[MUSIC] As Emily discussed, we're gonna see machine learning through the lens of a wide range of case studies in different areas that really ground the concepts behind them. So, other machine learning classes that you might take out there are really a laundry list of algorithms and methods. So things like support vector machines and kernels and logistic ration and networks and so on. And they're just like, a laundry list of methods. And the problem with that approach is that since you start from the algorithms you end up with really simplistic use cases with the applications, they're really disconnected from reality. So, we're doing things very different in this specialization, and we've done this for quite a while here. Emily and I created a course at the University of Washington on machine learning at scale for big data. We pioneered this use case approach for teaching machine learning. And in that course, we saw a lot of positive feedback from folks really understanding rounding the concepts. So we're going to start from the use cases in the first course. And by starting from use cases, you're really going to be able to grasp the key concepts and the techniques that allow you to build, measure the quality and understand whether your intelligent applications is working well or not. And in the end, you are going to build a bunch of these intelligent applications. So to build such intelligent applications, you typically have to think about what task am I going to do. I am going to solve a sentiment analysis problem and what models, what machine learning models am I going to use, and things like support vector machines or regression what methods when they use to optimize the parameters of that model? And then I ask a question like is this really providing the intelligence that I'm hoping for? How do we measure the quality of that system? So in this specialization what we're gonna do is defer the core pieces of how to describe a model and optimize it to the follow on courses. And this first course is going to be focused on helping us figure out what task we're trying to solve, what machine learning methods make sense, and how to measure them. And with that, using the algorithms as black boxes, we're going to be able to build a wide range of really intelligent cool applications together. And we'll actually code them and build them and demonstrate them in a wide range of ways. Now the following on courses, they're, it's going to be four of those plus a capstone. They really go into depth in different areas. So let me give you a few quick examples of the kind of depth we're going to see throughout this specialization. So the regression course is going to talk about various models of predicting a real value, so for example, a house price from the features of the house. And we're going to discuss linear regression techniques, we're going to discuss advanced techniques like ridge regression and lasso that allow you to select what features are most appropriate for your problem. We're going to talk about optimization techniques like gradient descent and coordinate descent to optimize the parameters of those models. As well as some key machine learning concepts like loss functions, bias-variance tradeoffs, cross-validation. Things that you need to know to really take this method and kind of improve them, develop them and build applications with them. The second course on classification, we're gonna build, for example, the sentiment analysis use case that Emily talked about, and talk about more of those classifications. From linear classifiers to more advanced things like linear regression, sorry, logistic regression, support vector machines. But then add kernels and decision trees which allow you to deal with non-linear complex features. We talked about optimization methods for dealing with these techniques at scale and for building ensembles of them something called boosting. And then the underlying concepts in machine learning that really help you grasp classifier and scaled it up and apply it to different methods. Now, in the next course, we're gonna focus on clustering and retrieval, especially in the context of documents. So we're gonna talk about basic techniques like nearest neighbors as well as more advanced clustering techniques, mixture of Gaussians, and even latent Dirichlet allocation can advance text analysis clustering technique. We're gonna talk about the algorithms that underpin these things and how to scale them up with techniques like KD-trees and sampling and expectation maximization. Now the core concepts here are really around how to scale these things up, how to measure the quality and really how to write them as distributed algorithms using techniques like map-reduce, which are implementing systems like Hadoop that you might have learned about. So in the fourth course, you're actually going to write some map-reduce code for distributed machine learning. Now in the final technical course we're gonna focus on techniques of matrix factorization and dimensionality reduction, which are widely applicable, but in particular for recommender systems, for recommending products. So these are things like collaborative filtering, matrix factorization, PCA, and the underlying techniques for optimizing them, like coordinate descent, Eigen decomposition, SVD. And then, a wide variety of whole machine learning concepts that are really useful. Especially in the recommenders' domain. Like how to pick a diverse set of recommendations and how to scale them up to large problems. Now the capstone is going to be really exciting and towards the end of this module, I'm going to go back and tell you quite a bit more about the capstone. But just to give you a little hint, you're going to build something extremely cool that you can show to all your friends, potential employers. And you'll see that it can build a really smart intelligent application around recommenders, the combined text data, image data, sentiment analysis, deep learning, it's going to be really cool. [MUSIC]