow
A machine learning project normally follows a series of steps from problem definition to model deployment.
Major Steps
- Problem Definition: Clearly define the problem.
- Data Collection: Gather relevant data.
- Data Cleaning: Handle missing and incorrect values.
- Data Preparation: Transform data into a suitable format.
- Feature Selection: Select useful input features.
- Model Training: Train a machine learning algorithm.
- Model Evaluation: Measure model performance.
- Model Deployment: Use the trained model in a real application.
Workflow Example
Problem
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Collect Data
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Clean Data
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Prepare Data
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Train Model
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Evaluate Model
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Deploy ModelCourse Curriculum
10 Chapters • 50 Lessons
Chapter 1 — Introduction to Machine Learning
5 Lessons
Chapter 2 — Types of Machine Learning
5 Lessons
Chapter 3 — Python for Machine Learning
5 Lessons
Chapter 4 — Data Preprocessing
5 Lessons
Chapter 5 — Regression Algorithms
5 Lessons
Chapter 6 — Classification Algorithms
5 Lessons
Chapter 7 — Clustering Algorithms
5 Lessons
Chapter 8 — Model Evaluation
5 Lessons
Chapter 9 — Feature Engineering and Model Improvement
5 Lessons
Chapter 10 — Machine Learning Projects
5 Lessons