subject
Intro

Machine Learning

 with  Andrew Ng
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#1 in Subjects > Computer Science
#1 in Subjects > Computer Science > Artificial Intelligence

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Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Syllabus

Introduction
Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.

Linear Regression with One Variable
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

Linear Algebra Review
This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.

Linear Regression with Multiple Variables
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.

Octave/Matlab Tutorial
This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

Logistic Regression
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

Regularization
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.

Neural Networks: Representation
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

Neural Networks: Learning
In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.

Advice for Applying Machine Learning
Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.

Machine Learning System Design
To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.

Support Vector Machines
Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.

Unsupervised Learning
We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.

Dimensionality Reduction
In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.

Anomaly Detection
Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.

Recommender Systems
When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.

Large Scale Machine Learning
Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.

Application Example: Photo OCR
Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.

292 Student
reviews
Cost Free Online Course
Pace Upcoming
Institution Stanford University
Provider Coursera
Language English
Certificates Paid Certificate Available
Hours 5-7 hours a week
Calendar 11 weeks long
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292 reviews

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26 out of 26 people found the following review useful
2 years ago
Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be medium.
Machine Learning is one of the first programming MOOCs Coursera put online by Coursera founder and Stanford Professor Andrew Ng. Although Machine learning has run several times since its first offering and it doesn’t seem to have been changed or updated much since then, it holds up quite well. This course assumes that Read More
Machine Learning is one of the first programming MOOCs Coursera put online by Coursera founder and Stanford Professor Andrew Ng. Although Machine learning has run several times since its first offering and it doesn’t seem to have been changed or updated much since then, it holds up quite well. This course assumes that you have basic programming skills. Assignments also require many vector and matrix operations and slides include some long formulas expressed in summation notation so it is recommended to have some familiarity with linear algebra. You don't need to know calculus or statistics to take this course, but you may gain deeper insight into some of the material if you do. The course uses the Octave programming language, a free clone of MATLAB.

The course runs 10 weeks and covers a variety of topics and algorithms in machine learning including gradient descent, linear and logistic regression, neural networks, support vector machines, clustering, anomaly detection, recommender systems and general advice for applying machine learning techniques. Lectures are split into 3 to 15 minute segments with periodic quizzes and each topic section has a corresponding quiz. Section quizzes are worth 1/3 of the total grade but you get unlimited attempts (with a 10-minute retry timer.). Andrew Ng does a good job explaining dense material and slides although the audio levels are often too low. If you don' have good speakers you might need headphones to hear him talk. The other 2/3 of the course grade is based on 8 multi-part programming assignments that typically involve filling in code for key functions to implement machine learning algorithms covered in lecture. The course gives you a lot of structure and direction for each homework, so it is generally pretty clear what you are supposed to do and how you are supposed to do it even if you don't understand 100% of the materiel covered in lecture. You need to achieve a total score of 80% to earn a certificate, so while you can retry quizzes and resubmit programming assignments you'll have to get most things to work in the end to get one.

Machine learning is a great course if you can get past quiet audio. If you've never used Octave or MATLAB before, don't let that stop you from taking this course: learning the basics necessary to do the assignments only takes a couple of hours and it will help you think of things in terms of vectorized operations.

I give this course 4.5 out of 5 stars: Great.
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35 out of 36 people found the following review useful
3 years ago
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Anonymous partially completed this course.
Background elements: I'm an engineer by trade and have been working on statiscal projects in field of transport regulation for about ten years. I have some general background in maths and theorical computer science, I'm capable of programming. I followed this course in order to developp my professional skills. I di Read More
Background elements:

I'm an engineer by trade and have been working on statiscal projects in field of transport regulation for about ten years.

I have some general background in maths and theorical computer science, I'm capable of programming.

I followed this course in order to developp my professional skills.

I did perform most review test without much trouble, I didn't do most of the programming exercise due to everyday work and as I wasn't too eager to get into another programming language at the time (Octave).

I had significant (if old) linear algebra training and had already previously learned some of the methods displayed.

I didn't find this course very intellectually challenging and you should take that as a compliment. A.Ng is able to expose what many people would make complex in a clear and simple fashion.

Many of the things I already know became much clearer after I took the course and I learned a lot of new stuff.

I wish I had had such a great teacher when I was a student. I highly recommend this course for anyone getting started with machine learining.

