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Machine learning for beginners

Course Reviews

Track :

Artificial Intelligence
Course Presenter :

Nation Innovation

Lessons no : 10

For Free Certificate After Complete The Course

To Register in Course you have to watch at least 30 Second of any lesson

Join The Course Go To Community Download Course Content

What will you learn in this course?
  • Implement supervised learning algorithms like linear and logistic regression for predictive modeling in Python using Scikit-learn and TensorFlow
  • Apply unsupervised learning techniques such as K-means clustering to analyze and segment data effectively
  • Develop reinforcement learning strategies to optimize decision-making processes in real-world applications
  • Evaluate machine learning models using performance metrics, cross-validation, and techniques to prevent overfitting
  • Utilize Python libraries to preprocess data, train models, and improve accuracy for practical machine learning tasks
  • Solve real-world problems by building, testing, and deploying machine learning models for automation and pattern recognition

How to Get The Certificate

  • You must have an account Register
  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
  • you can follow your course progress From Your Profile
  • You can Register With Any Course For Free
  • The Certificate is free !
Lessons | 10

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3.9
17 Reviews
Deekshitha vij PS

good 2025-10-11

Aditya Kshirsagar

. 2025-10-11

Ummadi Ushasree

Good 2025-10-10

Shirija Mula

Very helpful 2025-10-10

Tayeb haidari

Great 2025-10-09

MADHAN PRASATH

good 2025-10-09

Pradnya Sukhdeo Thele

Excellent teaching 2025-10-07

Jeffrey Ayson

thank you for the very informative insights 2025-10-07

Koushik Sai

Good 2025-09-29

Vedant Badgujar

It was my best experience in my life 2025-09-17

Manikur Rahman Anonno

it was good 2025-09-07

Febina Shiji R

excellent 2025-09-04

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Machine learning, in this course we will learn the foundational concepts and practical applications of machine learning. You will explore various types of learning methods including supervised, unsupervised, and reinforcement learning. We'll cover essential algorithms such as linear regression, logistic regression, decision trees, k-nearest neighbors, support vector machines, and clustering techniques like K-means. You will also gain hands-on experience with model training, evaluation, overfitting, cross-validation, and performance metrics. The course emphasizes real-world problem-solving using Python and popular ML libraries like Scikit-learn and TensorFlow. Whether you're aiming to build predictive models, automate tasks, or analyze patterns in data, this course provides a comprehensive and accessible path to mastering machine learning techniques. Nation Innovation