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: 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: Application Programming Interface (API), Microsoft Azure, Cloud API, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), User Accounts, Image Analysis, Artificial Intelligence, Cloud Computing, Software Development
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Machine Learning Algorithms, Data Visualization, Dashboard, Classification Algorithms, Interactive Data Visualization, Data Visualization Software, Model Evaluation, Machine Learning, Scikit Learn (Machine Learning Library), Plot (Graphics), Web Applications, Logistic Regression, Predictive Modeling, Data Science, Python Programming, Pandas (Python Package)
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Data Cleansing, Logistic Regression, Data Preprocessing, Applied Machine Learning, Data Import/Export, Data Mining, Python Programming, Google Cloud Platform, Scikit Learn (Machine Learning Library), Predictive Modeling, Classification Algorithms, Machine Learning, Supervised Learning
Intermediate · Guided Project · Less Than 2 Hours

Coursera
Skills you'll gain: Applied Machine Learning, Jupyter, Machine Learning Algorithms, Model Evaluation, Machine Learning, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis, Technical Communication
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Recurrent Neural Networks (RNNs), Exploratory Data Analysis, Deep Learning, Text Mining, Matplotlib, Data Cleansing, Data Analysis, Data Preprocessing, Natural Language Processing, Data Manipulation, Python Programming, Machine Learning, Model Evaluation
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Scikit Learn (Machine Learning Library), Classification Algorithms, Applied Machine Learning, Machine Learning Algorithms, Supervised Learning, Random Forest Algorithm, Machine Learning, Unsupervised Learning, Data Analysis
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Feature Engineering, Data Cleansing, Data Visualization, Data Preprocessing, Logistic Regression, Predictive Modeling, Applied Machine Learning, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning, Model Evaluation, Classification Algorithms, Data Science, Python Programming
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Data Import/Export, Model Evaluation, No-Code Development, Exploratory Data Analysis, Predictive Modeling, Applied Machine Learning, Data Pipelines, Data Manipulation, Data Preprocessing, Machine Learning, Time Series Analysis and Forecasting
Beginner · Guided Project · Less Than 2 Hours

Coursera
Skills you'll gain: Regression Analysis, NumPy, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Modeling, Deep Learning, Data Science, Python Programming
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Bash (Scripting Language), Python Programming, Unix Shell, Command-Line Interface, Programming Principles, Computer Programming, Scripting, Scripting Languages, Development Environment, Data Structures
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Machine Learning Algorithms, Applied Machine Learning, Predictive Analytics, Python Programming, Classification Algorithms, Machine Learning, Data Analysis
Beginner · Guided Project · Less Than 2 Hours
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.‎