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14 Février 2021
Microsoft Excel is the industry leading spreadsheet program, a powerful data visualization and analysis tool. Take your analytics to the next level with Excel. Seven Features in Microsoft Excel that you need to Learn 1. AutoSave Option. There's one thing that haunts all the Excel users in the world, that's closing an Excel file without saving it. But Microsoft played it smart by introducing the 'AutoSave' option. If that is the case, then regardless of how long excel 2010 has been activated, the message appears to refer to excel 2010 because as you said you don't have excel 2016. If you want to remove Excel 2016, you can do it using the control panel. Regardless of the situation, you should be able to open Excel 2010. By selecting it in the start menu.
Microsoft has added so many AI-driven features to its Office 365 productivity suite this year that we wanted to pull together a comprehensive list — but it's not as straightforward as it might seem. Features like PowerPoint Designer, OneNote's ink to text, and Word's grammar and style suggestions got notable improvements, but they didn't first show up in 2019.
That's because Microsoft doesn't simply ship AI-powered features in Office 365 once — it refines them over time. The team is constantly trying to figure out what makes users more productive and what doesn't. Many of the features also rely on machine learning models that adapt based on usage. 'You get this amazing signal about how it's making them more productive, how often are they using it and engaging with it, how often are they keeping the results of what you suggest to them,' Microsoft 365 general manager Rob Howard told VentureBeat. 'And that creates this really awesome feedback loop where we get to focus a bunch of energy on making them much more productive.'
Howard explained that AI features in Office are supposed to support and assist the user, not take over. 'It's really not about doing somebody's job for them, it's actually about being really assistive, about really helping them as they do their work,' he said. 'The user is still ultimately in control. The AI gets smarter based on that signal and we can help people more, but in all of these cases we're just taking out the busy work in between.'
After a couple of conversations with Microsoft, we settled on highlighting six major AI features added to Office this year. Three are generally available and three are in preview.
Last week, when we talked to Cortana lead Andrew Shuman, he was really excited about Outlook's various Cortana integrations. Shuman frequently 'triages' his own email with Cortana.
'The overall theme of that release is that we really want to think harder about where Microsoft can really add value to the assistant landscape,' Shuman explained. 'And where we think we can bring value to our most valuable users. And that really is about the Microsoft 365 users who have rich calendars, rich contact information. They're using Office every day, so we have a sense of what projects are important to them what people are important to them. And that really led us to kind of think harder about how we narrow our focus and drill really deeply into being this productivity assistant.'
Play My Emails in Outlook uses to Cortana to help you stay on top of your inbox, especially when you're on the go. Microsoft wants Cortana to be your personal productivity assistant and give you back time in your day. Cortana reads out your new emails intelligently, meaning rather than reading every detail, it summarizes and surfaces information like the sender, time sent, and email contents. Play My Emails is currently available in Outlook for iOS in the U.S., with availability for Android in the works.
Scheduler taps Cortana to help you schedule your meetings. You can include Cortana in your email to participants and let it know what you need in the body of the email (duration, timing, and location of the meeting) using natural language. You can also just write 'Find a time for us' and ask Cortana to book a conference room or a call. If you have access to your participants' calendar availability, Cortana will book the meeting when everyone is available. If you're meeting with people outside of your organization, Cortana will email them a few possible meeting times and broker a time that works for everyone. Meeting participants can accept the suggestion or propose new times by using natural language. Cortana will then send out an invite to everyone for you. Scheduler is in preview today and will hit generally available in early 2020.
The first AI feature in Excel is a perfect example of Microsoft trying to bridge the gap between mobile and desktop. The second feature is all about gleaning insights from data for the user.
'You don't have to create 30 different pivot tables or pivot charts to understand all the different ways that your data works,' Howard told VentureBeat. 'You've got an assistant that's here and is ready to help do all that busy work for you and let you focus on the insights from the data.'
Insert Data from Picture feature in the Excel mobile app helps you import analog data. With this feature, you can easily grab data in a table from a physical piece of paper. You can thus convert financial spreadsheets, work schedules, task lists, timetables, and so on into a digital format in Excel. Insert Data from Picture supports 21 languages on Android and iOS.
Excel supports natural language queries, meaning you can ask a question about your data and get quick answers without writing a formula. Using AI, Excel will quickly answer data questions using formulas, charts, or pivot tables. This feature is available for Office Insiders on Windows, Mac, and Excel for the web in English. Microsoft says it will hit general availability in early 2020.
PowerPoint has defined business presentations over the past decade. But the software itself hasn't seen major gains. The AI features Microsoft has been adding over the past few years have made the tool much smarter.
'We're taking a bunch of different basic AI algorithms — many of the services that are available to developers in something like Azure Cognitive Services — [and] we're bringing them all together into a single experience,' Howard said. 'That [way] we can help people be a more confident and more effective presenter within the tools that they're already using.'
