Machine Learning | Coursera

# Machine Learning | Coursera > Source: https://www.coursera.org/specializations/machine-learning-introduction Machine Learning | Coursera For Individuals For Businesses For Universities For Governments Explore Degrees ​ Log In Join for Free Join for Free Machine Learning Specialization About Outcomes Courses Testimonials Previous Next Browse Data Science Machine Learning Ends in 3 days! This point in the year is perfect for 40% off 10,000+ programs. Save now. Machine Learning Specialization #BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng Instructors: Andrew Ng +3 more Top Instructor Enroll for free Starts Jul 12 808,540 already enrolled Ask Coursera Is this right for me? 3 course series Get in-depth knowledge of a subject Beginner level Recommended experience 2 months to complete at 10 hours a week Flexible schedule Learn at your own pace 3 course series Get in-depth knowledge of a subject Beginner level Recommended experience 2 months to complete at 10 hours a week Flexible schedule Learn at your own pace About Outcomes Courses Testimonials Previous Next What you'll learn Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression) Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model Skills you'll gain Predictive Modeling Responsible AI Unsupervised Learning Supervised Learning Model Training Deep Learning Decision Tree Learning Machine Learning Artificial Intelligence Model Evaluation Data Ethics Reinforcement Learning Transfer Learning Machine Learning Algorithms Applied Machine Learning Show all Tools you'll learn Classification Algorithms Scikit Learn (Machine Learning Library) NumPy Jupyter Tensorflow Details to know Shareable certificate Add to your LinkedIn profile Taught in English 30 languages available See how employees at top companies are mastering in-demand skills Learn more about Coursera for Business Specialization - 3 course series The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. Applied Learning Project By the end of this Specialization, you will be ready to: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. • Build and train a neural network with TensorFlow to perform multi-class classification. • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. • Build and use decision trees and tree ensemble methods, including random forests and boosted trees. • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. Supervised Machine Learning: Regression and Classification Course 1 , 33 hours Course 1 • 33 hours Course details What you'll learn Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression Skills you'll gain Category: Regression Analysis Regression Analysis Category: Logistic Regression Logistic Regression Category: Supervised Learning Supervised Learning Category: Feature Engineering Feature Engineering Category: Classification Algorithms Classification Algorithms Category: Model Training Model Training Category: Model Optimization Model Optimization Category: Python Programming Python Programming Category: Model Evaluation Model Evaluation Category: Algorithms Algorithms Category: NumPy NumPy Category: Artificial Intelligence Artificial Intelligence Category: Machine Learning Machine Learning Category: Data Preprocessing Data Preprocessing Category: Jupyter Jupyter Category: Scikit Learn (Machine Learning Library) Scikit Learn (Machine Learning Library) Category: Predictive Modeling Predictive Modeling Category: Applied Machine Learning Applied Machine Learning Category: Machine Learning Algorithms Machine Learning Algorithms Advanced Learning Algorithms Course 2 , 34 hours Course 2 • 34 hours Course details What you'll learn Build and train a neural network with TensorFlow to perform multi-class classification Apply best practices for machine learning development so that your models generalize to data and tasks in the real world Build and use decision trees and tree ensemble methods, including random forests and boosted trees Skills you'll gain Category: Artificial Neural Networks Artificial Neural Networks Category: Tensorflow Tensorflow Category: Decision Tree Learning Decision Tree Learning Category: Model Evaluation Model Evaluation Category: Model Training Model Training Category: Classification Algorithms Classification Algorithms Category: Model Optimization Model Optimization Category: Random Forest Algorithm Random Forest Algorithm Category: Machine Learning Machine Learning Category: Transfer Learning Transfer Learning Category: Machine Learning Algorithms Machine Learning Algorithms Category: Supervised Learning Supervised Learning Category: Deep Learning Deep Learning Category: Responsible AI Responsible AI Category: Applied Machine Learning Applied Machine Learning Category: Logistic Regression Logistic Regression Category: Data Ethics Data Ethics Category: Fine-tuning Fine-tuning Unsupervised Learning, Recommenders, Reinforcement Learning Course 3 , 28 hours Course 3 • 28 hours Course details What you'll learn Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection Build recommender systems with a collaborative filtering approach and a content-based deep learning method Build a deep reinforcement learning model Skills you'll gain Category: Reinforcement Learning Reinforcement Learning Category: Anomaly Detection Anomaly Detection Category: Unsupervised Learning Unsupervised Learning Category: Artificial Neural Networks Artificial Neural Networks Category: Supervised Learning Supervised Learning Category: Deep Learning Deep Learning Category: Applied Machine Learning Applied Machine Learning Category: Artificial Intelligence Artificial Intelligence Category: Dimensionality Reduction Dimensionality Reduction Category: Machine Learning Machine Learning Category: Data Ethics Data Ethics Category: Responsible AI Responsible AI Earn a career certificate Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review. Instructors Top Instructor Andrew Ng Stanford University 51 Courses • 9,851,014 learners