Ementorhub

What you'll learn

Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.

Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.

Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.

Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.

Skills you'll gain

Decision Tree Learning

Applied Machine Learning

Unsupervised Learning

Scikit Learn (Machine Learning Library)

Machine Learning

Feature Engineering

Statistical Modeling

Supervised Learning

Regression Analysis

Predictive Modeling

Classification And Regression Tree (CART)

Dimensionality Reduction

There are 6 modules in this course

Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python.

Linear and Logistic Regression

Building Supervised Learning Models

Building Unsupervised Learning Models

Evaluating and Validating Machine Learning Models

Final Project and Exam