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.

Skills you'll gain: Feature Engineering, Model Evaluation, Advanced Analytics, Statistical Machine Learning, Unsupervised Learning, Machine Learning, Data Ethics, Supervised Learning, Decision Tree Learning, Random Forest Algorithm, Classification Algorithms, Python Programming, Performance Tuning
Advanced · Course · 1 - 3 Months

Duke University
Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis
Advanced · Specialization · 3 - 6 Months

Google Cloud
Skills you'll gain: Model Deployment, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Reinforcement Learning, Transfer Learning, Computer Vision, Systems Design, Machine Learning Methods, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Hybrid Cloud Computing, Systems Architecture, Performance Tuning, Embeddings
Advanced · Specialization · 3 - 6 Months

Skills you'll gain: Prompt Engineering, AI Orchestration, AI Workflows, LangChain, Retrieval-Augmented Generation, Agentic Workflows, Tool Calling, LangGraph, LLM Application, Agentic systems, Multimodal Prompts, Generative AI, AI Security, Generative AI Agents, Vector Databases, Generative Model Architectures, OpenAI API, Responsible AI, Embeddings, Software Development
Advanced · Professional Certificate · 3 - 6 Months

Skills you'll gain: Data Storytelling, Data Visualization, Exploratory Data Analysis, Regression Analysis, Data Presentation, Feature Engineering, Statistical Hypothesis Testing, Sampling (Statistics), Data Ethics, Logistic Regression, Model Evaluation, Data Visualization Software, Data Analysis, Statistical Analysis, Tableau Software, Object Oriented Programming (OOP), Data Science, Machine Learning, Interviewing Skills, Python Programming
Build toward a degree
Advanced · Professional Certificate · 3 - 6 Months

Skills you'll gain: Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Data Analysis, Applied Machine Learning, Statistical Analysis, Data Mining, Predictive Modeling, Machine Learning, Technical Communication, Scikit Learn (Machine Learning Library), Regression Analysis, Artificial Neural Networks, Deep Learning, Python Programming
Advanced · Course · 1 - 3 Months
Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Data Preprocessing, Exploratory Data Analysis, Logistic Regression, Statistical Machine Learning, Model Evaluation, Supervised Learning, Decision Tree Learning, Probability & Statistics, Statistics, Machine Learning Software, Classification And Regression Tree (CART), Workflow Management, Predictive Modeling, Random Forest Algorithm, Feature Engineering, SAS (Software), Machine Learning, Applied Machine Learning
Advanced · Professional Certificate · 3 - 6 Months
University of Illinois Urbana-Champaign
Skills you'll gain: Deep Learning, Convolutional Neural Networks, Health Informatics, Autoencoders, Recurrent Neural Networks (RNNs), Image Analysis, Embeddings, Machine Learning, Applied Machine Learning, Health Care, Model Deployment, Generative Adversarial Networks (GANs), Artificial Neural Networks, Healthcare Project Management, Supervised Learning, Model Evaluation, Machine Learning Methods, Graph Theory, Medical Science and Research, Big Data
Advanced · Specialization · 1 - 3 Months

University of Toronto
Skills you'll gain: Computer Vision, Convolutional Neural Networks, Image Analysis, Control Systems, Robotics, Embedded Software, Automation, Deep Learning, Software Architecture, Simulations, Safety Assurance, Traffic Flow Optimization, Artificial Neural Networks, Global Positioning Systems, Machine Controls, Hardware Architecture, Systems Architecture, Graph Theory, Estimation, Machine Learning Methods
Advanced · Specialization · 3 - 6 Months

University of Colorado Boulder
Skills you'll gain: Hardware Architecture, Bash (Scripting Language), Distributed Computing, Scalability, Computer Systems, File I/O, Big Data, Operating Systems, File Systems, Scripting, Command-Line Interface, C and C++, Performance Tuning, Computer Programming Tools, Linux, Load Balancing, Programming Principles, Communication Systems, Linear Algebra
Advanced · Specialization · 3 - 6 Months
Duke University
Skills you'll gain: Neurology, Physiology, Anatomy, Cell Biology, Psychology, Pathology, Molecular Biology
Advanced · Course · 3 - 6 Months

Peking University
Skills you'll gain: Oral Comprehension, Literacy, Language Competency, Vocabulary, Grammar, Language Learning
Advanced · Course · 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.‎