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.

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

Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Autoencoders, Feature Engineering, Dimensionality Reduction, Supervised Learning, 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, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Deep Learning, Data Science, Machine Learning, Python Programming
★ 4.6 (3.7K) · Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Retrieval-Augmented Generation, PyTorch (Machine Learning Library), Computer Vision, Unsupervised Learning, Generative Model Architectures, Generative AI, Model Optimization, Keras (Neural Network Library), Supervised Learning, LLM Application, Generative AI Agents, Model Evaluation, Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science
★ 4.6 (22K) · Intermediate · Professional Certificate · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Responsible AI, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, AI literacy, Machine Learning, Data Science, Deep Learning, Artificial Neural Networks
★ 4.8 (53K) · Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Model Deployment, Decision Tree Learning, Applied Machine Learning, Predictive Modeling, Microsoft Azure, No-Code Development, Machine Learning, Data Preprocessing, Model Training, Cloud Deployment, Machine Learning Algorithms, Model Evaluation, Data Science, Data Analysis, Classification Algorithms
★ 4.5 (836) · Beginner · Guided Project · Less Than 2 Hours

Imperial College London
Skills you'll gain: Linear Algebra, Applied Machine Learning, Data Science, Data Manipulation, Data Transformation, Machine Learning
★ 4.6 (13K) · Beginner · Course · 1 - 3 Months

IBM
Skills you'll gain: Prompt Engineering, Prompt Patterns, Unit Testing, Software Development Life Cycle, Generative AI, Retrieval-Augmented Generation, Large Language Modeling, Software Architecture, Responsible AI, Computer Vision, LangChain, LLM Application, Javascript, IBM Cloud, Data Ethics, Python Programming, Application Deployment, Software Development, Machine Learning, Data Science
★ 4.6 (83K) · Beginner · Professional Certificate · 3 - 6 Months

John Wiley & Sons
Skills you'll gain: Supervised Learning, Machine Learning Methods, Applied Machine Learning, Computer Vision, Machine Learning Algorithms, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Natural Language Processing, Deep Learning, Artificial Neural Networks, Decision Tree Learning, Data Science, Logistic Regression, Data Preprocessing, Embeddings
Advanced · Course · 1 - 4 Weeks

Skills you'll gain: Prompt Engineering, Prompt Patterns, Generative AI, Responsible AI, IBM Cloud, ChatGPT, AI literacy, Retrieval-Augmented Generation, Application Deployment, Artificial Intelligence and Machine Learning (AI/ML), AI Enablement, Workflow Management, Machine Learning, No-Code Development, Artificial Intelligence, Natural Language Processing, Deep Learning, Customer Service, AI Integrations, Data Science
★ 4.7 (37K) · Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Statistical Methods, Data Preprocessing, Statistical Inference, Statistical Hypothesis Testing, Data Access, Statistics, Statistical Analysis, Data Analysis, Data Cleansing, Data Science, Machine Learning, Probability & Statistics, Artificial Intelligence
★ 4.6 (2.6K) · Intermediate · Course · 1 - 3 Months

Skills you'll gain: Responsible AI, Exploratory Data Analysis, Data Storytelling, Dashboard, Data Presentation, Model Evaluation, Dashboard Creation, Interactive Data Visualization, No-Code Development, Business Analytics, Data Science, Analytics, Model Training, Model Deployment, Model Optimization, Applied Machine Learning, Machine Learning, Data Analysis, Predictive Modeling, Data Visualization
Beginner · Specialization · 1 - 3 Months

Coursera
Skills you'll gain: Applied Machine Learning, Predictive Modeling, Model Evaluation, Model Optimization, Data Analysis, Machine Learning, Data Science, Python Programming
★ 4.7 (230) · Intermediate · Guided Project · Less Than 2 Hours
Top-rated Machine Learning courses offered by IBM on Coursera.
Earn a certificate in Machine Learning from top universities and companies.
Top-rated beginner-friendly Machine Learning courses with no prerequisites.
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.‎