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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.


More to explore:

Cours et certificats populaires en Apprentissage automatique


  • D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing

    4.9
    Rating, 4.9 out of 5 stars
    ·
    38K reviews

    Beginner · Specialization · 1 - 3 Months

  • D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning

    4.9
    Rating, 4.9 out of 5 stars
    ·
    32K reviews

    Beginner · Course · 1 - 4 Weeks

  • I

    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Machine Learning Methods, Jupyter, Advanced Mathematics, Statistics, Statistical Analysis, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives

    4.6
    Rating, 4.6 out of 5 stars
    ·
    15K reviews

    Beginner · Specialization · 3 - 6 Months

  • I

    IBM

    Python for Data Science, AI & Development

    Skills you'll gain: Data Import/Export, Programming Principles, Web Scraping, File I/O, Python Programming, Jupyter, Data Structures, Pandas (Python Package), Data Manipulation, JSON, Computer Programming, Restful API, NumPy, Object Oriented Programming (OOP), Application Programming Interface (API), Automation, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    43K reviews

    Beginner · Course · 1 - 3 Months

  • I

    IBM

    Introduction to Artificial Intelligence (AI)

    Skills you'll gain: Responsible AI, Generative AI, Natural Language Processing, Robotics, Business Logic, Risk Mitigation

    4.7
    Rating, 4.7 out of 5 stars
    ·
    23K reviews

    Beginner · Course · 1 - 4 Weeks

  • U

    University of London

    Machine Learning for All

    Skills you'll gain: Data Preprocessing, Machine Learning, Artificial Intelligence, Model Evaluation, Data Analysis, Image Analysis, Data Collection, Classification Algorithms

    Build toward a degree

    4.7
    Rating, 4.7 out of 5 stars
    ·
    3.5K reviews

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • I

    IBM

    Machine Learning Introduction for Everyone

    Skills you'll gain: Model Evaluation, Predictive Modeling, Machine Learning, Supervised Learning, Applied Machine Learning, Data Science, Artificial Intelligence, Deep Learning, Classification Algorithms, Unsupervised Learning, Regression Analysis, Reinforcement Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    307 reviews

    Beginner · Course · 1 - 4 Weeks

  • D

    DeepLearning.AI

    AI For Everyone

    Skills you'll gain: AI Product Strategy, Responsible AI, Data Ethics, AI Enablement, Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Artificial Neural Networks

    4.8
    Rating, 4.8 out of 5 stars
    ·
    52K reviews

    Beginner · Course · 1 - 4 Weeks

  • A

    Amazon Web Services

    Developing Machine Learning Solutions

    Skills you'll gain: Model Evaluation, MLOps (Machine Learning Operations), AWS SageMaker, Amazon Web Services, Model Deployment, Machine Learning, Applied Machine Learning, Predictive Modeling

    4.5
    Rating, 4.5 out of 5 stars
    ·
    114 reviews

    Beginner · Course · 1 - 4 Weeks

  • I

    IBM

    IBM Generative AI Engineering

    Skills you'll gain: Prompt Engineering, Prompt Patterns, LangChain, Large Language Modeling, Retrieval-Augmented Generation, Exploratory Data Analysis, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, LLM Application, Keras (Neural Network Library), Supervised Learning, Model Evaluation, Data Analysis, Responsible AI, Vector Databases, Data Import/Export

    4.7
    Rating, 4.7 out of 5 stars
    ·
    99K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • G

    Google Cloud

    Introduction to AI and Machine Learning on Google Cloud

    Skills you'll gain: Generative AI, Google Cloud Platform, MLOps (Machine Learning Operations), Prompt Engineering, Tensorflow, AI Workflows, Cloud Infrastructure, Artificial Intelligence, Big Data, Model Deployment, Machine Learning, Supervised Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    322 reviews

    Beginner · Course · 1 - 3 Months

  • M

    Microsoft

    Microsoft Azure Machine Learning

    Skills you'll gain: Unsupervised Learning, Microsoft Azure, Applied Machine Learning, MLOps (Machine Learning Operations), Regression Analysis, Predictive Modeling, Machine Learning, No-Code Development, Artificial Intelligence and Machine Learning (AI/ML), Model Deployment, Artificial Intelligence, Classification Algorithms, Supervised Learning

    4.4
    Rating, 4.4 out of 5 stars
    ·
    289 reviews

    Beginner · Course · 1 - 4 Weeks

1234…283

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

  • Machine Learning: DeepLearning.AI
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Mathematics for Machine Learning: Imperial College London
  • Python for Data Science, AI & Development: IBM
  • Introduction to Artificial Intelligence (AI): IBM
  • Machine Learning for All: University of London
  • Machine Learning Introduction for Everyone: IBM
  • AI For Everyone: DeepLearning.AI
  • Developing Machine Learning Solutions: Amazon Web Services
  • IBM Generative AI Engineering: IBM

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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