• A course covers a specific ML topic—such as Unsupervised Learning, Recommenders, Reinforcement Learning.
• A Specialization includes multiple courses to build structured knowledge—like the Machine Learning Specialization, which teaches supervised, unsupervised, and advanced techniques.
• A Professional Certificate prepares you for job roles with end-to-end training, tools, and projects—such as the IBM Machine Learning Certificate or AI Engineering Certificate.‎

Yes. You can start learning machine learning on Coursera for free in two ways:

• Preview the first module of many machine learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
• Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

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.‎

Machine learning courses on Coursera cover a range of essential skills including:

• Supervised learning and unsupervised learning methodologies
• Neural networks and deep learning architectures
• Algorithmic foundations such as regression, classification, and clustering
• Data preprocessing, feature engineering, and data augmentation techniques
• Model evaluation and optimization methods
• Practical applications of machine learning in various industries
• Implementation of machine learning algorithms using Python and libraries like TensorFlow and PyTorch‎

No prior programming experience is required to begin beginner machine learning courses, but having some foundational knowledge in programming (especially Python) can be very beneficial. The curriculum is structured to accommodate learners at all levels:

• Beginners can start with introductory courses that cover basic programming and data handling.
• Intermediate learners can advance to applying machine learning algorithms on real data.
• Advanced professionals can explore specialized areas such as deep learning, reinforcement learning, or natural language processing.‎

Skills in machine learning can open doors to numerous high-demand roles in technology and research, including:

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• Robotics Engineer
• Quantitative Analyst in finance
• Software Developer specializing in AI
• Professionals in these roles utilize machine learning techniques to create innovative solutions, improve data-driven decision-making, and push the boundaries of artificial intelligence.

Discover which machine learning role suits you best by taking our career quiz!‎

Online learning algorithms are machine learning methods that update models continuously as new data arrives, rather than training on a fixed dataset. They’re useful for real-time applications like fraud detection or recommendation systems. You can explore these concepts in courses like Machine Learning by Stanford University on Coursera, which introduces foundational techniques used in adaptive models.‎

Causal inference in machine learning focuses on identifying cause-and-effect relationships rather than just correlations. It’s used in fields like healthcare, economics, and policy to make more reliable predictions and decisions. Courses like A Crash Course in Causality: Inferring Causal Effects from Observational Data from the University of Pennsylvania on Coursera offer a strong introduction to these methods.‎