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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
    D

    Duke University

    MLOps | Machine Learning Operations

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

    4.2
    Rating, 4.2 out of 5 stars
    ·
    572 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    The Nuts and Bolts of Machine Learning

    Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Advanced Analytics, Analytics, Statistical Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Unsupervised Learning, Machine Learning Algorithms, Workflow Management, Data Ethics, Supervised Learning, Data Preprocessing, Decision Tree Learning, Random Forest Algorithm, Verification And Validation, Python Programming, Classification Algorithms, Performance Tuning

    4.8
    Rating, 4.8 out of 5 stars
    ·
    593 reviews

    Advanced · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    S

    SAS

    SAS Machine Learning Engineer

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Data Preprocessing, Exploratory Data Analysis, Logistic Regression, Statistical Machine Learning, Model Evaluation, Supervised Learning, Decision Tree Learning, Probability & Statistics, Statistics, Machine Learning Software, Classification And Regression Tree (CART), Workflow Management, Predictive Modeling, Random Forest Algorithm, Feature Engineering, SAS (Software), Machine Learning, Applied Machine Learning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    103 reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Advanced Machine Learning on Google Cloud

    Skills you'll gain: Model Deployment, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Reinforcement Learning, Transfer Learning, Computer Vision, Systems Design, Machine Learning Methods, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Hybrid Cloud Computing, Systems Architecture, Performance Tuning, Embeddings

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

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    Google Advanced Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Visualization Software, Feature Engineering, Regression Analysis, Descriptive Statistics, Logistic Regression, Statistical Hypothesis Testing, Model Evaluation, Data Analysis, Data Science, Tableau Software, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
    ·
    11K reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Machine Learning Capstone

    Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Data Analysis, Applied Machine Learning, Data Presentation, Technical Communication, Machine Learning, Scikit Learn (Machine Learning Library), Python Programming, Regression Analysis, Keras (Neural Network Library), Artificial Neural Networks

    4.7
    Rating, 4.7 out of 5 stars
    ·
    191 reviews

    Advanced · Course · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM RAG and Agentic AI

    Skills you'll gain: Prompt Engineering, AI Orchestration, AI Workflows, Model Context Protocol, LangChain, Retrieval-Augmented Generation, Agentic Workflows, Tool Calling, LangGraph, LLM Application, Agentic systems, Multimodal Prompts, Generative AI, Generative AI Agents, Vector Databases, Generative Model Architectures, OpenAI API, Embeddings, Responsible AI, Software Development

    4.6
    Rating, 4.6 out of 5 stars
    ·
    669 reviews

    Advanced · Professional Certificate · 3 - 6 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, Embedded Software, Automation, Deep Learning, Software Architecture, Simulations, Safety Assurance, Traffic Flow Optimization, Artificial Neural Networks, Global Positioning Systems, Machine Controls, Hardware Architecture, Systems Architecture, Network Routing, Estimation, Machine Learning Methods

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

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Illinois Urbana-Champaign

    Deep Learning for Healthcare

    Skills you'll gain: Deep Learning, Convolutional Neural Networks, Health Informatics, Autoencoders, Recurrent Neural Networks (RNNs), Image Analysis, Embeddings, Health Information Management, Machine Learning, Applied Machine Learning, Health Care, Model Deployment, Generative Adversarial Networks (GANs), Artificial Neural Networks, Healthcare Project Management, Supervised Learning, Model Evaluation, Machine Learning Methods, Graph Theory, Big Data

    3.3
    Rating, 3.3 out of 5 stars
    ·
    49 reviews

    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 Visualization, Data Ethics, Exploratory Data Analysis, Model Evaluation, Unsupervised Learning, Data Presentation, Tensorflow, Application Deployment, Dimensionality Reduction, MLOps (Machine Learning Operations), Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Supervised Learning, Design Thinking, Data Science, Machine Learning, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    354 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    More Applied Data Science with Python

    Skills you'll gain: Unsupervised Learning, Data Mining, Social Network Analysis, ChatGPT, Embeddings, Bayesian Network, Machine Learning Methods, Data Science, Supervised Learning, Generative AI, Machine Learning, Anomaly Detection, Data Preprocessing, Data Analysis, Recurrent Neural Networks (RNNs), Data Manipulation, Python Programming, Exploratory Data Analysis, Machine Learning Algorithms, Classification Algorithms

    4.3
    Rating, 4.3 out of 5 stars
    ·
    8 reviews

    Advanced · Specialization · 3 - 6 Months

  • C

    Coursera

    Semantic Segmentation with Amazon Sagemaker

    Skills you'll gain: AWS SageMaker, AWS Identity and Access Management (IAM), Amazon Web Services, Model Deployment, Image Analysis, Amazon Elastic Compute Cloud, Amazon S3, Machine Learning Algorithms, Data Preprocessing, Convolutional Neural Networks, Computer Vision, Deep Learning, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    94 reviews

    Advanced · Guided Project · Less Than 2 Hours

Searches related to machine learning

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

  • MLOps | Machine Learning Operations: Duke University
  • The Nuts and Bolts of Machine Learning: Google
  • SAS Machine Learning Engineer: SAS
  • Advanced Machine Learning on Google Cloud: Google Cloud
  • Google Advanced Data Analytics: Google
  • Machine Learning Capstone: IBM
  • IBM RAG and Agentic AI: IBM
  • Self-Driving Cars: University of Toronto
  • Deep Learning for Healthcare: University of Illinois Urbana-Champaign
  • IBM AI Enterprise Workflow: 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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