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Getting Started with Azure ML
1. What You Will Learn in This Section?
Hello, and welcome to the Azure Machine Learning course. In the previous section, we covered "Machine Learning Is the Future" with some great examples and case studies. We also covered: What is machine learning? It's definitely a matter of definition as well as how machine learning works along with supervised and unsupervised learning. We also covered different data types, categories of data, and common machine learning terms such as mean, median, mode, range, etc. We briefly touched upon different types of machine learning models that are available now. In this section, we will define Microsoft AzureML and provide an overview of the Azure ML Studio. We will also create your free account on Azure ML and then walk you through the various modules and sections within the studio. Every machine learning algorithm implementation follows a set workflow, and we will cover that in the context of Azure ML. Last but definitely not least is the Azure ML Cheat Sheet. Azure Machine Learning Studio has a large library of algorithms ranging from regression, classification, clustering, and animal detection, and each is designed to address a different type of machine learning problem. The ML Algorithm Cheat Sheet provided by Microsoft helps you choose the right algorithm for a predictive analytics model. So there is a great deal of material to be covered, and I'm so excited to get you started on your ML journey by creating a free account for zero machine learning. Thank you so much for joining me in this one. I'll see you in the next class. Until then, have a great time. Bye.
2. What is Azure ML and high level architecture.
Hello and welcome. In this lecture, we are going to learn about what Microsoft Azure ML is. As you may be aware, Azure is the cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services. Azure Machine Learning is a fully managed cloud service to build, deploy, and share a predictive analytics solution. It provides tools such as Azure ML Studio to create complete machine-learning solutions in the cloud. One of the main differentiators of Azure ML is quick model creation as well as very quick deployment using Azure ML Studio without writing a single line of code for basic ML experiments. Secondly, though one can build various models, it requires some effort to deploy them as a web service so that other developers within your project or outside users can also use it. Azuraml solves that problem very efficiently and allows models to be deployed as Web services almost instantly. Any machine learning library finds its strength in the pre-built modules and algorithms. Azure ML scores heavily here as well as against some of the other such service providers, and it comes with a large library of prebuilt machine learning algorithms and modules. And if you're a data science developer who has prebuilt models, Azure ML will work for you as well. It allows the developers to extend the models with custom-built R and Python code. Let's try to understand the AzuraMel with an architectural overview. Azure Cloud is where you will be able to create and store all your projects, experiments, models, and data sets. These can be accessed from anywhere and at any time. All you need is an Internet connection and a laptop computer. With that set up, we can now access and process the data on Azure. We can also do feature engineering and build and test our models, as well as deploy them. We can also transfer the data in and out of Azura as required. Various data transfer protocols and means are supported by Azure, such as http, hive, Azure, SQL, DB, any on-premises data that you may have, and various other forms of storage on Azure, such as document, DB, or blob storage as well. All the models that we will build can also be consumed using a simple web service from anywhere in the world. This will help you deploy and test your models in the real world, as well as even sell your ML models as a service. That is why Azure ML is considered to be a game changer in cloud-based machine learning services. That brings us to the end of this short lecture on what surveillance is a Surveillance.I hope you have enjoyed this session, and I'll see you in the next class. Until then, enjoy your time. Bye.
3. Creating a Free Azure ML Account
Hello, and welcome to the Azure Machine Learning course. In this session, we are going to create your first Azura account. I'm so excited to show it to you. Well, it begins with visiting www.microsoft.com on the right side of your browser. Click on sign in button. If you have an existing account with Microsoft, you can sign in using that. Or if you are a new user, you can click on "Create One." To sign up, enter your email ID and password. And you may want to pause this video here and sign in or sign up before we proceed to create an Azure Male account. All right, so fill in all the required details and create your Microsoft account first. Great. Once you have created your Microsoft account, the next step is to go to More and then Microsoft Azure. This will take you to Azure Microsoft.com.Click on Continue for Free sign up link. This is where it requires lots of details such as some personal details, a credit card, and phone verification. Once you have done all of that and checked the agreement, you can then sign in to the Azure account. Once inside Azure, click on Product Data Analytics and then on Machine Learning. As this photo keeps changing, you may see some slight changes to the navigation since the video was uploaded. Upon clicking there, it will take you to the Azure Machine Learning Homepage. Simply click on "Get Started" or go to Studio AzureML net.This should open the Azure MLStudio, which looks like this. If you have been following me, you most certainly have Azure ML Studio open. Congratulations, and I will see you in the next class.
