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.

University of Washington
Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Predictive Modeling, Supervised Learning, Bayesian Statistics, Logistic Regression, Statistical Modeling, Artificial Intelligence, Data Preprocessing, Deep Learning, Data Mining, Decision Tree Learning, Computer Vision, Statistical Machine Learning
Intermediate · Specialization · 3 - 6 Months

Duke University
Skills you'll gain: PyTorch (Machine Learning Library), Logistic Regression, Transfer Learning, Reinforcement Learning, Convolutional Neural Networks, Deep Learning, Image Analysis, Applied Machine Learning, Natural Language Processing, Machine Learning, Recurrent Neural Networks (RNNs), Artificial Neural Networks, Supervised Learning, Unsupervised Learning, Python Programming, Computer Vision, Medical Imaging
Intermediate · Course · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing
Beginner · Specialization · 1 - 3 Months

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
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Unsupervised Learning, Supervised Learning, Classification Algorithms, Regression Analysis, Dimensionality Reduction, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Data Access, Anomaly Detection, Logistic Regression, Scikit Learn (Machine Learning Library), Machine Learning, Machine Learning Algorithms
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering
Intermediate · Course · 1 - 3 Months

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
Beginner · Course · 1 - 4 Weeks

Microsoft
Skills you'll gain: Unsupervised Learning, Microsoft Azure, Applied Machine Learning, MLOps (Machine Learning Operations), Regression Analysis, Predictive Modeling, Machine Learning, No-Code Development, Artificial Intelligence and Machine Learning (AI/ML), Model Deployment, Artificial Intelligence, Classification Algorithms, Supervised Learning
Beginner · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning
Beginner · Course · 1 - 4 Weeks

O.P. Jindal Global University
Skills you'll gain: Model Evaluation, Supervised Learning, Scikit Learn (Machine Learning Library), Tensorflow, Applied Machine Learning, Artificial Neural Networks, Python Programming, NumPy, Matplotlib, Deep Learning, Image Analysis, Machine Learning, Embeddings, Pandas (Python Package), Convolutional Neural Networks, Natural Language Processing, Regression Analysis
Build toward a degree
Beginner · Course · 1 - 3 Months

Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Advanced Analytics, Analytics, Statistical Machine Learning, Machine Learning, Scikit Learn (Machine Learning Library), Unsupervised Learning, Machine Learning Algorithms, Workflow Management, Data Ethics, Supervised Learning, Data Preprocessing, Random Forest Algorithm, Decision Tree Learning, Verification And Validation, Python Programming, Classification Algorithms, Performance Tuning
Advanced · Course · 1 - 3 Months
University of Michigan
Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Random Forest Algorithm, Regression Analysis, Classification Algorithms, Artificial Neural Networks
Intermediate · Course · 1 - 4 Weeks
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.‎