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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


  • Status: Trending in AI
    Trending in AI
    I

    IBM

    IBM Machine Learning

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

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.6K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • I

    IBM

    Machine Learning Introduction for Everyone

    Skills you'll gain: Model Evaluation, Predictive Modeling, Machine Learning, Supervised Learning, Applied Machine Learning, Data Science, Artificial Intelligence, Deep Learning, Classification Algorithms, Unsupervised Learning, Regression Analysis, Reinforcement Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    307 reviews

    Beginner · Course · 1 - 4 Weeks

  • J

    John Wiley & Sons

    Data Science and Machine Learning for Business Professionals

    Skills you'll gain: Statistics, Probability & Statistics, Data Analysis, Data Literacy, Data Collection, Unsupervised Learning, Text Mining, Business Analytics, Statistical Inference, Probability, Exploratory Data Analysis, Predictive Modeling, Data-Driven Decision-Making, Data Science, Deep Learning, Machine Learning, Machine Learning Methods, Data Visualization, Business Communication, Communication

    Intermediate · Course · 3 - 6 Months

  • D

    DeepLearning.AI

    AI For Everyone

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

    4.8
    Rating, 4.8 out of 5 stars
    ·
    52K reviews

    Beginner · Course · 1 - 4 Weeks

  • P

    Packt

    Mastering Python for Data Science

    Skills you'll gain: Data Science, Statistical Inference, Data Visualization, Pandas (Python Package), Probability & Statistics, Statistics, Regression Analysis, Apache Hadoop, Big Data, Machine Learning, Data Manipulation, Data Preprocessing, Data Analysis, Analytics, Random Forest Algorithm, Python Programming, Data Mapping, Object Oriented Programming (OOP), JavaScript Frameworks, HTML and CSS

    Intermediate · Course · 1 - 3 Months

  • Status: Trending in AI
    Trending in AI
    I

    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Apache Spark, PyTorch (Machine Learning Library), Large Language Modeling, Retrieval-Augmented Generation, Transfer Learning, Model Evaluation, Computer Vision, Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Vision Transformer (ViT), Keras (Neural Network Library), Supervised Learning, Generative AI Agents, Vector Databases, 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

What brings you to Coursera today?

  • 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), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Statistical Modeling, Exploratory Data Analysis, Statistical Visualization

    4.6
    Rating, 4.6 out of 5 stars
    ·
    679 reviews

    Intermediate · Course · 1 - 4 Weeks

  • C

    Coursera

    Data Science Challenge

    Skills you'll gain: Applied Machine Learning, Jupyter, Machine Learning Algorithms, Model Evaluation, Machine Learning, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis, Technical Communication

    4.6
    Rating, 4.6 out of 5 stars
    ·
    212 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • Status: Trending in AI
    Trending in AI
    I

    IBM

    IBM AI Developer

    Skills you'll gain: Prompt Engineering, Prompt Patterns, Software Development Life Cycle, Retrieval-Augmented Generation, Software Architecture, Computer Vision, LangChain, ChatGPT, Restful API, Responsive Web Design, Generative AI, Responsible AI, IBM Cloud, Data Ethics, Data Import/Export, AI Workflows, Python Programming, Engineering Software, Machine Learning, Data Science

    Build toward a degree

    4.7
    Rating, 4.7 out of 5 stars
    ·
    81K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • 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, Applied Machine Learning, Data Access, Anomaly Detection, Statistical Analysis, Data Analysis, Data Cleansing, Data Manipulation, Data Science, Machine Learning, Probability & Statistics, Data Import/Export

    4.6
    Rating, 4.6 out of 5 stars
    ·
    2.5K reviews

    Intermediate · Course · 1 - 3 Months

  • C

    Coursera

    Machine Learning Pipelines with Azure ML Studio

    Skills you'll gain: Model Deployment, Applied Machine Learning, Predictive Modeling, Microsoft Azure, No-Code Development, Machine Learning, Data Preprocessing, Cloud Deployment, Feature Engineering, Model Evaluation, Data Science, Data Analysis, Classification Algorithms

    4.5
    Rating, 4.5 out of 5 stars
    ·
    826 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Hands-On Projects
    Hands-On Projects
    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, Statistics, Statistical Analysis, Data Science, Tableau Software, 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

1234…28

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

  • IBM Machine Learning: IBM
  • Machine Learning Introduction for Everyone: IBM
  • Data Science and Machine Learning for Business Professionals: John Wiley & Sons
  • AI For Everyone: DeepLearning.AI
  • Mastering Python for Data Science: Packt
  • IBM AI Engineering: IBM
  • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
  • Data Science Challenge: Coursera
  • IBM AI Developer: IBM
  • Exploratory Data Analysis for 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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