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  • Machine Learning

Cours en Apprentissage automatique

Les cours en apprentissage automatique peuvent vous aider à comprendre comment construire, entraîner et analyser des modèles prédictifs. Vous pouvez développer des compétences en préparation des données, choix d'algorithmes, optimisation et évaluation. De nombreux cours utilisent des bibliothèques courantes pour tester des modèles.


Cours et certificats populaires en Apprentissage automatique


  • Status: Free Trial
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    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

    4.9
    Rating, 4.9 out of 5 stars
    ·
    37K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
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    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Feature Engineering, Applied Machine Learning, Scikit Learn (Machine Learning Library), Machine Learning, Predictive Modeling, Dimensionality Reduction, Regression Analysis, Decision Tree Learning, Classification And Regression Tree (CART), Statistical Modeling

    4.7
    Rating, 4.7 out of 5 stars
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    18K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
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    DeepLearning.AI

    Mathematics for Machine Learning and Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Data Transformation

    4.6
    Rating, 4.6 out of 5 stars
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    3.1K reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
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    IBM

    IBM Machine Learning

    Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Feature Engineering, Regression Analysis, Dimensionality Reduction, Time Series Analysis and Forecasting, Reinforcement Learning, Generative Model Architectures, Data Cleansing, Data Access, Deep Learning, Data Analysis, Applied Machine Learning, Predictive Modeling, Statistical Inference, Data Science, Machine Learning Algorithms, Machine Learning, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
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    3.5K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: Free
    Free
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    Amazon Web Services

    Fundamentals of Machine Learning and Artificial Intelligence

    Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Generative AI, Deep Learning, Artificial Intelligence, Amazon Web Services, Applied Machine Learning, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
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    2.2K reviews

    Mixed · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
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    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Probability & Statistics, Unsupervised Learning, Machine Learning Algorithms, Jupyter, Data Science, Advanced Mathematics, Statistics, Statistical Analysis, Artificial Neural Networks, Algorithms, Data Manipulation, Python Programming, Derivatives

    4.6
    Rating, 4.6 out of 5 stars
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    15K reviews

    Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
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    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Feature Engineering, Artificial Intelligence, Classification And Regression Tree (CART), Python Programming, Regression Analysis, Statistical Modeling, Data Transformation

    4.9
    Rating, 4.9 out of 5 stars
    ·
    31K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Washington

    Machine Learning

    Skills you'll gain: Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Modeling, Artificial Intelligence, Deep Learning, Data Mining, Computer Vision, Statistical Machine Learning, Predictive Analytics, Text Mining, Machine Learning Algorithms, Big Data

    4.6
    Rating, 4.6 out of 5 stars
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    16K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
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    Coursera

    Foundations of Machine Learning

    Skills you'll gain: Supervised Learning, Unsupervised Learning, Time Series Analysis and Forecasting, Applied Machine Learning, Machine Learning Algorithms, Feature Engineering, Dimensionality Reduction, Machine Learning, Predictive Modeling, Predictive Analytics, Scikit Learn (Machine Learning Library), Forecasting, Data Processing, Anomaly Detection, Data Manipulation, Regression Analysis, Statistical Modeling, Data Transformation, Data Cleansing

    4.5
    Rating, 4.5 out of 5 stars
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    8 reviews

    Intermediate · Course · 1 - 4 Weeks

  • D

    DeepLearning.AI

    Machine Learning in Production

    Skills you'll gain: MLOps (Machine Learning Operations), Application Deployment, Continuous Deployment, Software Development Life Cycle, Machine Learning, Applied Machine Learning, Data Validation, Feature Engineering, Data Quality, Continuous Monitoring, Data Pipelines

    4.8
    Rating, 4.8 out of 5 stars
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    3.3K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Introduction to Artificial Intelligence (AI)

    Skills you'll gain: Responsible AI, Generative AI, Natural Language Processing, Business Intelligence, Content Creation, Risk Mitigation

    4.7
    Rating, 4.7 out of 5 stars
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    22K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
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    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Application Deployment, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
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    556 reviews

    Advanced · Specialization · 3 - 6 Months

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In summary, here are 10 of our most popular machine learning courses

  • Machine Learning: DeepLearning.AI
  • Machine Learning with Python: IBM
  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • IBM Machine Learning: IBM
  • Fundamentals of Machine Learning and Artificial Intelligence: Amazon Web Services
  • Mathematics for Machine Learning: Imperial College London
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Machine Learning: University of Washington
  • Foundations of Machine Learning: Coursera
  • Machine Learning in Production: DeepLearning.AI

Frequently Asked Questions about Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It is important because it drives innovation across various sectors, from healthcare to finance, by automating processes and providing insights that were previously unattainable. As industries increasingly rely on data-driven decision-making, understanding machine learning becomes essential for staying competitive.‎

A variety of job opportunities exist in the field of machine learning. Positions include machine learning engineer, data scientist, AI researcher, and business intelligence analyst. These roles often require a blend of programming skills, statistical knowledge, and domain expertise. As organizations continue to adopt machine learning technologies, the demand for skilled professionals in this area is expected to grow.‎

To learn machine learning effectively, you should focus on several key skills. Proficiency in programming languages such as Python or R is crucial, along with a solid understanding of statistics and linear algebra. Familiarity with data manipulation and visualization tools, as well as experience with machine learning frameworks like TensorFlow or PyTorch, will also be beneficial. These skills will provide a strong foundation for your machine learning journey.‎

There are many excellent online resources for learning machine learning. Notable options include the IBM Machine Learning Professional Certificate and the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These programs offer structured learning paths and hands-on projects to help you build practical skills.‎

Yes. You can start learning Machine Learning on Coursera for free in two ways:

  1. Preview the first module of many Machine Learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in Machine Learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn machine learning, start by taking introductory courses that cover the basics of algorithms and data analysis. Engage in hands-on projects to apply what you've learned, and gradually progress to more advanced topics. Utilize online resources, participate in forums, and collaborate with peers to enhance your understanding. Consistent practice and real-world application will reinforce your skills.‎

Typical topics covered in machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, and neural networks. Additionally, courses often explore data preprocessing, feature engineering, and model evaluation. Understanding these concepts will equip you with the knowledge needed to tackle various machine learning challenges.‎

For training and upskilling employees in machine learning, programs like the Applied Machine Learning Specialization are highly effective. These courses focus on practical applications and real-world scenarios, making them suitable for professionals looking to enhance their skills and contribute to their organizations' data-driven initiatives.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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