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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


  • 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
  • G

    Google

    Google AI

    Skills you'll gain: Vibe coding, Prompt Patterns, AI powered creativity, Brainstorming, Planning, AI literacy, Visual Storytelling, Responsible AI, Prompt Engineering Tools, Generative AI, Data Wrangling, Data Analysis, Content Creation, Artificial Intelligence, Presentations, Business Communication, Data Visualization, Research, Project Management, Machine Learning

    ★ 4.8 (3.1K) · Beginner · Professional Certificate · 3 - 6 Months

    Category: New
    New
    Category: Credit offered
    Credit offered
  • G

    Google

    Google Advanced Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, A/B Testing, Sampling (Statistics), Data Analysis, Exploratory Data Analysis, Regression Analysis, Data Visualization Software, Data Presentation, Data Ethics, Feature Engineering, Statistical Hypothesis Testing, Analytics, Statistical Analysis, Data Science, Tableau Software, Machine Learning, Object Oriented Programming (OOP), Web Presence, Python Programming

    ★ 4.8 (11K) · Advanced · Professional Certificate · 3 - 6 Months

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

    Google

    Introduction to AI

    Skills you'll gain: Generative AI, AI Enablement, Artificial Intelligence and Machine Learning (AI/ML), AI literacy, Model Training, Machine Learning, Innovation, Critical Thinking

    ★ 4.8 (12K) · Beginner · Course · 1 - 4 Weeks

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

    Google

    Google Prompting Essentials

    Skills you'll gain: Data Visualization, Prompt Engineering Tools, Prompt Patterns, AI powered creativity, Graphing, Context Engineering, AI literacy, AI Personalization, Generative AI, Timelines, LLM Application, Ideation, Artificial Intelligence, Business Correspondence, Complex Problem Solving, Report Writing, Solution Design, Document Management, Critical Thinking, Machine Learning

    ★ 4.8 (7K) · Beginner · Specialization · 1 - 4 Weeks

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

    Google

    Google Advanced Data Analytics Capstone

    Skills you'll gain: Data Visualization, Data Presentation, Regression Analysis, Advanced Analytics, Statistical Analysis, Data Analysis, Statistical Modeling, Applied Machine Learning, Analytical Skills, Data Science, Machine Learning Methods, Artificial Intelligence, Python Programming, AI literacy, Machine Learning, Portfolio Management

    ★ 4.8 (1.4K) · Advanced · Course · 1 - 4 Weeks

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

What brings you to Coursera today?

  • G

    Google

    Foundations of Data Science

    Skills you'll gain: Data Storytelling, Data Ethics, Data Analysis, Data-Driven Decision-Making, Analytics, Workflow Management, Data Science, Advanced Analytics, Analytical Skills, Business Solutions, Technical Communication, Process Design, Project Management, Communication, Stakeholder Communications, Machine Learning

    ★ 4.7 (3.9K) · Advanced · Course · 1 - 3 Months

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

    Google

    Regression Analysis: Simplify Complex Data Relationships

    Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Logistic Regression, Statistical Analysis, Statistical Methods, Correlation Analysis, Predictive Modeling, Supervised Learning, Predictive Analytics, Statistical Modeling, Machine Learning, Model Evaluation, Variance Analysis, Python Programming

    ★ 4.7 (592) · Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
1

In summary, here are 8 of our most popular machine learning courses

  • The Nuts and Bolts of Machine Learning: Google
  • Google AI: Google
  • Google Advanced Data Analytics: Google
  • Introduction to AI: Google
  • Google Prompting Essentials: Google
  • Google Advanced Data Analytics Capstone: Google
  • Foundations of Data Science: Google
  • Regression Analysis: Simplify Complex Data Relationships: 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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