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Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

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NumPy For Data Science & Machine Learning...

By : Pruthviraja L

From Beginner to Advanced Level...

4.1 64350

2:7:1 hrs   19 lectures Intermedite Level   

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Python from Zero to Hero + Machine Learning...

By : Shyngys Shynbolatov

Includes Machine Learning, Data Science, Web Scraping...

4.3 68447

1:11:24 hrs   4 lectures All Level   

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12 Real world Casestudies for Machine Learning...

By : Cosmic .

Master Machine Learning by getting your hands dirty on Real Life Case studies. Be A K...

4.7 48193

11:55:15 hrs   112 lectures All Level   

javascript-course-for-beginner-to-expert-data-visualization

Deploying Machine Learning Models - A Complete Guide...

By : Cosmic .

Learn to Deploy Machine Learning Models. Learn about Server and Serverless Frameworks...

4.1 53286

7:49:34 hrs   109 lectures All Level   

javascript-course-for-beginner-to-expert-data-visualization

Data Science Basic Course - DATAhill Solutions....

By : Datahill Solutions

Basic about Data Science, Machine Learning, Data Analytics, Data Visualization...

4.2 61460

7:49:40 hrs   13 lectures All Level   

javascript-course-for-beginner-to-expert-data-visualization

A to Z (NLP) Machine Learning Model building and Deployment...

By : MD Rijwan

Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in ...

4.7 73074

3:17:18 hrs   21 lectures All Level   

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Complete Python Machine Learning & Data Science for Dummies....

By : Abhilash Nelson

Machine Learning and Data Science for programming beginners using python with scikit-...

4.2 73045

10:4:11 hrs   88 lectures Beginner Level   

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Deep Learning & Neural Networks Python - Keras : For Dummies...

By : Abhilash Nelson

Deep Learning and Data Science using Python and Keras Library - Beginner to Professio...

4.7 79798

6:17:24 hrs   45 lectures Beginner Level   

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Road Map to Artificial Intelligence and Machine Learning...

By : Vinoth Rathinam

This course specifically created for AI Aspirants who are eager to know about the roa...

4.8 29197

13 lectures Beginner Level   

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Machine Learning with R...

By : Bert Gollnick

Understand machine learning models and how to implement them in R from an expert in D...

4.5 44504

13:1:56 hrs   124 lectures Intermedite Level   

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Frequently asked questions
  • What is Machine Learning (ML)?

    Machine Learning is a branch of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. ML allows systems to improve their performance over time based on experience.

  • How does Machine Learning work?

    Machine Learning works by training models on labeled data, allowing the algorithm to learn patterns and relationships. These trained models can then make predictions or decisions when presented with new, unseen data. The process involves training, testing, and iterating to improve the model's accuracy.

  • What are the types of Machine Learning?

    Types of ML include:
    Supervised Learning: Models learn from labeled data with input-output pairs.
    Unsupervised Learning: Models discover patterns and relationships in unlabeled data.
    Reinforcement Learning: Agents learn through interaction with an environment, receiving rewards or penalties based on actions.
    Semi-Supervised Learning and Self-Supervised Learning: Hybrid approaches using both labeled and unlabeled data.

  • What are common applications of Machine Learning?

    ML is used in various applications, including:
    Image and speech recognition
    Natural language processing
    Recommendation systems
    Fraud detection
    Predictive analytics
    Autonomous vehicles
    Healthcare diagnostics

  • How does Machine Learning use algorithms and models?

    Machine Learning algorithms process data and learn patterns to create models. These models, based on learned parameters, can then make predictions or decisions when presented with new data. Common algorithms include decision trees, neural networks, support vector machines, and clustering algorithms.

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