The only problem I see with this course if that it sets the expectation bar very high for other courses. Unfortunately many other courses on Coursera, even from renowed universities, aren't as great.
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6 out of 6 people found the following review useful
4 years ago
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Anonymous completed this course.
This is possibly the most outstanding university class you will ever take. It is definitely the best university level course I have ever taken, and I have taken quite a few, both in person and online (MOOC). If you have any interest whatsoever in how computers learn to recognize faces, text, or recommend movies you mig Read More
This is possibly the most outstanding university class you will ever take. It is definitely the best university level course I have ever taken, and I have taken quite a few, both in person and online (MOOC). If you have any interest whatsoever in how computers learn to recognize faces, text, or recommend movies you might like, this class is nearly perfect in every way.

The instructor is an amazing human being and as cofounder of Coursera cares deeply about education. Yes, you will have to do some programming, but the instructor assumes no previous knowledge and all the information you need is available online. You will probably need to plan to spend more time on this class than estimated if you are a newcomer to computing, but only due to your background, not because the instructor has not organized the material in the most efficient and convenient format possible. Unlike some other poorly-thought-out MOOC where you waste time looking for information or confused about what is expected, this class is extremely well organized and presented in a straightforward, humble manner. In fact, I would suggest that any professor wishing to teach an online MOOC class should take this class first to see how real teaching is done by a professional who really knows the material but is not trying to impress the students with his knowledge by confusing them unnecessarily.

This is not an easy class, but it is tremendously rewarding to complete. It will probably expand your mind a few IQ points.
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8 out of 10 people found the following review useful
2 years ago
Prose Simian partially completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
Prof Ng simplifies ML as much as possible - and no more. In the complex arena of ML, that still leaves things fairly complex... But thanks to this course (which I'm 90% of the way through) I feel like I'll have a sufficient intuitive grasp of ML for vaguely sensible use of the many prebuilt libraries now available in t Read More
Prof Ng simplifies ML as much as possible - and no more. In the complex arena of ML, that still leaves things fairly complex... But thanks to this course (which I'm 90% of the way through) I feel like I'll have a sufficient intuitive grasp of ML for vaguely sensible use of the many prebuilt libraries now available in the field.

This course should also provide a framework for coping with the remaining complexity entailed by deeper study, and motivation to brush up on the related mathematical tools, where necessary.

On the downside, there are some avoidable glitches in the course materials. For someone like me, new to Matlab/Octave, these significantly increase the time requirement for the coding assignments - which are clearly intended to be pretty simple if you know what you're supposed to be doing. This adds to the already high frustration level learning a new programming language/environment can entail. And presumably the course's attrition rate - a shame, because even with these flaws it's really very well done.

Deep Learning can wait Prof Ng - this deserves your attention! ML for the people!
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9 out of 9 people found the following review useful
3 years ago
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Scott Orr completed this course.
Andrew Ng is a clear and charismatic lecturer, he covers advanced techniques, and he provides a number of practical tips, but the programming exercises are a bit canned, and may not fully prepare students to write their own scripts in Octave. The exercises involve mostly copying and pasting, rather than writing entire Read More
Andrew Ng is a clear and charismatic lecturer, he covers advanced techniques, and he provides a number of practical tips, but the programming exercises are a bit canned, and may not fully prepare students to write their own scripts in Octave.

The exercises involve mostly copying and pasting, rather than writing entire scripts. There's a reason for this: the focus of the course is on algorithms, not on other parts of solving machine learning problems

My final concern is that Machine Learning seems to have gone on autopilot at this point, with little or no attention from Ng or anyone else who helped him prepare the course materials. Questions in the discussion forum are answered instead by "Community TA's", that is, volunteers who took earlier sessions of the course.

Despite these concerns, I still heartily recommend Machine Learning as a valuable starting point for anyone interested in data science.

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9 out of 10 people found the following review useful
3 years ago
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Wickwack completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
Professor Ng is extremely clear. His lectures are extraordinarily well-organized, thoughtful, and clear. The assignments are interesting, relevant, and not too difficult. After completing the course, I took MIT's open Linear Algebra course, and at that point was able to get more of the mathematical background. Prof Read More
Professor Ng is extremely clear. His lectures are extraordinarily well-organized, thoughtful, and clear. The assignments are interesting, relevant, and not too difficult.

