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  1. Aug 7, 2024 · Machine learning (ML) is a type of Artificial Intelligence (AI) that allows computers to learn without being explicitly programmed. It involves feeding data into algorithms that can then identify patterns and make predictions on new data.

    • 47 min
  2. Learn the fundamentals and advanced concepts of machine learning, a subset of artificial intelligence that enables computers to learn from data. Explore the types, applications, history, and examples of machine learning algorithms.

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  3. Mar 27, 2024 · Learn what machine learning is, how it works, and the different types of it powering the services and applications we rely on every day. Explore real-world examples, benefits and dangers, and beginner-friendly courses on Coursera.

    • 62 min
    • Introduction : Getting Started with Machine Learning. An Introduction to Machine Learning. What is Machine Learning ? Introduction to Data in Machine Learning.
    • Data and It’s Processing: Introduction to Data in Machine Learning. Understanding Data Processing. Python | Create Test DataSets using Sklearn. Python | Generate test datasets for Machine learning.
    • Supervised learning : Getting started with Classification. Basic Concept of Classification. Types of Regression Techniques. Classification vs Regression. ML | Types of Learning – Supervised Learning.
    • Unsupervised learning : ML | Types of Learning – Unsupervised Learning. Supervised and Unsupervised learning. Clustering in Machine Learning. Different Types of Clustering Algorithm.
  4. Learn what machine learning (ML) is, how it works, and how it differs from deep learning and neural networks. Explore the types, methods, and applications of ML with IBM resources and examples.

  5. Google's fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, interactive visualizations, and hands-on practice exercises.

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  7. May 14, 2024 · Learn the core concepts and types of machine learning (ML), a process of training software to make predictions or generate content from data. Explore examples of supervised, unsupervised, reinforcement, and generative ML systems and their applications.

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