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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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Learn Practical Machine Learning using Python...

By : Manas Dasgupta

Concepts and Projects based learning for aspiring Machine Learning Professionals...

4.3 227

21:25:4 hrs   106 lectures All Level   

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Learn Data Science Practically using Python...

By : Manas Dasgupta

Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit L...

4.3 224

22:52:2 hrs   111 lectures All Level   

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TinyML with Wio Terminal...

By : Ashraf Al Madhoun

TinyML is a program for machine learning (ML) and in this course we will help you sta...

4.5 223

2:15:16 hrs   14 lectures All Level   

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

2024 Master Data Science 5-in-1 Projects Data Interview Show...

By : Temotec Learning Academy

Unleash the Power of Data: EDA, Sentiment Analysis, Predictive Modeling, Time Series ...

4.3 265

27 lectures All Level   

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

Python For Linear Regression Analysis...

By : Zeeshan Ahmad

Linear Regression - Theory, Intuition, Mathematics and Implementation in Python...

4.4 296

6:43:50 hrs   41 lectures All Level   

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Python for Machine Learning & Deep Learning In One Semester ...

By : Zeeshan Ahmad

Practical Oriented explanations of Machine Learning and Deep Learning Models With mor...

4.8 322

306 lectures All Level   

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2023 Data Science: Python for Data Analysis Full Bootcamp...

By : Ahmed Ibrahim

Learn and build your Python Programming skills from the ground up in addition to Pyth...

4.5 406

6:9:56 hrs   74 lectures All Level   

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Machine Learning with Python Basics (For Beginners)...

By : Mohamed Gamal

Learn the Basics of Machine Learning with Python (First Step For Beginners)...

4 605

1:46:17 hrs   50 lectures Beginner Level   

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Introduction to Machine Learning in PHP...

By : Amir Kamizi

Learn to Build Different Machine Learning Models Easily...

4.5 312

1:13:12 hrs   15 lectures All Level   

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Applied Time Series Analysis and Forecasting in Python...

By : Akhil Vydyula

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH...

3.9 302

6:50:28 hrs   11 lectures All Level   

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  • 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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