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Machine Learning: Classification

A focused and skill-oriented course that teaches how to build and scale classification models for real-world machine learning tasks.

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

What will you in the Machine Learning: Classification Course

  • Understand how classification models work and where they are applied.

  • Implement logistic regression for binary and multi-class problems.

  • Build and interpret decision trees and apply boosting for improved performance.

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  • Use stochastic gradient ascent to handle large datasets.

  • Evaluate models with metrics such as precision and recall

Program Overview

Module 1: Introduction to Classification
Duration: ~1 hour

  • Overview of classification and real-world use cases.

  • Introduction to the tools and data used in the course.

Module 2: Linear Classifiers and Logistic Regression
Duration: ~3 hours

  • Implement logistic regression from scratch.

  • Explore class boundaries, gradient ascent, and feature selection.

  • Handle multi-class problems using one-vs-all classification.

Module 3: Decision Trees
Duration: ~3 hours

  • Understand how decision trees split data based on feature values.

  • Learn tree construction, stopping rules, and overfitting prevention.

  • Apply decision trees to structured and unstructured data.

Module 4: Boosting for Classification
Duration: ~2 hours

  • Introduction to ensemble learning and boosting techniques.

  • Learn how to improve weak learners to build a strong classifier.

Module 5: Scaling With Stochastic Gradient Ascent
Duration: ~2 hours

  • Use stochastic methods to handle massive datasets efficiently.

  • Learn convergence techniques and optimization strategies.

Module 6: Handling Missing Data and Model Evaluation
Duration: ~2 hours

  • Techniques to manage incomplete data inputs.

  • Evaluate models with accuracy, precision, recall, and ROC curves.

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Job Outlook

  • Machine Learning Engineers: Apply scalable classification models in production systems.

  • Data Scientists: Build predictive models for business, healthcare, or finance sectors.

  • Software Developers: Implement classification-based features in intelligent applications.

  • AI Researchers: Use classification foundations in academic and product-focused research.

  • Marketing & Risk Analysts: Predict churn, detect fraud, or assess risk using classification methods.

9.7Expert Score
Highly Recommended
This course is ideal for learners looking to apply machine learning classification techniques in real scenarios. It balances theory and practical coding well.
Value
9.3
Price
9.5
Skills
9.7
Information
9.6
PROS
  • Strong foundation in classification algorithms
  • Real-world project applications
  • Scalable model building techniques
  • Includes performance evaluation methods
CONS
  • Assumes prior Python and math knowledge
  • No direct instructor feedback due to self-paced format

Specification: Machine Learning: Classification

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

Machine Learning: Classification
Machine Learning: Classification
Course | Career Focused Learning Platform
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