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.

Multiple educators
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

Imperial College London
Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Unsupervised Learning, Applied Mathematics, Statistical Methods, Descriptive Statistics, Model Optimization, Mathematical Software, Jupyter, Statistics, Numerical Analysis, Applied Machine Learning, Geometry, Artificial Neural Networks, Data Science, Data Manipulation, Data Transformation
★ 4.6 (15K) · Beginner · Specialization · 3 - 6 Months

DeepLearning.AI
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

Skills you'll gain: Responsible AI, Generative AI, Generative Model Architectures, LLM Application, AI literacy, Natural Language Processing, Robotics, Risk Mitigation
★ 4.7 (23K) · Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Python Programming, NumPy, Data Analysis
★ 4.6 (44K) · Beginner · Course · 1 - 3 Months

Skills you'll gain: Model Evaluation, Predictive Modeling, Model Training, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Supervised Learning, Applied Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Deep Learning, Classification Algorithms, Unsupervised Learning, Regression Analysis, Reinforcement Learning
★ 4.6 (309) · Beginner · Course · 1 - 4 Weeks

Amazon Web Services
Skills you'll gain: Model Evaluation, MLOps (Machine Learning Operations), Model Training, Amazon Web Services, AI Workflows, Model Deployment, Machine Learning Methods, Machine Learning, Applied Machine Learning
★ 4.5 (117) · Beginner · Course · 1 - 4 Weeks
University of London
Skills you'll gain: Model Training, Applied Machine Learning, Feature Engineering, Machine Learning Software, Machine Learning, Machine Learning Methods, Artificial Intelligence, Statistical Machine Learning, Model Evaluation, Machine Learning Algorithms, AI literacy, Test Data, Data Collection, Classification Algorithms
★ 4.7 (3.5K) · Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Prompt Engineering, Prompt Patterns, Data Wrangling, Large Language Modeling, LangChain, Retrieval-Augmented Generation, Exploratory Data Analysis, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, Prompt Engineering Tools, LLM Application, Keras (Neural Network Library), Responsible AI, Vector Databases, Fine-tuning, Python Programming
★ 4.7 (99K) · Beginner · Professional Certificate · 3 - 6 Months

Skills you'll gain: Generative AI, Generative AI Agents, Google Gemini, Google Cloud Platform, Generative Model Architectures, MLOps (Machine Learning Operations), Prompt Engineering, Tensorflow, AI Workflows, Cloud Infrastructure, Applied Machine Learning, Artificial Intelligence, Data Infrastructure, Multimodal Prompts, Model Deployment, Model Training, Machine Learning, Model Evaluation, Supervised Learning
★ 4.6 (327) · Beginner · Course · 1 - 3 Months

DeepLearning.AI
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 (52K) · Beginner · Course · 1 - 4 Weeks

IBM
Skills you'll gain: Prompt Engineering, Prompt Patterns, Software Development Life Cycle, Retrieval-Augmented Generation, Large Language Modeling, Software Architecture, Computer Vision, LangChain, ChatGPT, Restful API, Responsive Web Design, Generative AI, Responsible AI, IBM Cloud, Data Ethics, AI Workflows, Python Programming, Software Development, Machine Learning, Data Science
★ 4.7 (81K) · Beginner · Professional Certificate · 3 - 6 Months
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:
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.‎