After completing the course, I took MIT's open Linear Algebra course, and at that point was able to get more of the mathematical background. Professor Ng was very careful to present the material without much math -- impressive to say the least. However, once I got more of the mathematical background, I felt much more solid in my understanding.
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2 out of 3 people found the following review useful
a year ago
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Anonymous completed this course.
I was able to finish this 11-week MOOC in ten days because the materials are a fine balance between succinct and comprehensive and very engagingly presented. I was initially turned off by the use of MATLAB/Octave as the programming language of choice for the assignments, but I found them relatively painless and well-cr Read More
I was able to finish this 11-week MOOC in ten days because the materials are a fine balance between succinct and comprehensive and very engagingly presented. I was initially turned off by the use of MATLAB/Octave as the programming language of choice for the assignments, but I found them relatively painless and well-crafted to give the student a modular view of how these machine learning algorithms work and the possible optimizations when implementing them.

I had exposure to many of the concepts before taking the class, but had never implemented or understood the mathematics behind any of the algorithms until taking this MOOC. It serves equally well as an overview of some of the most important "bread and butter" techniques in the field as it does a jumping off point into other, more specialized machine learning lessons. In fact, that is my suggestion for anyone interested in this MOOC: take it and complete all the lessons, then immediately dive into using the techniques in another MOOC or toward a project of your choosing to maximize the benefits.

The concepts covered in this MOOC include linear regression, logistic regression, forward propagation and backpropagation in neural networks, support vector machines, recommendation systems, and collaborative filtering.
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1 out of 1 people found the following review useful
a year ago
James Solomon-rounce is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.
Having completed a number of MOOCs I was pleasantly surprised to find out how good this one is. The course is taught well with lectures that are challenging at first glance but explained well, I felt like I made good progress in understanding the subject. The course did require some understanding of calculus and alge Read More
Having completed a number of MOOCs I was pleasantly surprised to find out how good this one is. The course is taught well with lectures that are challenging at first glance but explained well, I felt like I made good progress in understanding the subject.

The course did require some understanding of calculus and algebra, but nothing too difficult. Some people who had not done either subject for some time did need to spend some time refreshing their knowledge. In addition, you will need some familiarity of programming or at least the willingness to put in the time required to bring yourself up to speed.

For more advanced learners, this should serve as a good introductory course, although it will require more in depth learning in addition to the course, to be able to fully utilise some of the ideas.

To sum up, a worthwhile course for a range of abilities. Based on our study group of 20 or so learners of all levels, people seemed to think the course was good. Everyone found the programming exercises on backtesting a challenge!
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5 out of 5 people found the following review useful
3 years ago
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John Johnson completed this course.
A lot of participants were concerned that it was a watered down version of Stanford’s CS229. And, in fact, the course was more limited in scope and more applied than the official Stanford class. However, I found this to be a strength. Because I was already familiar with most of the methods in the beginning (linear and Read More
A lot of participants were concerned that it was a watered down version of Stanford’s CS229. And, in fact, the course was more limited in scope and more applied than the official Stanford class. However, I found this to be a strength. Because I was already familiar with most of the methods in the beginning (linear and multiple regression, logistic regression), I could focus more on the machine learning perspective that the class brought to these methods. Programming exercises were done in Octave, an open source Matlab-like programming environment.
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3 out of 3 people found the following review useful
5 years ago
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Anonymous completed this course.
It is a very well-balanced version of the course. Some time ago I tried watching the original Stanford video recording of this course and it was too dry with endless math derivations. On the other hand, this interactive Coursera version strikes the right balance between the theory and application. The course is very pr Read More
It is a very well-balanced version of the course. Some time ago I tried watching the original Stanford video recording of this course and it was too dry with endless math derivations. On the other hand, this interactive Coursera version strikes the right balance between the theory and application. The course is very practical and you can build very useful systems just based on the material presented in the course. I've watched several similar courses, and this one is by far the best.
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0 out of 1 people found the following review useful
2 years ago
Alan Du completed this course, spending 4 hours a week on it and found the course difficulty to be very easy.
A fairly good overview of machine learning, with a fair amount of breadth but almost no depth. Good introduction for a non-technical audience, with only a high-school grasp of calculus and a little bit of linear algebra. The exercises were very basic, and the programming exercises were pretty canned --- you could easi Read More
A fairly good overview of machine learning, with a fair amount of breadth but almost no depth. Good introduction for a non-technical audience, with only a high-school grasp of calculus and a little bit of linear algebra.

The exercises were very basic, and the programming exercises were pretty canned --- you could easily complete them without any real understanding of the material nor any programming knowledge.