View all 4 instructors Offered by Stanford University Learn more DeepLearning.AI Learn more Why people choose Coursera for their career Felipe M. Learner since 2018 "To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood." Jennifer J. Learner since 2020 "I directly applied the concepts and skills I learned from my courses to an exciting new project at work." Larry W. Learner since 2021 "When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go." Chaitanya A. "Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits." Get midyear savings and gain career momentum Save now Add momentum to your team Save 40% now Frequently asked questions What is machine learning? Machine learning is a branch of artificial intelligence that enables algorithms to automatically learn from data without being explicitly programmed. Its practitioners train algorithms to identify patterns in data and to make decisions with minimal human intervention. In the past two decades, machine learning has gone from a niche academic interest to a central part of the tech industry. It has given us self-driving cars, speech and image recognition, effective web search, fraud detection, a vastly improved understanding of the human genome, and many other advances. Amid this explosion of applications, there is a shortage of qualified data scientists, analysts, and machine learning engineers, making them some of the world’s most in-demand professionals. What is the Machine Learning Specialization about? The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. What will I learn in the Machine Learning Specialization? By the end of this Specialization, you will be ready to • Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression) • Build and train a neural network with TensorFlow to perform multi-class classification. • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. • Build and use decision trees and tree ensemble methods, including random forests and boosted trees. • Use unsupervised learning techniques for unsupervised learning including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. What background knowledge is necessary for the Machine Learning Specialization? Learners should understand basic coding (for loops, functions, if/else statements) and high school-level math (arithmetic, algebra). Any additional math concepts will be explained along the way. Who is the Machine Learning Specialization for? The Machine Learning Specialization is a beginner-level program aimed at those new to AI and looking to gain a foundational understanding of machine learning models and real-world experience building systems using Python. This Specialization is suitable for learners with some basic knowledge of programming and high-school level math, as well as early-stage professionals in software engineering and data analysis who wish to upskill in machine learning. How long does it take to complete the Machine Learning Specialization? This Specialization consists of three courses. At the rate of 5 hours per week, it will take 3 weeks to complete Course 1, 4 weeks to complete Course 2, and 3 weeks to complete Course 3 of the Machine Learning Specialization. Who created the Machine Learning Specialization? This Specialization was created by Andrew Ng, Eddy Shyu, Aarti Bagul, and Geoff Ladwig. Andrew Ng Opens in a new tab is the Founder of DeepLearning.AI, Founder and CEO of Landing AI, Chairman and Co-founder of Coursera, and an Adjunct Professor at Stanford University. Dr. Ng has changed countless lives through his work, authoring or co-authoring over 200 research papers in machine learning, robotics, and related fields. He was the founding lead of the Google Brain team and Chief Scientist at Baidu, and through this work built the teams that led the AI transformation of two leading internet companies. He is the co-founder and Chairman of Coursera — the world's largest online learning platform — which had started with his machine learning course. Dr. Ng now focuses primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy. Eddy Shyu Opens in a new tab is a product lead at DeepLearning.AI and has led the teams that built the Machine Learning Specialization (featuring Andrew Ng), TensorFlow Advanced Techniques (featuring Laurence Moroney), as well as the Natural Language Processing Specialization, and AI for Medicine Specialization. Eddy was also co-instructor for Udacity's AI for Trading Nanodegree program. Aarti Bagul Opens in a new tab is a machine learning engineer at Snorkel AI. Before Snorkel, she worked closely with Andrew Ng in various capacities: At the AI Fund, she helped build and invest in machine learning companies. Previously, she was a machine learning engineer at Landing AI and was the head teacher’s assistant for Dr. Ng’s deep learning class at Stanford University. She graduated with a Master's in Computer Science from Stanford and a Bachelor's in Computer Science and Computer Engineering from NYU with the highest honors. Geoff Ladwig Opens in a new tab started as a Deep Learning student and a mentor for the Deep Learning Specialization. He worked as a consultant on the Natural Language Processing Specialization and as a Curriculum Engineer on the Machine Learning Specialization. Geoff has spent most of his career as an ASIC/Hardware/System engineer/architect in the communications and computer industries. What makes the Machine Learning Specialization so unique? The Machine Learning Specialization is a foundational online program taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. This program has been designed to teach you foundational machine learning concepts without prior math knowledge or a rigorous coding background. Unlike the original course, which required some knowledge of math, the new Specialization aptly balances intuition, code practice, and mathematical theory to create a simple and effective learning experience for first-time students. Each lesson begins with a visual representation of machine learning concepts and a high-level explanation of the intuition behind them. It then provides the code to help you implement these algorithms and additional videos explaining the underlying math if you wish to dive deeper. These lessons are optional and are not required to complete the Specialization or apply machine learning to real-world projects. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. How is the new Machine Learning Specialization different from the original course? The new Machine Learning Specialization includes an expanded list of topics that focus on the most crucial machine learning concepts (such as decision trees) and tools (such as TensorFlow). Unlike the original course, the new Specialization is designed to teach foundational ML concepts without prior math knowledge or a rigorous coding background. It aptly balances intuition, code practice, and mathematical theory to create a simple and effective learning experience for first-time students. Each lesson begins with a visual representation of machine learning concepts and a high-level explanation of the intuition behind them. It then provides the code to help you implement these algorithms and additional videos explaining the underlying math if you wish to dive deeper. These lessons are optional and are not required to complete the Specialization or apply machine learning to real-world projects. In the decade since the first Machine Learning course debuted, Python has become the primary programming language for AI applications. The assignments and lectures in the new Specialization have been rebuilt to use Python rather than Octave, like in the original course. Before the graded programming assignments, there are additional ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises. The section on practical advice on applying machine learning has been updated significantly based on emerging best practices from the last decade. I'm a complete beginner. Can I take this Specialization? The new Machine Learning Specialization is the best entry point for beginners looking to break into the AI field or kick start their machine learning careers. This updated Specialization takes the core curriculum — which has been vetted by millions of learners over the years — and makes it more approachable for beginners. Unlike the original course, the new Specialization is designed to teach foundational ML concepts without prior math knowledge or a rigorous coding background. It aptly balances intuition, code practice, and mathematical theory to create a simple and effective learning experience for first-time students. Each lesson begins with a visual representation of machine learning concepts and a high-level explanation of the intuition behind them. It then provides the code to help you implement these algorithms and additional videos explaining the underlying math if you wish to dive deeper. These lessons are optional and are not required to complete the Specialization or apply machine learning to real-world projects. The new Machine Learning Specialization includes an expanded list of topics that focus on the most crucial machine learning concepts (such as decision trees) and tools (such as TensorFlow). In the decade since the first Machine Learning course debuted, Python has become the primary programming language for AI applications. The assignments and lectures in the new Specialization have been rebuilt to use Python rather than Octave, like in the original course. Before the graded programming assignments, there are additional ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises. The section on practical advice on applying machine learning has been updated significantly based on emerging best practices from the last decade. I enrolled in but couldn’t complete the original Machine Learning course. Can I take the new Machine Learning Specialization? If you enrolled in but didn’t complete the original course because you may have been discouraged by the math requirements or didn’t know if you would be able to keep up with the lessons, then the new Machine Learning Specialization is for you. This updated Specialization takes the core curriculum — which has been vetted by millions of learners over the years — and makes it more approachable for beginners. Unlike the original course, the new Specialization is designed to teach foundational ML concepts without prior math knowledge or a rigorous coding background. It aptly balances intuition, code practice, and mathematical theory to create a simple and effective learning experience for first-time students. Each lesson begins with a visual representation of machine learning concepts and a high-level explanation of the intuition behind them. It then provides the code to help you implement these algorithms and additional videos explaining the underlying math if you wish to dive deeper. These lessons are optional and are not required to complete the Specialization or apply machine learning to real-world projects. The new Machine Learning Specialization includes an expanded list of topics that focus on the most crucial machine learning concepts (such as decision trees) and tools (such as TensorFlow). In the decade since the first Machine Learning course debuted, Python has become the primary programming language for AI applications. The assignments and lectures in the new Specialization have been rebuilt to use Python rather than Octave, like in the original course. Before the graded programming assignments, there are additional ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises. The section on practical advice on applying machine learning has been updated significantly based on emerging best practices from the last decade. I’ve completed the original Machine Learning course. Should I take the new Machine Learning Specialization? Congratulations on completing the original Machine Learning course! This new Specialization is an excellent way to refresh the foundational concepts you