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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

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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
Nsidibe Michael Etim 

Excellent
2025-11-04

Himanshu Dobariya 

so learn is carefully.
2025-10-24

R.K. Shanthini 

Good!
2025-10-15

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

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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