Really, this course only taught you enough to be dangerous --- enough "understanding" to go around and build models, but not enough to avoid pitfalls.
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1 out of 2 people found the following review useful
2 years ago
Ankit Dhall completed this course, spending 5 hours a week on it and found the course difficulty to be easy.
A really good course with focus on basic algorithms and techniques in the field of ML. Regression, Neural networks and SVMs are some of the techniques taught by Andrew Ng. Video lectures are good and material is well explained. The course also helps in learning Octave and its basic syntax; the notion of vectorized code Read More
A really good course with focus on basic algorithms and techniques in the field of ML. Regression, Neural networks and SVMs are some of the techniques taught by Andrew Ng. Video lectures are good and material is well explained. The course also helps in learning Octave and its basic syntax; the notion of vectorized code is introduced. Coding assignments are easy and most of the code is just ready to be filled. It is a very basic intro to ML nonetheless a well-constructed course.
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4 out of 6 people found the following review useful
3 years ago
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Nitin Gupta completed this course.
I was completely new to ML but never felt lost while taking this course (completed yesterday). The programming assignments are a bit watered down in that most of the "boilerplate" is already written but you still get great insight with whatever is left for you to implement -- in particular, learning to write vectorized code is what I found immensely useful.
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3 out of 3 people found the following review useful
4 years ago
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Anonymous completed this course.
All other Machine Learning courses require an advanced knowledge of programming, this one is not, and I really appreciate it as I have a background in statistics but not much coding experience . Great course, highly recommend to anybody who is interested in data.
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1 out of 2 people found the following review useful
a year ago
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Anonymous is taking this course right now.
The introduction videos had high-quality audio. The audio quality of the videos I have seen after I payed for the course is terrible. Breath sounds are as as loud as the speech itself. The production quality of freely available instruction exceeds what I have experienced with Coursera.
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1 out of 1 people found the following review useful
4 years ago
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Anonymous completed this course.
Good introductory course on important topic for many businesses. ML is used in filtering spam, weather prediction, customer segmentation, Netflix recommendation, fraud detection, medical treatments etc. If you're interested in the subject this course will get you started with basic algorithms and implementation.
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1 out of 3 people found the following review useful
3 years ago
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Anonymous completed this course.
Great stuf, even if such a broad range of topics could be easily split in two more courses.

Explanations are clear. I had liked a way to use other programming languages than octave (i.e. Scala or Python)
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1 out of 2 people found the following review useful
a year ago
Michał Gąsiorowski completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Great introduction to machine learning.

Programming assignments were too much "paint-by-numbers" for my taste - not enough depth.

I didnt't like language choice either - I thing more "mainstream" would be R/Python.
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3 out of 3 people found the following review useful
3 years ago
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Rick completed this course, spending 6 hours a week on it and found the course difficulty to be hard.
Best class I've ever taken.

Now I feel like I have a super power. The hard part now is trying to figure out what problems I'd like to swoop in and try to solve.

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2 out of 3 people found the following review useful
3 years ago
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Santosh Goteti completed this course, spending 5 hours a week on it and found the course difficulty to be easy.
This is a watered-down course of Machine Learning. However, if you are a beginner, this course is a great way to start learning about Machine Learning.
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10 out of 21 people found the following review useful
3 years ago
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Mark Wilbur partially completed this course.
This course is famous. It’s taught by the equally famous Coursera co-founder and ML-star, Andrew Ng. Though I found this class to be one of the worst learning experiences I’ve had with a MOOC, I really have to say I love Andrew’s ability to explain things and the way he teaches in general. There were several problems, Read More
This course is famous. It’s taught by the equally famous Coursera co-founder and ML-star, Andrew Ng. Though I found this class to be one of the worst learning experiences I’ve had with a MOOC, I really have to say I love Andrew’s ability to explain things and the way he teaches in general.