have learned. The new Machine Learning Specialization includes an expanded list of topics that focus on the most crucial machine learning concepts (such as decision trees) and tools (such as TensorFlow). In the decade since the first Machine Learning course debuted, Python has become the primary programming language for AI applications. The assignments and lectures in the new Specialization have been rebuilt to use Python rather than Octave, like in the original course. Before the graded programming assignments, there are additional ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises. The section on practical advice on applying machine learning has been updated significantly based on emerging best practices from the last decade. I’ve completed the Deep Learning Specialization. Should I take the new Machine Learning Specialization? Congratulations on completing the Deep Learning Specialization! Compared to the more advanced Deep Learning Specialization, the new Machine Learning Specialization covers topics such as unsupervised learning, recommender systems, tree-based models, and other commonly used traditional machine learning algorithms not based on neural networks. If you are already a working AI professional, refreshing your knowledge base and learning about these latest techniques will help you advance your career. Is this a standalone course or a Specialization? The Machine Learning Specialization is made up of 3 courses. Do I need to take the courses in a specific order? We recommend taking the courses in the prescribed order for a logical and thorough learning experience. How much does the Specialization cost? A Coursera subscription costs $49 / month. Can I apply for financial aid? Yes, Coursera provides financial aid to learners who cannot afford the fee. How do I get a receipt to get this reimbursed by my employer? ∙ Go to your Coursera account. ∙ Click on My Purchases and find the relevant course or Specialization. ∙ Click Email Receipt and wait up to 24 hours to receive the receipt. You can read more about it here Opens in a new tab . I want to purchase this Specialization for my employees. How can I do that? Visit coursera.org/business Opens in a new tab for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from. Will I earn university credit for completing the Specialization? No Will I receive a certificate at the end of the Specialization? You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate. If you complete all 3 courses and are subscribed to the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization. Is this course really 100% online? Do I need to attend any classes in person? This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device. Can I just enroll in a single course? Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress. Is financial aid available? Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page. Can I take the course for free? No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid. Show all 25 frequently asked questions More questions Visit the learner help center Financial aid available, learn more ¹ Median salary and job opening data are sourced from Lightcast™ Job Postings Report. Content Creator, Machine Learning Engineer and Salesforce Development Representative (1/1/2024 - 12/31/2024) All other job roles (7/1/2025 - 7/1/2026) Enroll for free Starts Jul 12 Coursera Footer Skills Accounting Artificial Intelligence (AI) Cybersecurity Data Analytics Digital Marketing Human Resources (HR) Microsoft Excel Project Management Python SQL Professional Certificates Google AI Certificate Google Cybersecurity Certificate Google Data Analytics Certificate Google IT Support Certificate Google Project Management Certificate Google UX Design Certificate IBM AI Engineering Certificate IBM AI Product Manager Certificate IBM Data Science Certificate Intuit Academy Bookkeeping Certificate Courses & Specializations AI Essentials Specialization AI For Business Specialization AI For Everyone Course AI in Healthcare Specialization Deep Learning Specialization Excel Skills for Business Specialization Financial Markets Course Machine Learning Specialization Prompt Engineering for ChatGPT Course Python for Everybody Specialization Career Resources Career Aptitude Test CAPM Certification Requirements CompTIA A+ Certification Requirements CompTIA Security+ Certification Requirements Essential IT Certifications High-Income Skills to Learn How to Learn Artificial Intelligence PMP Certification Requirements Popular Cybersecurity Certifications Share your Coursera learning story Coursera About What We Offer Leadership Careers Catalog Coursera Plus Professional Certificates MasterTrack® Certificates Degrees For Enterprise For Government For Campus Become a Partner Social Impact Free Courses Udemy Community Learners Partners Beta Testers Blog The Coursera Podcast Tech Blog More Press Investors Terms Privacy Help Accessibility Contact Articles Directory Affiliates Modern Slavery Statement Cookies Preference Center Learn Anywhere © 2026 Coursera Inc. All rights reserved.

Machine Learning | Coursera Source: Machine Learning | Coursera For Individuals For Businesses For Universities For Governments Explore Degrees ​ Log In Join for Free Join for Free Machine Learning Specialization About Outcomes Courses Testimonials Previous Next Browse Data Science Machine Learning Ends in 3 days! This point in the year is perfect for 40% off 10,000+ programs. Save now. Machine Learning Specialization #BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng Instructors: Andrew Ng +3 more Top Instructor Enroll for free Starts Jul 12 808,540 already enrolled Ask Coursera Is this right for me? 3 course series Get in-depth knowledge of a subject Beginner level Recommended experience 2 months to complete at 10 hours a week Flexible … Ứng dụng: nối nghiên cứu với ai, USD, lãi suất và risk regime — đưa vào journal và playbook. DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường.