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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
    Free Trial
    G

    Google

    The Nuts and Bolts of Machine Learning

    Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Model Training, Random Forest Algorithm, Model Optimization, Predictive Modeling, Model Evaluation, Python Programming, Performance Tuning, Classification Algorithms

    4.8
    Rating, 4.8 out of 5 stars
    ·
    637 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Unit Testing, Data Engineering, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    633 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Advanced Machine Learning on Google Cloud

    Skills you'll gain: Model Deployment, Model Optimization, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Model Training, Transfer Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    1.5K reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    S

    SAS

    SAS Machine Learning Engineer

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Data Preprocessing, Classification And Regression Tree (CART), Exploratory Data Analysis, Logistic Regression, Statistical Machine Learning, Model Evaluation, Model Training, Supervised Learning, Decision Tree Learning, Probability & Statistics, Data Processing, Statistical Software, Machine Learning Methods, Machine Learning Software, Workflow Management, Machine Learning, Correlation Analysis, Applied Machine Learning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    105 reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Deep Learning Engineering

    Skills you'll gain: Model Deployment, Fine-tuning, PyTorch (Machine Learning Library), Model Evaluation, Model Training, Vision Transformer (ViT), Model Optimization, Transfer Learning, MLOps (Machine Learning Operations), Natural Language Processing, Debugging, Containerization, Kubernetes, Docker (Software), Distributed Computing, Performance Tuning, Tensorflow, Deep Learning, Cloud Computing, Data Pipelines

    Advanced · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Enterprise Workflow

    Skills you'll gain: Feature Engineering, Model Deployment, Data Ethics, Exploratory Data Analysis, Model Evaluation, Unsupervised Learning, Data Presentation, Tensorflow, Application Deployment, Dimensionality Reduction, MLOps (Machine Learning Operations), Model Training, Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Design Thinking, Market Opportunities, Data Science, Machine Learning, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    376 reviews

    Advanced · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    G

    Google

    Google IT Automation with Python

    Skills you'll gain: Version Control, Cloud Management, Test Automation, Infrastructure As A Service (IaaS), Cloud Computing, Cloud Infrastructure, Virtual Machines, Development Testing, Test Script Development, Scripting, Network Troubleshooting, Cloud Services, Email Automation, Web Presence, Python Programming, CI/CD, Configuration Management, Program Development, Containerization, Unit Testing

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
    ·
    55K reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Generative AI for Mobile App Developers

    Skills you'll gain: Prompt Engineering, Prompt Patterns, ChatGPT, Generative AI, Data Ethics, Generative AI Agents, AI Personalization, Mobile Development, Software Design Documents, Software Design, Generative Model Architectures, Anthropic Claude, Mobile Development Tools, LLM Application, AI literacy, iOS Development, AI Integrations, Personalized Campaigns, Software Development, Artificial Intelligence and Machine Learning (AI/ML)

    4.7
    Rating, 4.7 out of 5 stars
    ·
    12K reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Machine Learning Capstone

    Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Applied Machine Learning, Predictive Modeling, Data Presentation, Machine Learning, Collaborative Software, Data Analysis, Predictive Analytics, Technical Communication, Machine Learning Algorithms, Statistical Analysis, Scikit Learn (Machine Learning Library), Keras (Neural Network Library), Descriptive Statistics, Regression Analysis, Python Programming, Text Mining, Artificial Neural Networks

    4.7
    Rating, 4.7 out of 5 stars
    ·
    216 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    The Power of Statistics

    Skills you'll gain: A/B Testing, Sampling (Statistics), Data Analysis, Analytics, Statistics, Descriptive Statistics, Statistical Analysis, Statistical Hypothesis Testing, Probability & Statistics, Advanced Analytics, Probability Distribution, Data Science, Statistical Inference, Statistical Programming, Statistical Methods, Probability, Python Programming

    4.8
    Rating, 4.8 out of 5 stars
    ·
    908 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Production Machine Learning Systems

    Skills you'll gain: Model Optimization, MLOps (Machine Learning Operations), Model Deployment, Model Training, Systems Design, Tensorflow, Hybrid Cloud Computing, Google Cloud Platform, Machine Learning Methods, Performance Tuning, Applied Machine Learning, Machine Learning, Distributed Computing, Dependency Analysis, Data Pipelines

    4.6
    Rating, 4.6 out of 5 stars
    ·
    1K reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Toronto

    Self-Driving Cars

    Skills you'll gain: Computer Vision, Convolutional Neural Networks, Image Analysis, Control Systems, Robotics, Deep Learning, Simulation and Simulation Software, Software Architecture, Simulations, Safety Assurance, Global Positioning Systems, Hardware Architecture, Systems Architecture, Network Routing, Graph Theory, Estimation, Algorithms, Artificial Intelligence, Mathematical Modeling, Linear Algebra

    4.7
    Rating, 4.7 out of 5 stars
    ·
    3.6K reviews

    Advanced · Specialization · 3 - 6 Months

1234…34

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

  • The Nuts and Bolts of Machine Learning: Google
  • MLOps | Machine Learning Operations: Duke University
  • Advanced Machine Learning on Google Cloud: Google Cloud
  • SAS Machine Learning Engineer: SAS
  • Deep Learning Engineering: Coursera
  • IBM AI Enterprise Workflow: IBM
  • Google IT Automation with Python: Google
  • Generative AI for Mobile App Developers: IBM
  • Machine Learning Capstone: IBM
  • The Power of Statistics: Google

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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