Ementorhub

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

Bayesian Statistics

Data Science

Classification And Regression Tree (CART)

Machine Learning

Supervised Learning

Probability & Statistics

Machine Learning Algorithms

Feature Engineering

Predictive Modeling

Data Mining

Data Analysis

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