The content table of Machine Learning
This course is a coursera version teached by Andrew NG, AP of Stanford University, which corresponds to the full-time campus version CS229 at Stanford university, that is increasingly difficult version.
01_introduction
02_linear-regression-with-one-variable
03_linear-algebra-review
04_linear-regression-with-multiple-variables
05_octave-matlab-tutorial
06_logistic-regression
07_regularization
08_neural-networks-representation
09_neural-networks-learning
10_advice-for-applying-machine-learning
11_machine-learning-system-design
12_support-vector-machines
13_unsupervised-learning
14_dimensionality-reduction
15_anomaly-detection
16_recommender-systems
17_large-scale-machine-learning
18_application-example-photo-ocr
my class assignments
Conclusion of Andrew NG
Welcome to the final video of this Machine Learning class. We’ve been through a lot of different videos together. In this video I would like to just quickly summarize the main topics of this course and then say a few words at the end and that will wrap up the class. So what have we done?
summary
In this class we spent a lot of time talking about supervised learning algorithms like linear regression, logistic regression, neural networks, SVMs. for problems where you have labelled data and labelled examples like x(i), y(i) And we also spent quite a lot of time talking about unsupervised learning like K-means clustering, Principal Components Analysis for dimensionality reduction and Anomaly Detection algorithms for when you have only unlabelled data x(i) Although Anomaly Detection can also use some labelled data to evaluate the algorithm. We also spent some time talking about special applications or special topics like Recommender Systems and large scale machine learning systems including parallelized and rapid-use systems as well as some special applications like sliding windows object classification for computer vision. And finally we also spent a lot of time talking about different aspects of, sort of, advice on building a machine learning system. And this involved both trying to understand what is it that makes a machine learning algorithm work or not work. So we talked about things like bias and variance, and how regularization can help with some variance problems. And we also spent a little bit of time talking about this question of how to decide what to work on next. So, how to prioritize how you spend your time when you’re developing a machine learning system. So we talked about evaluation of learning algorithms, evaluation metrics like precision recall, F1 score as well as practical aspects of evaluation like the training, cross-validation and test sets. And we also spent a lot of time talking about debugging learning algorithms and making sure the learning algorithm is working. So we talked about diagnostics like learning curves and also talked about things like error analysis and ceiling analysis. And so all of these were different tools for helping you to decide what to do next and how to spend your valuable time when you’re developing a machine learning system. And in addition to having the tools of machine learning at your disposal so knowing the tools of machine learning like supervised learning and unsupervised learning and so on, I hope that you now not only have the tools, but that you know how to apply these tools really well to build powerful machine learning systems. So, that’s it. Those were the topics of this class and if you worked all the way through this course you should now consider yourself an expert in machine learning. As you know, machine learning is a technology that’s having huge impact on science, technology and industry. And you’re now well qualified to use these tools of machine learning to great effect.
THX
I hope that many of you in this class will find ways to use machine learning to build cool systems and cool applications and cool products. And I hope that you find ways to use machine learning not only to make your life better but maybe someday to use it to make many other people’s life better as well. I also wanted to let you know that this class has been great fun for me to teach. So, thank you for that. And before wrapping up, there’s just one last thing I wanted to say. Which is that: It was maybe not so long ago, that I was a student myself. And even today, you know, I still try to take different courses when I have time to try to learn new things. And so I know how time-consuming it is to learn this stuff. I know that you’re probably a busy person with many, many other things going on in your life. And so the fact that you still found the time or took the time to watch these videos and, you know, many of these videos just went on for hours, right? And the fact many of you took the time to go through the review questions and that many of you took the time to work through the programming exercises. And these were long and complicate programming exercises. I wanted to say thank you for that. And I know that many of you have worked hard on this class and that many of you have put a lot of time into this class, that many of you have put a lot of yourselves into this class. So I hope that you also got a lot of out this class. And I wanted to say: Thank you very much for having been a student in this class.
My Grades
Here are the details of my grades