4. Azure ML Studio Overview and walk-through
Hello and welcome. Now that you have the Azurama account and are logged in there, I'm so excited to give you an overview of Azurama Studio. Azuraml Studio is a workbench environment. The best part is that you can use your browser to access it. Azure ML Studio gives you an interactive and visual workspace to easily build, test, and iterate on a predictive analysis model. You can drag and drop your data set and analysis modules onto an interactive canvas. You can connect them together to form an experiment, which you can then run inside Azure ML Studio. Being a cloud-based environment, it requires virtually no startup cost. That is, either in terms of time or infrastructure. It's a pretty straightforward tool with seven high-level tabs. Those are projects, experiments, Web Services, notebooks,data sets, trained models and settings. Let's look at them one by one. I will explain the projects in the lastas it includes all the other includes all the other Here you can build your own experiments using a completely iterative process. You can build, test, and iterate multiple times, as well as edit any of the previously saved experiments. It provides a complete drag and drop visual interface, which makes it very easy to visualise and edit the steps required to build a model. Simply put, Azure Machine Learning gives you the speed to either fail quickly or ultimately succeed. The next one is Web Services. Once we build, train, and test our models, we can deploy them as Web Services. You would be able to see some Web services here. Once we create them, they can be exposed as public APIs over the Internet. All right. The next one is notebooks. It represents the Jupiter notebooks that you can create the data set for. This is where the data is uploaded to Azure ML Studio. The data is visible in the form of a data set. And Microsoft has also provided a number of sample datasets within Azure ML Studio for you to experiment with. And you can upload more data sets as you need them. You can use the CRM data set, CRM Churn predictions, movingV and restore data set, and trading data set, among other things. The next one is training models. When you train a model on some existing data set and algorithm, you can save it for future use, and it will be available under this particular tab. The Settings tab allows you to view and edit the workspace, view and regenerate authorization tokens, as well as invite other users to clone or edit the experiments. All right. You also see the button with a big plus sign here that says "New" when you click on it. It allows you to upload a new data set from a local file, or it allows you to upload a preprocessed module created in Our or Python. Microsoft has also provided a huge list of sample modules that you can use in your projects. An empty project can be created. The experiment link allows you to either create a new blank experiment or load an existing sample from the gallery. We are going to use them as we would create various experiments using data sets and algorithms. That brings us to the end of this lecture on Azure ML Studio. In the next lecture, we will look at the experiment workflow for Azure Machine Learning. Until then, get yourself familiarised with the Azure MLstudio, and I'll see you in the next class. Thank you for joining me in this one, and have a great time. Bye.
5. Azure ML Experiment Workflow
Hello, and welcome to the Azure Machine Learning course. We are now only a few steps away from creating our machine learning model in Azure, but these are very, very important steps. Today we are going to talk about the workflow of the Azul machine learning experiment. You may want to pause the video and get yourself a cup of coffee before we proceed. All right, it typically follows a five-step workflow. First, one must get the data, prepare the data feature selection, choose and apply a learning algorithm, and finally train and evaluate the model. Let's go through them in slightly more detail. Remember, we are going to have separate sessions on data transformation as well as for individual algorithms. I usually prefer explaining some of the complex principles while doing the hands-on labs. In this lecture, we will simply learn about various steps. All right, let's look at the Get the Data step. This simply means making the raw data available for the experiment. This is logically the first step for any machine learning experiment, and Azure ML is no different. Azure ML provides multiple options for making the data available for the experiment. You can use the Enter Data Manually module to create a small single-column data set by typing values rather than loading the data from a source in Azure Machine Learning Studio or from a local file. In case you're wondering, why would you want to insert the data manually? Well, it can actually be helpful in certain scenarios, such as generating a very short list of values for testing or typing a list of columns to insert in a data set. All right, another method by which the data can be loaded is to import data. You can use the Import Data module to load the data into an experiment from existing cloud data services that are outside of Azure Machine Learning Studio. The module now features a vizard, which makes it much easier to help you choose a storage option and select from among existing subscriptions as well as accounts to quickly configure all the options. The visa can also load your previous configuration details so that you don't have to start from scratch again. Another method to make the data available is by using the module to unpack a ZIP data set. You can use the Unpack Zip Data Set module to get compressed data files and unzip them for use in an experiment. The module takes a data set as input in your workspace. You need to upload that data set in a compressed format and then decompress the dataset and add the data to your workspace. This is particularly useful because you can reduce the data transfer time when working with very large data sets by saving and uploading your data files in a compressed format. All right, the next step in the Azure MLExperiment workflow is preparing the data for the algorithm. Azure ML provides various modules to prepare and transform the data. You can apply various filters. You can also manipulate the data either by adding rows or columns to the data or by cleaning missing data values or