Machine Learning with Python

This course is part of multiple programs. Learn more

Instructors: Joseph Santarcangelo +1 more

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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

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