There were several problems, though. First of all, the set-up instructions for Mac were broken. Much more importantly, the class is not comparable to Andrew’s actual ML class at Stanford. Throughout the course, he keeps telling students not to worry about the math, and spoon feeding equations to us. Worse still, I was able to get a 150% (i.e. massive extra credit) on the first assignment without actually understanding what was going on. The programming assignments were mostly done for us, with just a line or two that needed to be filled in. Amazingly, those missing lines were sometimes in the class slides. If I could change just one thing about the class, it would be to greatly increase the amount of homework.
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1 out of 2 people found the following review useful
a year ago
Mal Minhas completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
I published my thoughts on the course and its contents on in a blog post which you can find here: http://malm.teqy.net/machine-learning-coursera/
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1 out of 1 people found the following review useful
3 years ago
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Martin Bede completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
A great introduction to machine learning. I wish it was more in-depth and covered more topics, but it's still my favorite MOOC so far.
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1 out of 1 people found the following review useful
3 years ago
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Class Central user completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
The course has some theory and math as necessary, but was primarily a practical skills course and focused on applications of ML.
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a year ago
Jasper Brener completed this course.
This was my first MOOC and I took it on its first offering. I found the course to be thoroughly engaging and immediately became hooked on MOOCS. This was not only because the subject matter was so interesting but also because of Professor Ng's enlightened use of the internet to teach complex material. Indeed, although Read More
This was my first MOOC and I took it on its first offering. I found the course to be thoroughly engaging and immediately became hooked on MOOCS. This was not only because the subject matter was so interesting but also because of Professor Ng's enlightened use of the internet to teach complex material. Indeed, although I had been a university professor for 40 years, this course changed my view of how to provide higher education. Letting students rewatch lecture videos as often as they wish, retake tests until they have it right, everything "open-book" and providing hands-on experience with real tools and data, makes the subject matter completely accessible and learnable by the interested student.
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2 years ago
Moshe Bergman completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
Machine learning course was the best courses I ever found. Andrew is a great teacher on Machine Learning, and he presents just the right amount of math that you need to know in order to understand the lecture. The programming exercises are also setup in a way, where all the hard work of getting the data, converting it Read More
Machine learning course was the best courses I ever found. Andrew is a great teacher on Machine Learning, and he presents just the right amount of math that you need to know in order to understand the lecture. The programming exercises are also setup in a way, where all the hard work of getting the data, converting it to the right format, getting the matrices setup etc. is all done for you. All you have to do is add the relevant material presented in the lectures and use the grader to see if you got it right.

I highly recommend this course to anyone who is interested in the subject.
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3 years ago
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Kirank Karkera completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
One cannot ask for a better introduction to Machine learning. This course has a gentle pace, no prerequisites, and gives a 10000 ft view of the many aspects of Machine learning in general. Prof Ng is a very gentle instructor, and he tries to break down the complicated math and explain it in a very understanding manner Read More
One cannot ask for a better introduction to Machine learning. This course has a gentle pace, no prerequisites, and gives a 10000 ft view of the many aspects of Machine learning in general.

Prof Ng is a very gentle instructor, and he tries to break down the complicated math and explain it in a very understanding manner.

The assignments are probably too easy as they just ask you to fill up a few lines in Octave files, but that does not take away the usefulness of the course. It shouldn’t be hard to complete this course in parallel with a couple of others
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2 years ago
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Wei En completed this course, spending 6 hours a week on it and found the course difficulty to be easy.
This course is an excellent introduction to machine learning. Professor Ng's explanations were extremely helpful and his use of visuals and demos helped me to understand the content very well. Unfortunately, this course is just an introduction and is a watered-down version. The equations are spoonfeeded to students wi Read More
This course is an excellent introduction to machine learning. Professor Ng's explanations were extremely helpful and his use of visuals and demos helped me to understand the content very well.

Unfortunately, this course is just an introduction and is a watered-down version. The equations are spoonfeeded to students without much explanation. If you wish to apply machine learning to your programs, this course is an excellent choice. However, if you wish to understand more about the math behind this, you should consider taking another course.
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a year ago
Robert Stahr completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
This is a great course. Andrew Ng really knows how to communicate his passion for the subject. Everything is clear and concise, well said and well written. I wish there were more courses like this one. The only thing I would have liked to be different is the programming language used. Andrew uses Octave, which is fine Read More
This is a great course. Andrew Ng really knows how to communicate his passion for the subject. Everything is clear and concise, well said and well written. I wish there were more courses like this one. The only thing I would have liked to be different is the programming language used. Andrew uses Octave, which is fine for a course, but much less useful than Python. Maybe a couple of videos on "Machine Learning in Python" could do the trick.