even editing the metadata, such as changing the variable type from "not defined" to "categorical" or from "numerical" to "categorical" and vice versa. For any data set available for experimentation, we want to split it into a training set and a test set. Azuraml achieves it by providing a split module now that the data has been loaded and processed. Also, the next logical step in the AzuraML experiment workflow is to select the features for our experiment. Azuraml provides a variety of ways, such as filter-based selection, feature LDA, as well as permutation feature importance. Now, within the filter-based feature selection, it does provide a variety of options such as Pearson correlation, Kai Square, and so on. The next step in the experiment workflow is to choose and apply the algorithm. Azuramil provides a vast array of algorithms that we can apply. It also provides a range of parameters that can be tweaked to get better results. Last but not least, training and evaluating the model Again, there are modules for training, scoring, and evaluating the experiment and the model that you have built. All we need to do is provide the appropriate input and output connections. That brings us to the end of the lecture on the workflow of the AzuraML experiment. To summarise what we just discussed, there are five steps in the Azure ML experiment creation process. The first one is to get the data to Azure using various input modules repairing the data using a collection of data transformation modules, selecting the features for the experiment, and choosing and applying the algorithm based on criteria and from the list that has been provided. Finally, we train and evaluate the model, evaluating its accuracy as well as a few other parameters. Please remember that any machine learning experiment can be implemented by following these five steps. All right. In the next lecture, we will try to understand which algorithm to use in which condition by considering the Azure ML cheat sheet. Until then, enjoy your time, and thank you so much for joining in this one.
6. Azure ML Cheat Sheet for Model Selection
Hello and welcome to the last lecture of this section of Getting Started with Azure ML. In the previous lectures, we learned about what Microsoft Azure ML is. We created a free account for ML Studio, and now we are familiar with AzuraML Studio. One of the important lectures to get us started was the workflow of an Azure ML experiment, in which we understood the steps needed from getting the data to evaluating the model. In this lecture, we will cover very briefly the Azure ML Cheat Sheet. I strongly recommend that you download the one from the course content or by searching for Microsoft AzureML Cheat Sheet, which will straightaway take you to the link from where you can download it. You can pause this video while you download it. All right, so this is how it looks, and the first time I saw it I was completely overwhelmed. It is indeed very intimidating as well. Do not worry, though, as I have broken it down further for easy understanding. The goal of Microsoft and this cheatsheet is to cater to a specific audience, which is a beginner data scientist with undergraduate-level machine learning knowledge who is attempting to select an algorithm to begin with. That also means that it makes some generalisations and oversimplifications, but it more or less points you in the right direction. All of these recommendations are rules of thumb and should not be taken as exact. At a higher level, it boils down to what outcome or type of prediction we are trying to make. If we are predicting categories, we would use classification. However, when the outcome that we want to predict is binary, such as yes or no, pass or fail, success or failure, That is, if the outcome can only have one of the two values, we would use two classifications. If the outcome is in more than one category, then we use the multi-classification algorithm. All right. Next is if the value that we are predicting is a continuous variable, in which case we use the regression algorithm. Sometimes the goal is to identify data points that do not fit the usual path or are considered outliers, such as trying to identify a fraudulent transaction. Then we use animal detection algorithms. And when our goal is to find patterns in the data set and we do not know the dependent variable, we would use clustering algorithms. Simply adding our problem statement to these four buckets is not sufficient. There are several other algorithms, and all of them perform to varying degrees in different circumstances. Their characteristics are also very different. Hence we have to consider some additional parameters so that we can choose a correct algorithm beyond classification, regression, clustering, and anomaly detection. Let's go through them one by one. Those are accurate. Sometimes you do not need an exact or very accurate answer. It may even result in overfitting. An approximation can sometimes be sufficient, as higher accuracy could require more iterations and hence more computing resources. The next one is training time. Similarly, depending upon the algorithm we use, the training time could also be different. This is also one of the points to be considered if training time is a limitation. The number of features or independent variables could also limit the ability of an algorithm to provide faster results, making it an important criterion for the selection of an algorithm. The next one is the number of parameters. As we will see when we build the models, every algorithm requires a certain set of input parameters for it to provide the desired results. A large number of parameters could also affect the training time and accuracy. Another factor is the linearity. Many machine learning algorithms make use of linear assumptions. In the first two examples, the alinear algorithm might work best. However, in this particular example, a linear algorithm might just not provide the correct results. Hence, it is very important to visualise the data and understand the linearity of the data. before we choose an algorithm. That brings us to the end of this lecture on the AzureML cheat sheet. I suggest you keep it handy all the time whenever you are working on machine learning models. It guides you in the right direction. Thank you so much for joining me in this one, and I will see you in the next class. Until then, enjoy your time.
