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, 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

Imperial College London
Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives
Beginner · Specialization · 3 - 6 Months

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

Skills you'll gain: Responsible AI, Generative AI, Natural Language Processing, Robotics, Business Logic, Risk Mitigation
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
University of London
Skills you'll gain: Data Preprocessing, Machine Learning, Artificial Intelligence, Model Evaluation, Data Analysis, Image Analysis, Data Collection, Classification Algorithms
Build toward a degree
Beginner · Course · 1 - 4 Weeks

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

Amazon Web Services
Skills you'll gain: Model Evaluation, MLOps (Machine Learning Operations), AWS SageMaker, Amazon Web Services, AI Workflows, Model Deployment, Machine Learning, Applied Machine Learning
Beginner · Course · 1 - 4 Weeks

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
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Prompt Engineering, Prompt Patterns, ChatGPT, Generative AI, Responsible AI, IBM Cloud, AI Workflows, No-Code Development, Model Deployment, Natural Language Processing, AI Enablement, Analytics, Data Analysis, Artificial Intelligence, Self Service Technologies, Application Deployment, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Science
Beginner · Specialization · 3 - 6 Months

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

Skills you'll gain: Generative AI, Google Cloud Platform, MLOps (Machine Learning Operations), Prompt Engineering, Tensorflow, AI Workflows, Cloud Infrastructure, Artificial Intelligence, Big Data, Model Deployment, Machine Learning, Supervised Learning
Beginner · Course · 1 - 3 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.