Question 1

What is machine learning?

Accepted Answer

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.

Question 2

What is the Machine Learning Specialization about?

Accepted Answer

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.

Question 3

What will I learn in the Machine Learning Specialization?

Accepted Answer

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.

Question 4

What background knowledge is necessary for the Machine Learning Specialization?

Accepted Answer

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.

Question 5

Who is the Machine Learning Specialization for?

Accepted Answer

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.

Question 6

How long does it take to complete the Machine Learning Specialization?

Accepted Answer

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.

Question 7

Who created the Machine Learning Specialization?

Accepted Answer

Question 8

What makes the Machine Learning Specialization so unique?

Accepted Answer

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.

Question 9

How is the new Machine Learning Specialization different from the original course?

Accepted Answer

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.

Question 10

I&#x27;m a complete beginner. Can I take this Specialization?

Accepted Answer

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.

Question 11

I enrolled in but couldn’t complete the original Machine Learning course. Can I take the new Machine Learning Specialization?

Accepted Answer

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.

Question 12

I’ve completed the original Machine Learning course. Should I take the new Machine Learning Specialization?

Accepted Answer

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.

Question 13

I’ve completed the Deep Learning Specialization. Should I take the new Machine Learning Specialization?

Accepted Answer

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.

Question 14

Is this a standalone course or a Specialization?

Accepted Answer

The Machine Learning Specialization is made up of 3 courses.

Question 15

Do I need to take the courses in a specific order?

Accepted Answer

We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

Question 16

How much does the Specialization cost?

Accepted Answer

A Coursera subscription costs $49 / month.

Question 17

Can I apply for financial aid?

Accepted Answer

Yes, Coursera provides financial aid to learners who cannot afford the fee.

Question 18

How do I get a receipt to get this reimbursed by my employer?

Accepted Answer

Question 19

I want to purchase this Specialization for my employees. How can I do that?

Accepted Answer

Question 20

Will I earn university credit for completing the Specialization?

Accepted Answer

No

Question 21

Will I receive a certificate at the end of the Specialization?

Accepted Answer

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.

Question 22

Is this course really 100% online? Do I need to attend any classes in person?

Accepted Answer

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.

Question 23

Can I just enroll in a single course?

Accepted Answer

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.

Question 24

Is financial aid available?

Accepted Answer

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.

Question 25

Can I take the course for free?

Accepted Answer

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.

Machine Learning Specialization

Machine Learning Specialization

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Specialization - 3 course series

Supervised Machine Learning: Regression and Classification

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Advanced Learning Algorithms

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Unsupervised Learning, Recommenders, Reinforcement Learning

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Felipe M.

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Larry W.

Chaitanya A.

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