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Machine Learning Scikit-Learn

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VR
Vikram Reddy
/
September 7, 2026

Machine Learning Scikit-Learn

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AI & Data Science

This course teaches you how to build and evaluate machine learning models using Scikit-Learn, one of the most widely used Python libraries for machine learning. Covering everything from data preprocessing to core algorithms like regression, classification, and clustering, this course is designed for beginners with basic Python knowledge who want to build a strong foundation in practical machine learning. By the end, you’ll be able to build, evaluate, and improve machine learning models on real-world datasets.

 

What Will I Learn?

  • Understand core machine learning concepts and the different types of learning
  • Preprocess and prepare raw data for machine learning models
  • Build and evaluate regression models to predict continuous values
  • Build and evaluate classification models using algorithms like Decision Trees, KNN, and SVM
  • Apply unsupervised learning techniques including clustering and dimensionality reduction
  • Improve model performance using cross-validation and hyperparameter tuning
  • Identify and address overfitting and underfitting in models
  • Complete an end-to-end machine learning project from raw data to a final evaluated model

Course Content

Introduction to Machine Learning
This topic introduces students to the core concept of machine learning and gets their development environment set up before diving into hands-on model building.

Data Preprocessing
This topic covers the essential steps needed to prepare raw data before it can be used to train a machine learning model, a critical stage that heavily impacts model performance.

Supervised Learning – Regression
This topic introduces regression, a core supervised learning technique used to predict continuous numerical values, along with how to evaluate how well a regression model performs.

Supervised Learning – Classification
This topic introduces classification, another core supervised learning technique used to predict categorical outcomes, covering several popular algorithms and how to evaluate them.

Unsupervised Learning
This topic introduces unsupervised learning techniques, where models find patterns in data without labeled outcomes, covering clustering and dimensionality reduction.

Model Improvement Techniques
This topic covers techniques to improve model performance and reliability, helping students build models that generalize well to new, unseen data.

Real-World Project
This final topic lets students apply everything learned throughout the course by working through a complete, end-to-end machine learning project.

About the instructor

VR

Vikram Reddy

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8 Courses

0 students

Start Learning
  • Beginner
Durations: 8 hours
Lectures: 20
Students: Max 0
Level: Beginner
Language:
Certificate:

Material Includes

  • 8 hours on-demand video
  • 1 article
  • 6 downloadable resources
  • Full lifetime access
  • Certificate of completion

Requirements

  • Basic knowledge of Python (required)
  • Basic understanding of statistics is helpful but not mandatory
  • Python, Scikit-Learn, and Jupyter Notebook installed on your system (free)
  • Willingness to code along with practical examples and complete the final project

Audience

  • Beginners with basic Python knowledge who want to learn machine learning
  • Data analysts looking to transition into data science or ML roles
  • Students preparing for a career in machine learning or data science
  • Developers wanting to add practical ML skills to their toolkit
  • Anyone who wants to build and evaluate ML models using real-world datasets