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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: Preview
    Preview
    D

    DeepLearning.AI

    AI For Everyone

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

    4.8
    Rating, 4.8 out of 5 stars
    ·
    53K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Retrieval-Augmented Generation, PyTorch (Machine Learning Library), Computer Vision, Unsupervised Learning, Generative Model Architectures, Prompt Patterns, Generative AI, PySpark, Model Optimization, Keras (Neural Network Library), Supervised Learning, LLM Application, Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    22K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Pittsburgh

    Applied Bayesian Data Analysis

    Skills you'll gain: Bayesian Statistics, Statistical Modeling, Predictive Analytics, Statistics, Regression Analysis, Predictive Modeling, Statistical Inference, Probability & Statistics, Mathematical Modeling, Model Evaluation, Data Analysis, Data Science, Statistical Machine Learning, Statistical Analysis, Statistical Programming, Markov Model, Probability Distribution, Sampling (Statistics), Machine Learning, Python Programming

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning: Linear Algebra

    Skills you'll gain: Linear Algebra, Applied Mathematics, Jupyter, Data Science, Data Manipulation, Data Transformation, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    13K reviews

    Beginner · Course · 1 - 3 Months

  • C

    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
    ·
    225 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    I

    IBM

    AI Foundations for Everyone

    Skills you'll gain: Prompt Engineering, Prompt Patterns, Responsible AI, ChatGPT, Generative AI, Machine Learning Methods, Generative AI Agents, IBM Cloud, Generative Model Architectures, Prompt Engineering Tools, AI Enablement, AI Workflows, Application Deployment, AI literacy, Machine Learning Software, Business Workflow Analysis, Workflow Management, Machine Learning, Deep Learning, Data Science

    4.7
    Rating, 4.7 out of 5 stars
    ·
    36K reviews

    Beginner · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    Edureka

    Explainable AI (XAI)

    Skills you'll gain: Incident Management, Governance, Incident Response, Data Governance, Compliance Management, Policy Development, Risk Management, Governance Risk Management and Compliance, Risk Management Framework, Supplier Risk Management, Artificial Intelligence, Model Training, AI Security, Testability, Statistical Methods, Data Science, Model Optimization, Applied Machine Learning, Machine Learning Methods, Python Programming

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Genomic Data Science

    Skills you'll gain: Bioinformatics, Unix Commands, grep, Biostatistics, R (Software), Exploratory Data Analysis, Statistical Analysis, Unix Shell, Unix, Data Science, Data Management, Statistical Methods, Information Management, Command-Line Interface, Statistical Hypothesis Testing, Data Structures, Big Data, Molecular Biology, R Programming, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    6.8K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Applied DeepSeek: Building Intelligent Systems

    Skills you'll gain: DeepSeek API, LLM Application, Deepseek, Generative AI Agents, Prompt Engineering, AI Workflows, Tool Calling, Model Deployment, AI Orchestration, Agentic Workflows, Prompt Patterns, Large Language Modeling, AI Integrations, Cloud Deployment, Gemini, OpenAI, Application Deployment, Generative AI, Agentic systems, Data Science

    Intermediate · Course · 1 - 4 Weeks

  • C

    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

  • C

    Coursera

    Build a Machine Learning Web App with Streamlit and Python

    Skills you'll gain: Machine Learning Algorithms, Dashboard Creation, Classification Algorithms, Interactive Data Visualization, Data Visualization Software, Model Evaluation, Applied Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Plot (Graphics), Web Applications, Logistic Regression, Predictive Modeling, Model Training, Data Science, Python Programming, Fine-tuning, Model Optimization, Pandas (Python Package)

    4.7
    Rating, 4.7 out of 5 stars
    ·
    417 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • I

    IBM

    Scalable Machine Learning on Big Data using Apache Spark

    Skills you'll gain: Apache Spark, PySpark, Applied Machine Learning, Big Data, Machine Learning Methods, Data Storage Technologies, Statistical Machine Learning, Data Preprocessing, Data Storage, Data Pipelines, Machine Learning Algorithms, Machine Learning, Data Processing, Data Science, Model Evaluation, Descriptive Statistics

    3.8
    Rating, 3.8 out of 5 stars
    ·
    1.3K reviews

    Intermediate · Course · 1 - 4 Weeks

1234…31

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

  • AI For Everyone: DeepLearning.AI
  • IBM AI Engineering: IBM
  • Applied Bayesian Data Analysis: University of Pittsburgh
  • Mathematics for Machine Learning: Linear Algebra: Imperial College London
  • Data Science Challenge: Coursera
  • AI Foundations for Everyone: IBM
  • Explainable AI (XAI): Edureka
  • Genomic Data Science: Johns Hopkins University
  • Applied DeepSeek: Building Intelligent Systems: Packt
  • Employee Attrition Prediction Using Machine Learning: 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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