Data Processing
1. Data Input-Output - Upload Data
Hello and welcome. Today we will do one of the first steps of the Azure Machine Learning workflow, which is loading the data in the Azure ML Workspace. In this lecture, we will cover how to upload a data set to the Azure ML Workspace and also how to enter the data manually. So let's go to the Azure.ML Studio. Here we are in the Azure ML Studio. Let's first try to upload a new data set to the Azure ML Workspace. Before we do that, I suggest you please download all the comma-separated files of employees from the Course Resources section. You can pause the video while we do that. Alright, let's try to upload these by clicking on this small little button in the corner that says New. Click on the data set, then click on "from the local file," and a dialogue will pop up. Using the Upload a New Data Set dialog, you can choose a particular file from your local hard disk. I'm going to select this one and click "Open." As you can see, it has already identified the type of new data set. By default, Azure ML supports various types of data sets. That includes a generic CSV file with the header and the dotCSV extension, a generic CSV file with no header, and a generic TSV file, which is nothing but tab-separated data. The tab-separated data may or may not have headers as well. It also supports text files, SPM light files, ARFF formats, zip files, and our objects. You can also provide an optional description for your data set and click Okay. It is going to upload a new data set to the Azure ML Studio. Let's wait until it is done. All right, so our new dataset has been successfully uploaded. Let me click on "okay." Then let's go to the new tab and click on "Experiment" or "Blank Experiment" and check the data sets. As you can see under my data set, a new CSV file has been uploaded. great, and congratulations. If you've been following along with me, you've finished your first task of uploading a sample data set to Azure ML Workspace. If you want to see and use this particular data set, all you need to do is simply drag and drop this data set onto the canvas. It appears like a small box similar to the flowchart with a small node right here. Typically, you will find one or multiple nodes for a particular module. The nodes on this side are usually to accept an input, whereas the node on this side of the rectangle provides an output. In our case, it's a plain and simple data set, and hence there is no input node. Let's now try to visualise this data set. To visualize, you need to right-click on this small node here and then click on Visualize. As you can see, it has already identified the first row as the header row. It also provides you with information on how many rows and columns are present in this particular data set. You can also view various types of graphs for these columns. For example, if I click on "gender," it provides me the statistics as well as the visualisation of the column gender. As you can see here, there are two unique values and a few missing ones as well. Unique values are basically male and female. You can also see the same information in histogram format if need be. You can also compare gender with married status or any other column. All right, let's close the visualisation and get back to the data input and output. Let me clean this space by deleting our data set from the canvas. It will free up some space for us. Let's now try to use another module called Enter the Data Manually. Using "Enter Data Manually," you can type the data manually and use it later on in the experiments. And because we have this checkbox checked, I'm going to enter the first row as a header row, which has name, age, and gender. As the headers, hit Enter, and then on the second row, we can enter some data. Let's try to enter a couple of rows here. Okay, let me enter Jtash 39 M. Let's add a second row, Andrew, 38 M, and Sandra, 32 F. All right, and let's come back to the module and try to run it. Now, to run it, we have two options. We can right-click and select "Run Selected," or we can simply click on "Run" at the bottom of the canvas. Let's run it and see the Now, to run itIt has finished running the module, and let's see what our data set looks like by visualising on "RunIt has created a data set with the same data we entered with the Haters, as we specified. In case you wonder, why do we need to enter the data manually? Well, there could be situations where you may need to provide the headers for a particular data set or you may require some test data with only 89 values, and the Enter Data Manually module can come in very handy in such cases. This concludes the first part of the lecture on data input/output for Azure workspace. In this lecture, we have seen how to upload a data set and how to enter the data manually. In the next couple of lectures, we will cover how to convert the data formats, how to unpack a zipdata set, as well as how to import the data from an external source using the UCI Adult Sensors data. As you can see, it is pretty easy to work with Azure ML Studio. That concludes this lecture. Thank you so much for joining me in this one, and have a great time ahead. I'll see you in the next class.
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