Classification Analysis

This course is part of Data Analysis with Python Specialization

Instructor: Di Wu

What you'll learn

  •   Understand the concept and significance of classification as a supervised learning method.
  •   Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
  •   Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.
  • Skills you'll gain

  •   Supervised Learning
  •   Feature Engineering
  •   Machine Learning
  •   Probability & Statistics
  •   Machine Learning Algorithms
  •   Data Analysis
  •   Data Science
  •   Classification And Regression Tree (CART)
  •   Predictive Modeling
  •   Data Mining
  •   Bayesian Statistics
  • There are 6 modules in this course

    By the end of this course, you will be able to: 1. Understand the concept and significance of classification as a supervised learning method. 2. Identify and describe different classifiers, such as KNN, decision tree, support vector machine, naive bayes, and logistic regression. 3. Apply each classifier to perform binary and multiclass classification tasks on diverse datasets. 4. Evaluate the performance of classifiers using appropriate metrics, including accuracy, precision, recall, F1 score, and ROC curves. 5. Select and fine-tune classifiers based on dataset characteristics and learning requirements. Gain practical experience in solving classification problems through guided tutorials and case studies.

    Decision Tree Classification

    Support Vector Machine Classification

    Naïve Bayes and Logistic Regression

    Classification Evaluation

    Case Study

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