Anyway, I highly recommend this course for anybody wanting to know about Machine Learning!
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a year ago
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Vishnuvardhan Reddy Gillella completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
It is a very good course for anyone who wants to begin their journey into Machine Learning. The course is well structured and well taught by the Prof. Ng. You don't have to have any background in Matlab/Octave but a programming background is needed. Neural Network related programming assignments are a bit hard compar Read More
It is a very good course for anyone who wants to begin their journey into Machine Learning. The course is well structured and well taught by the Prof. Ng.

You don't have to have any background in Matlab/Octave but a programming background is needed.

Neural Network related programming assignments are a bit hard compared to other assignments. But, overall there isn't much programming to do except for filling code in some functions.

Overall, it is a very good course and you will learn a lot at the end of the course.
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a year ago
Hideki Saito completed this course.
This class is very well instructed and easy to follow. (The contents are fairly complicated, but since he limits prerequisite, and it's fairly "self-contained.") The course covers various regressions in machine learning, as well as some application topics that can be useful in designing effective machine learning syst Read More
This class is very well instructed and easy to follow. (The contents are fairly complicated, but since he limits prerequisite, and it's fairly "self-contained.") The course covers various regressions in machine learning, as well as some application topics that can be useful in designing effective machine learning systems. I can see that Andrew Ng is a brilliant man with real-life knowledge of how machine learning system works, and I like the way he provides some context, too.

I highly recommend this course.
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3 years ago
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Kartik kukreja completed this course.
The course focuses on the practical applications of machine learning rather than the mathematical theory behind it. The programming assignments are very thorough and cover almost all the topics taught in the class : Regression, Spam Classification, Neural Networks, SVMs, Dimensionality reduction and Recommendation syst Read More
The course focuses on the practical applications of machine learning rather than the mathematical theory behind it. The programming assignments are very thorough and cover almost all the topics taught in the class : Regression, Spam Classification, Neural Networks, SVMs, Dimensionality reduction and Recommendation systems. All coding is done in Octave.

This is a hectic course which will keep you engaged throughout. I would recommend it for everyone serious about Computer Science.
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3 years ago
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Kartik kukreja completed this course.
The course focuses on the practical applications of machine learning rather than the mathematical theory behind it. The programming assignments are very thorough and cover almost all the topics taught in the class : Regression, Spam Classification, Neural Networks, SVMs, Dimensionality reduction and Recommendation syst Read More
The course focuses on the practical applications of machine learning rather than the mathematical theory behind it. The programming assignments are very thorough and cover almost all the topics taught in the class : Regression, Spam Classification, Neural Networks, SVMs, Dimensionality reduction and Recommendation systems. All coding is done in Octave.

This is a hectic course which will keep you engaged throughout. I would recommend it for everyone serious about Computer Science.
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2 years ago
Procellaria completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
Prof. Andrew Ng is a wonderful guide to introduce the machine learning basis. The course offers an overview on linear regression, logistic regression, neural network, support vector machine, regularization, validation, and some unsupervised machine learning methods. The contents are very clear and easy, but powerful. I Read More
Prof. Andrew Ng is a wonderful guide to introduce the machine learning basis. The course offers an overview on linear regression, logistic regression, neural network, support vector machine, regularization, validation, and some unsupervised machine learning methods. The contents are very clear and easy, but powerful. It is not required a pregressive knowledge of programming, a brief introduction to MATLAB/Octave is provided at the beginning of the course.
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4 months ago
Eugenio Francisco Martinez Pacheco completed this course, spending 5 hours a week on it and found the course difficulty to be hard.
Easy to follow, hard to complete. Andrew Ng explains subjects extremely well, avoiding demostrations, showing formulas applied to real world examples, and his speech emanates experience. Quizzes are a little challenge, but exercises are really well balanced, i say, you can achieve passing grade easily but you need something more to get the 100% score. One of the best courses I've done.
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6 months ago
Athira completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
A brilliant course for anyone interested to know about machine learning!

