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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


  • D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms

    ★ 4.9 (39K) · Beginner · Specialization · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Methods, Applied Machine Learning, Model Training, Statistical Machine Learning, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Model Optimization, Predictive Analytics, Classification Algorithms

    ★ 4.7 (18K) · Intermediate · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    IBM

    IBM Machine Learning

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Autoencoders, Feature Engineering, Dimensionality Reduction, Supervised Learning, Generative AI, Classification Algorithms, Regression Analysis, Time Series Analysis and Forecasting, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Reinforcement Learning, Generative Adversarial Networks (GANs), Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Science, Machine Learning, Python Programming

    ★ 4.6 (3.6K) · Intermediate · Professional Certificate · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • I

    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Data Preprocessing, Unsupervised Learning, Feature Engineering, Applied Mathematics, Model Optimization, Mathematical Software, Jupyter, Statistics, Numerical Analysis, Applied Machine Learning, Artificial Neural Networks, Data Science, Data Manipulation, Python Programming, Data Transformation

    ★ 4.6 (15K) · Beginner · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • U

    University of Washington

    Machine Learning

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning, Image Analysis, Machine Learning Algorithms, AI Personalization, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Supervised Learning, Bayesian Statistics, Statistical Machine Learning, Model Training, Logistic Regression, Statistical Modeling, Data Mining

    ★ 4.6 (16K) · Intermediate · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

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

    ★ 4.9 (32K) · Beginner · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered

What brings you to Coursera today?

  • 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 (617) · Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • A

    Amazon Web Services

    Fundamentals of Machine Learning and Artificial Intelligence

    Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Generative AI, Deep Learning, Artificial Intelligence, Amazon Web Services, Applied Machine Learning, AI literacy, Machine Learning, Digital Transformation

    ★ 4.6 (3.1K) · Mixed · Course · 1 - 4 Weeks

    Category: Free
    Free
    Category: Credit offered
    Credit offered
  • D

    DeepLearning.AI

    Mathematics for Machine Learning and Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Linear Algebra, Statistical Inference, Model Optimization, Machine Learning Methods, Statistics, Applied Mathematics, Probability, Calculus, Dimensionality Reduction, Applied Machine Learning, Mathematical Software, Data Transformation, Machine Learning

    ★ 4.6 (3.2K) · Intermediate · Specialization · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    IBM

    IBM Introduction to Machine Learning

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Feature Engineering, Dimensionality Reduction, Supervised Learning, Classification Algorithms, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Machine Learning Methods, Statistical Hypothesis Testing, Model Training, Data Processing, Machine Learning

    ★ 4.6 (3.4K) · Intermediate · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • 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, Responsible AI, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    ★ 4.2 (606) · Advanced · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • D

    DeepLearning.AI

    Machine Learning in Production

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Application Deployment, Model Training, Continuous Deployment, Model Evaluation, Data Preprocessing, Model Optimization, Machine Learning, Applied Machine Learning, Data Validation, Data Integrity, Data Maintenance, Data Quality, Data Synthesis, Data Collection, System Monitoring, Continuous Monitoring, Unstructured Data

    ★ 4.8 (3.4K) · Intermediate · Course · 1 - 4 Weeks

    Category: Credit offered
    Credit offered
1234…641

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

  • Machine Learning: DeepLearning.AI
  • Machine Learning with Python: IBM
  • IBM Machine Learning: IBM
  • Mathematics for Machine Learning: Imperial College London
  • Machine Learning: University of Washington
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • The Nuts and Bolts of Machine Learning: Google
  • Fundamentals of Machine Learning and Artificial Intelligence: Amazon Web Services
  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • IBM Introduction to Machine Learning: 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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