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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

    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
  • U

    University of Pennsylvania

    AI and Machine Learning Essentials with Python

    Skills you'll gain: Statistical Machine Learning, Data Preprocessing, Model Evaluation, PyTorch (Machine Learning Library), Statistical Methods, Probability, Probability & Statistics, Sampling (Statistics), Logistic Regression, Deep Learning, Probability Distribution, Python Programming, Statistical Modeling, Supervised Learning, Machine Learning, Data Processing, Agentic systems, Artificial Intelligence, Algorithms, AI literacy

    ★ 4.5 (47) · Intermediate · Specialization · 3 - 6 Months

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

    DeepLearning.AI

    Probability & Statistics for Machine Learning & Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Probability, Statistical Inference, Statistics, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Correlation Analysis, Histogram, Statistical Visualization, Box Plots

    ★ 4.6 (685) · Intermediate · Course · 1 - 4 Weeks

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

    Dartmouth College

    Machine Learning Fundamentals

    Skills you'll gain: Supervised Learning, Predictive Modeling, Logistic Regression, Statistical Modeling, Model Evaluation, Statistical Machine Learning, Machine Learning Methods, Applied Machine Learning, Machine Learning, Generative Model Architectures, Machine Learning Algorithms, Classification Algorithms, Model Optimization, Regression Analysis, Probability & Statistics

    Intermediate · Course · 1 - 3 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • D

    Dartmouth College

    Practical Machine Learning: Foundations to Neural Networks

    Skills you'll gain: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Convolutional Neural Networks, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Statistical Inference

    Intermediate · Specialization · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • E

    EDUCBA

    AI Machine Learning with R & Python Projects

    Skills you'll gain: Sampling (Statistics), Matplotlib, Data Analysis, Machine Learning Algorithms, Data Mining, Statistical Analysis, Statistical Hypothesis Testing, Plot (Graphics), Probability & Statistics, NumPy, Pandas (Python Package), Probability Distribution, Dimensionality Reduction, Model Evaluation, R Programming, Python Programming, Data Preprocessing, Applied Machine Learning, Regression Analysis, Artificial Intelligence and Machine Learning (AI/ML)

    ★ 4.6 (18) · Beginner · Specialization · 3 - 6 Months

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

What brings you to Coursera today?

  • J

    Johns Hopkins University

    Data Science: Statistics and Machine Learning

    Skills you'll gain: Shiny (R Package), Rmarkdown, Model Evaluation, Regression Analysis, Leaflet (Software), Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Interactive Data Visualization, Probability & Statistics, Data Visualization, Statistical Analysis, Statistical Modeling, R Programming, Model Training, Machine Learning, GitHub

    ★ 4.4 (7.2K) · Intermediate · Specialization · 3 - 6 Months

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

    IBM

    Exploratory Data Analysis for Machine Learning

    Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Statistical Methods, Data Preprocessing, Statistical Inference, Statistical Hypothesis Testing, Data Processing, Applied Machine Learning, Data Access, Statistics, Statistical Analysis, Data Analysis, Data Cleansing, Data Manipulation, Data Science, Data Wrangling, Machine Learning, Probability & Statistics, Data Import/Export, Data Transformation

    ★ 4.6 (2.6K) · Intermediate · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • 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, Machine Learning Software, Statistical Software, Machine Learning Methods, Process Modeling, Machine Learning, Correlation Analysis, Applied Machine Learning

    ★ 4.7 (105) · Advanced · Professional Certificate · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • J

    John Wiley & Sons

    Data Science and Machine Learning for Business Professionals

    Skills you'll gain: Statistics, Probability & Statistics, Data Analysis, Statistical Methods, Data Literacy, Data Collection, Unsupervised Learning, Text Mining, Analytical Skills, Statistical Inference, Unstructured Data, Probability, Predictive Modeling, Data Science, Deep Learning, Machine Learning, Machine Learning Methods, Data Visualization, Business Communication, Communication

    Intermediate · Course · 3 - 6 Months

    Category: New
    New
    Category: Preview
    Preview
    Category: Credit offered
    Credit offered
  • U

    University of Pennsylvania

    Machine Learning Essentials

    Skills you'll gain: Statistical Machine Learning, Model Evaluation, Statistical Methods, Logistic Regression, Python Programming, Statistical Modeling, Supervised Learning, Machine Learning Methods, Machine Learning, Classification Algorithms, Regression Analysis, Statistical Analysis, Applied Machine Learning, Predictive Modeling, Probability & Statistics, Bayesian Statistics, Dimensionality Reduction, Statistical Hypothesis Testing, Model Optimization, Feature Engineering

    Intermediate · Course · 1 - 4 Weeks

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

    EDUCBA

    Machine Learning with Python & Statistics

    Skills you'll gain: Sampling (Statistics), Data Mining, Statistical Hypothesis Testing, Probability, Statistical Machine Learning, Probability & Statistics, Linear Algebra, Statistical Methods, Statistical Analysis, Statistical Inference, Data Analysis, Probability Distribution, Data Science, Statistics, Machine Learning Methods, Applied Machine Learning, Unsupervised Learning, Machine Learning Algorithms, Machine Learning, Supervised Learning

    Mixed · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
1234…13

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

  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • AI and Machine Learning Essentials with Python: University of Pennsylvania
  • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
  • Machine Learning Fundamentals: Dartmouth College
  • Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
  • AI Machine Learning with R & Python Projects: EDUCBA
  • Data Science: Statistics and Machine Learning: Johns Hopkins University
  • Exploratory Data Analysis for Machine Learning: IBM
  • SAS Machine Learning Engineer: SAS
  • Data Science and Machine Learning for Business Professionals: John Wiley & Sons

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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