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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


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    Coursera

    Scikit-Learn For Machine Learning Classification Problems

    Skills you'll gain: Scikit Learn (Machine Learning Library), Classification Algorithms, Applied Machine Learning, Model Training, Machine Learning Algorithms, Predictive Modeling, Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    19 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
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    Coursera

    Machine Learning Pipelines with Azure ML Studio

    Skills you'll gain: Model Deployment, Decision Tree Learning, Applied Machine Learning, Predictive Modeling, Machine Learning Software, Microsoft Azure, No-Code Development, Machine Learning, Data Preprocessing, Predictive Analytics, Model Training, Cloud Deployment, Feature Engineering, Machine Learning Algorithms, Model Evaluation, Data Science, Data Analysis, Classification Algorithms

    4.5
    Rating, 4.5 out of 5 stars
    ·
    831 reviews

    Beginner · Guided Project · Less Than 2 Hours

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    Coursera

    Data Science Challenge

    Skills you'll gain: Applied Machine Learning, Jupyter, Machine Learning Algorithms, Model Evaluation, Machine Learning, Model Training, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    218 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    M

    Microsoft

    Build a computer vision app with Azure Cognitive Services

    Skills you'll gain: Application Programming Interface (API), Microsoft Azure, Cloud API, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), User Accounts, Image Analysis, Artificial Intelligence, Cloud Computing, Software Development

    4.5
    Rating, 4.5 out of 5 stars
    ·
    526 reviews

    Intermediate · Guided Project · Less Than 2 Hours

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    Coursera

    Fake News Detection with Machine Learning

    Skills you'll gain: Recurrent Neural Networks (RNNs), Exploratory Data Analysis, Deep Learning, Text Mining, Plot (Graphics), Artificial Neural Networks, Data Cleansing, Data Import/Export, Data Preprocessing, Natural Language Processing, Model Training, Applied Machine Learning, Python Programming, Machine Learning, Automation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    265 reviews

    Beginner · Guided Project · Less Than 2 Hours

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    Coursera

    Breast Cancer Prediction Using Machine Learning

    Skills you'll gain: Data Cleansing, Logistic Regression, Data Preprocessing, Applied Machine Learning, Data Import/Export, Data Mining, Python Programming, Data Access, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning Methods, Classification Algorithms, Machine Learning, Supervised Learning

    4.2
    Rating, 4.2 out of 5 stars
    ·
    57 reviews

    Intermediate · Guided Project · Less Than 2 Hours

What brings you to Coursera today?

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    Coursera

    Scikit-Learn to Solve Regression Machine Learning Problems

    Skills you'll gain: Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Machine Learning Algorithms, Applied Machine Learning, Predictive Analytics, Python Programming, Classification Algorithms, Model Training, Machine Learning, Data Analysis

    4.8
    Rating, 4.8 out of 5 stars
    ·
    11 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Tweet Emotion Recognition with TensorFlow

    Skills you'll gain: Recurrent Neural Networks (RNNs), Tensorflow, Model Optimization, Python Programming, Model Training, Natural Language Processing, Data Preprocessing, Applied Machine Learning, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Classification Algorithms, Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    174 reviews

    Intermediate · Guided Project · Less Than 2 Hours

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    Coursera

    Titanic Survival Prediction Using Machine Learning

    Skills you'll gain: Feature Engineering, Data Cleansing, Data Visualization, Data Preprocessing, Logistic Regression, Model Training, Predictive Modeling, Statistical Machine Learning, Applied Machine Learning, Data Processing, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning, Machine Learning Methods, Model Evaluation, Classification Algorithms, Data Science, Python Programming

    4.8
    Rating, 4.8 out of 5 stars
    ·
    19 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Deep Learning with PyTorch : Image Segmentation

    Skills you'll gain: PyTorch (Machine Learning Library), Model Evaluation, Convolutional Neural Networks, Transfer Learning, Image Analysis, Model Training, Deep Learning, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    228 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free
    Free
    C

    Coursera

    Build your first Machine Learning Pipeline using Dataiku

    Skills you'll gain: Model Training, Data Import/Export, Model Evaluation, No-Code Development, Exploratory Data Analysis, Predictive Modeling, Applied Machine Learning, Data Pipelines, Machine Learning Software, Data Preprocessing, Data Analysis, Machine Learning, Predictive Analytics

    4.5
    Rating, 4.5 out of 5 stars
    ·
    61 reviews

    Beginner · Guided Project · Less Than 2 Hours

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    Coursera

    Employee Attrition Prediction Using Machine Learning

    Skills you'll gain: Data Preprocessing, Data Visualization, Logistic Regression, Feature Engineering, Statistical Visualization, Data Processing, Data Cleansing, Data Wrangling, People Analytics, Predictive Modeling, Scikit Learn (Machine Learning Library), Data Science, Applied Machine Learning, Model Training, Statistical Modeling, Statistical Machine Learning, Regression Analysis, Supervised Learning, Model Evaluation, Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    15 reviews

    Beginner · Guided Project · Less Than 2 Hours

1234…32

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

  • Scikit-Learn For Machine Learning Classification Problems: Coursera
  • Machine Learning Pipelines with Azure ML Studio: Coursera
  • Data Science Challenge: Coursera
  • Build a computer vision app with Azure Cognitive Services: Microsoft
  • Fake News Detection with Machine Learning: Coursera
  • Breast Cancer Prediction Using Machine Learning: Coursera
  • Scikit-Learn to Solve Regression Machine Learning Problems: Coursera
  • Tweet Emotion Recognition with TensorFlow: Coursera
  • Titanic Survival Prediction Using Machine Learning: Coursera
  • Deep Learning with PyTorch : Image Segmentation : Coursera

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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