It covers all the basics and after taking the course, you can clearly explain what is regression, classification, etc. The concepts are crystal clear. Needs a basic understanding of calculus and linear algebra. Andrew Ng does an amazing job and the discussion forums have answers for anytime you get stuck.
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4 months ago
Jordan Lui completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
Really really recommend this course for anyone who wants to start a data science career. This is one of the best online courses I've ever taken. Really recommend this because of the good visuals and explanations and the good pace and assignment setup of this course. I am also learning ML in a university course by a very good professor and this Coursera course is on par with that.
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a year ago
Eric Cole completed this course.
I loved this course. The focus was on applying a broad selection of algorithms leaving math theory for later courses. Being able to see results on real world data via the lesson applications was very helpful in my learning process. After finishing the course I also feel like I can make better informed choices about next steps in my development.
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3 months ago
Soonyau Cheong completed this course, spending 8 hours a week on it and found the course difficulty to be easy.
This is a good introductory course to machine learning for absolute beginner. I think the only downside of the course is that assignments were a bit too easy in the sense the code for most of algorithm works were already given and rendered the assignments to almost like MATLAB/OCTAVE programming exercises to port the equations into software.
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3 years ago
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Anonymous completed this course.
It was a great class. Supervised and non-supervised machine learning algorithms were explained really well as well as how to design, analyse, and tune the system. Programming assignments required writing couple of functions in the given scripts. Understanding linear algebra and some knowledge of Octave are nice to have for this class.
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a month ago
Mike Ba completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
This course is a great introduction to ML. Ng does a great job of explaining the concepts. Best presentation of these concepts I have come across. There is a heavy emphasis on Octave and unless you are well-versed in programming languages, it might be a bit daunting.
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a year ago
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Alfredo Kalaitzis completed this course, spending 5 hours a week on it and found the course difficulty to be easy.
A very accessible course for beginners to machine learning. Good cover of important topics and with a principled approach. Andrew's intuitive explanations are second to none. I extremely recommend this course to anyone interested in a data science career.
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5 months ago
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Anonymous completed this course.
I am an Electronics Engineer so obviously I had limited programming knowledge and was absolutely a beginner in field of ML. But Prof. Andrew was a terrific teacher. He was highly organized, thoughtful and made complex topics extremely easy to understand.
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4 weeks ago
Farsan Rashid completed this course, spending 6 hours a week on it and found the course difficulty to be easy.
Machine Learning is a complicated topic that involve calculus, linear algebra, statistics but this course give you an excellent introduction avoiding mathematical details which is understandable. Best introductory course for Machine Learning. PERIOD.
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4 years ago
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Anonymous completed this course.
This class was awesome. Prof. Ng has a fantastic way of getting complex ideas and concepts and bring them to a level that even a 6-year old kid can understand. For those in the fields of Data mining & analysis, this course is highly recommended.
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3 months ago
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Saber completed this course and found the course difficulty to be easy.
Great course to begin machine learning, using MATLAB archive assignment. Although it does't provide enough theory, it gives an intuition of machine learning. After this course you will be more comfortable to learning some deeper class in this area.
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9 months ago
Wayag completed this course, spending 5 hours a week on it.
I saw too many courses which just provided boring introduction and explanation , and some quizzes whithout any support . But this course of Prof. Ng is really the best MOOC which I have ever seen.

不需要多说了 , 绝对的业界良心.
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4 years ago
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Anonymous completed this course.
What amazes me the most is the ability that Andrew has to make complex theory something easy and fun to learn. This course was the very best one I've taken in Coursera and I really hope that Andrew will teach us a lot more in the near future!
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2 years ago
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Anonymous completed this course.
Great course for ML. You don't need anything about ML to take this course. Andrew Ng does a great job of explaining fundamentals and some pretty advanced concepts. Some programming experience is required to complete the assignments.
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4 years ago
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Anonymous completed this course.
I took the first session of this course in 2011. Great teaching of great subject that is the core for AI. Programming assignments are not very difficult, but having working knowledge of Octave or Matlab would be helpful.
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3 weeks ago
Pham Binh completed this course.
Great introductory course regarding machine learning. Thanks Prof. Ng for making this class accessible to everyone. It is worth spending every single minute on the course. Give it a try and you will never regret.
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9 months ago
Dina Bova completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
One of the best courses in machine learning. Andrew Ng is a gifted teacher, able to explain complicated subject in a very intuitive and clear way, including the math behind all concepts.

Highly recommended.
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4 years ago
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Anonymous completed this course.
Great ML course. It gets you started using algorithms of supervised and not supervised ML and builds an understanding how to choose the best algorithms are for your data/problem and how to look for an error.
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a year ago
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Anonymous completed this course.
This course is awesome.

Andrew Ng has a gift for explaining simply complex notions. His course is perfect for engineers who want to get to know machine learning without having to learn too much maths.
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4 years ago
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Anonymous completed this course.
Great introductory course to machine learning. Concepts are presented at the right pace and practical implementation tips are something you cannot find easily elsewhere. I strongly recommend it.
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4 years ago
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Anonymous completed this course.
It's aimed for beginners.Prof. Andrew Ng explains everything really well, even the basic matrix operations.

