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Machine Learning

Computer Science

Chapter 1 — Introduction to Machine Learning

Progress: 4%

Lesson 1.3 : Applications of Machine Learning

Reading & Interactive Lesson

Lesson 1.3 FREE

Applications of Machine Learning

Reading Material & Notes

Applications of Machine Learning

Machine Learning is used in many practical applications where systems need to identify patterns or make predictions from data.

Major Applications

  • Healthcare: Disease risk prediction and medical image analysis.
  • Finance: Fraud detection and credit risk analysis.
  • E-Commerce: Product recommendations.
  • Transportation: Traffic and demand prediction.
  • Education: Student performance prediction.
  • Cyber Security: Detection of unusual activities.
  • Computer Vision: Image and object recognition.
  • Natural Language Processing: Text classification and language applications.

Example

A bank can train a machine learning model using historical transaction data to identify transactions that may require fraud investigation.

Course Curriculum

10 Chapters • 50 Lessons

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Chapter 4 — Data Preprocessing

5 Lessons

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Lesson 4.1 : Understanding Data Preprocessing
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Lesson 4.2 : Handling Missing Values
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Lesson 4.3 : Encoding Categorical Data
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Lesson 4.4 : Feature Scaling
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Lesson 4.5 : Train-Test Data Preparation
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Chapter 5 — Regression Algorithms

5 Lessons

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Lesson 5.1 : Introduction to Regression
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Lesson 5.2 : Simple Linear Regression
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Lesson 5.3 : Multiple Linear Regression
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Lesson 5.4 : Polynomial Regression
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Lesson 5.5 : Regression Applications
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Chapter 6 — Classification Algorithms

5 Lessons

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Lesson 6.1 : Introduction to Classification
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Lesson 6.2 : Logistic Regression
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Lesson 6.3 : Decision Trees
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Lesson 6.4 : K-Nearest Neighbors
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Lesson 6.5 : Classification Applications
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Chapter 7 — Clustering Algorithms

5 Lessons

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Lesson 7.1 : Introduction to Clustering
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Lesson 7.2 : K-Means Clustering
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Lesson 7.3 : Hierarchical Clustering
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Lesson 7.4 : DBSCAN
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Lesson 7.5 : Clustering Applications
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Chapter 8 — Model Evaluation

5 Lessons

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Lesson 8.1 : Why Model Evaluation is Important
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Lesson 8.2 : Confusion Matrix
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Lesson 8.3 : Accuracy, Precision, Recall and F1 Score
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Lesson 8.4 : Regression Evaluation Metrics
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Lesson 8.5 : Cross-Validation
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Chapter 9 — Feature Engineering and Model Improvement

5 Lessons

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Lesson 9.1 : Introduction to Feature Engineering
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Lesson 9.2 : Feature Selection
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Lesson 9.3 : Feature Extraction
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Lesson 9.4 : Handling Imbalanced Data
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Lesson 9.5 : Improving Model Performance
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Chapter 10 — Machine Learning Projects

5 Lessons

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Lesson 10.1 : Planning a Machine Learning Project
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Lesson 10.2 : Machine Learning Workflow
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Lesson 10.3 : Building a Prediction Project
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Lesson 10.4 : Building a Classification Project
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Lesson 10.5 : Machine Learning Project Case Study
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Machine Learning

Chapters

10

Lessons

50

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