This course has many Machine Learning algorithms that are used by many applications.
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4 years ago
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Anonymous completed this course.
This is a great course. Professors explains advanced topics very well, so newbies like me could grasp it. It's very practical and applicable. Programming assignments are very interesting.
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4 years ago
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Anonymous completed this course.
I've learned a lot from this course: how to make sense of data, how to write code to filter spam or do recommendations, and that using Octave the solution could be just one line of code.
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9 months ago
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Anonymous dropped this course.
I attended few lectures and it was very easy to understand. But I dropped the course because the assignments should be in Octave/MATLAB. Please add Python/ Scala to do the exercises.
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4 weeks ago
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Anonymous completed this course.
Nice Course Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
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4 years ago
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Anonymous completed this course.
This was a great class. Professor Ng was highly knowledgable, but moved at a good pace for a novice. Programming in Octave was not difficult, but applied the concepts well. I would recommend.
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8 months ago
Johan Rylander is taking this course right now, spending 4 hours a week on it and found the course difficulty to be easy.
Very good overview of machine learning taught at an easy to follow pace. I'm far from fluent in calculus and algebra though I do got through a masters in cs. Very pedagogical.
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a year ago
Cristi Constantin is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.
Andrew Ng has very good teaching skills, which counts very much. I started the course out of curiosity, but now I am really fond of machine learning area, thanks to this course.
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4 years ago
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Anonymous completed this course.
Broad spectrum of topics: from linear regression to SVM. Good examples of applications. Useful programming assignments although I would prefer to use R instead of Octave.
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3 months ago
Sebastiaan Bekker completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
Great course, nice mix of math, tinkering and understanding. examples that really work. High level enough so that a lot of people can get an overview of the basics.
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3 years ago
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Full_name partially completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
Good informative course.

A lot of efforts to make the course MOOC friendly. Other teachers should take note: a good MOOC should go beyond static powerpoint with voice-over.

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6 months ago
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Stergios Stavropoulos is taking this course right now, spending 3 hours a week on it and found the course difficulty to be medium.
Great course for beginners in Machine Learning! Engaging lectures and comprehensive topic coverage from the Coursera co-founder Prof. Ng. Highly recommended!
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4 years ago
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Anonymous completed this course.
High quality course. Some lectures are optional (I browsed them anyway), but Octave was new to me as well as some algorithms such as SVM.
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4 years ago
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Anonymous completed this course.
The teacher is excellent, the materials are very clear and well prepared. This is a very recommended class if you are interested in the topic.
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4 years ago
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Anonymous completed this course.
Excellent course, the best thing compared to other courses is that it is approachable even if you don't have a very high algebra level.
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3 years ago
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Nuno Goncalves completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
I think in the overall the course is good, but needs some refinements.

In some parts needs to go deeper in the theorical background.
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a year ago
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Anonymous completed this course.
-1 star for Octave instead of R (or other "real-life" programming language). Other than that - pretty good intro to machine learning.
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3 years ago
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Troddel completed this course.
Really good class. However, there is a gap between the video lectures and the programming assignments. It is not easy.
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3 years ago
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Anonymous completed this course.
Great introduction, clear explanations. Those interested in further details can find a lot more stuff on prof. Ng's website.
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4 years ago
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Anonymous completed this course.
This was probably the best class I've ever attended. At the end of the class I was amazed on how much I've learnt.
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4 years ago
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Anonymous completed this course.
ONe of the best course I have taken so far.

Prof. Ng explain the concept very clearly.

Very good class.
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4 years ago
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Anonymous completed this course.
Perfect course for data scientists, statisticians, and programmers. Highly recommend!
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4 years ago
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Anonymous completed this course.
It is a very good beginning course for machine learning.
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3 years ago
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Anonymous completed this course.
Great course, especially its focus on practical implementation!
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Evgeniy Mikhailovich Zheltonozhskiy completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
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Nola Donato partially completed this course, spending 5 hours a week on it and found the course difficulty to be easy.
This course would have been great if I could have continued it. I only got thru the first week and didn't event get to the Octave tutorial when I was locked out. This is NOT a self-paced course - it is a self-paced advertisement for a course you can enroll in. Bait and Switch :-(
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