If you are deaf, hard of hearing, or speak a different language from the presenter, a PowerPoint presentation can be difficult to follow. Live Captions and Subtitles in PowerPoint help everyone in the room understand the presentation. Powered by AI, this feature provides captions and subtitles for presentations in real time. Captions and subtitles can be displayed in the same language that the presenter is speaking or as a translation. The feature supports 12 spoken languages and displays on-screen captions or subtitles in over 60 languages for Office 365 subscribers on Windows, Mac, and the web.
Presenter Coach uses AI to help business professionals, teachers, and students improve their presentation skills. When you enter rehearsal mode and speak into a microphone, the feature gives you real-time on-screen feedback on pacing, the use of filler words, and inclusive language. It even warns you if you are reading off your slides. Ip webcam client. At the end of the presentation, Presenter Coach generates a report so you can practice and learn from the feedback. The feature is available in public preview for English users in PowerPoint on the web.
Next year, Howard promised Office users can expect Microsoft to focus more on writing assistance (like Ideas in Word) and mobile productivity (like Excel's Insert Data from Picture).
Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn't touched. Make everything ok button virus. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.
But mastering machine learning is a difficult process. Last terraria update. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.
And if you want to create machine learning systems that integrate and scale, you'll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.
Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.
But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.
To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.
While I've been using Excel's mathematical tools for years, I didn't come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.
Learn Data Mining Through Excel takes you through the basics of machine learning step by step and shows how you can implement many algorithms using basic Excel functions and a few of the application's advanced tools.
While Excel will in no way replace Python machine learning, it is a great window to learn the basics of AI and solve many basic problems without writing a line of code.
Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.
One of the most intuitive is the data chart tool, which is a powerful data visualization feature. For instance, the scatter plot chart displays the values of your data on a cartesian plane. But in addition to showing the distribution of your data, Excel's chart tool can create a machine learning model that can predict the changes in the values of your data. The feature, called Trendline, creates a regression model from your data. You can set the trendline to one of several regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning model, which you can use to predict the outcome of new observations.
You can add several trendlines to the same chart. This makes it easy to quickly test and compare the performance of different machine learning models on your data.
Above: Excel's Trendline feature can create regression models from your data.
In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG, which calculate the parameters of your machine learning models based on your training data.
The author also takes you through the step-by-step creation of linear regression models using Excel's basic formulas such as SUM and SUMPRODUCT. This is a recurring theme in the book: You'll see the mathematical formula of a machine learning model, learn the basic reasoning behind it, and create it step by step by combining values and formulas in several cells and cell arrays.
While this might not be the most efficient way to do production-level data science work, it is certainly a very good way to learn the workings of machine learning algorithms.
Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, naive Bayes classification, and decision trees.
The process can get a bit convoluted at times, but if you stay on track, the logic will easily fall in place. For instance, in the k-means clustering chapter, you'll get to use a vast array of Excel formulas and features (INDEX, IF, AVERAGEIF, ADDRESS, and many others) across several worksheets to calculate cluster centers and refine them. This is not a very efficient way to do clustering, but you'll be able to track and study your clusters as they become refined in every consecutive sheet. From an educational standpoint, the experience is very different from programming books where you provide a machine learning library function your data points and it outputs the clusters and their properties.
Above: When doing k-means clustering on Excel, you can follow the refinement of your clusters on consecutive sheets.
In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.
In many of the book's chapters, you'll use the Solver tool to minimize your loss function. This is where you'll see the limits of Excel, because even a simple model with a dozen parameters can slow your computer down to a crawl, especially if your data sample is several hundred rows in size. But the Solver is an especially powerful tool when you want to fine-tune the parameters of your machine learning model.
Above: Excel's Solver tool fine-tunes the parameters of your model and minimizes loss functions.
Learn Data Mining Through Excel shows that Excel can even express advanced machine learning algorithms. There's a chapter that delves into the meticulous creation of deep learning models. First, you'll create a single layer artificial neural network with less than a dozen parameters. Then you'll expand on the concept to create a deep learning model with hidden layers. The computation is very slow and inefficient, but it works, and the components are the same: cell values, formulas, and the powerful Solver tool.
Above: Deep learning with Microsoft Excel gives you a view under the hood of how deep neural networks operate.
In the last chapter, you'll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. You'll use formulas to create a 'bag of words' model, preprocess and tokenize hotel reviews, and classify them based on the density of positive and negative keywords. In the process you'll learn quite a bit about how contemporary AI deals with language and how much different it is from how we humans process written and spoken language.
Whether you're making C-level decisions at your company, working in human resources, or managing supply chains and manufacturing facilities, a basic knowledge of machine learning will be important if you will be working with data scientists and AI people. Likewise, if you're a reporter covering AI news or a PR agency working on behalf of a company that uses machine learning, writing about the technology without knowing how it works is a bad idea (I will write a separate post about the many awful AI pitches I receive every day). In my opinion, Learn Data Mining Through Excel is a smooth and quick read that will help you gain that important knowledge.
Beyond learning the basics, Excel can be a powerful addition to your repertoire of machine learning tools. While it's not good for dealing with big data sets and complicated algorithms, it can help with the visualization and analysis of smaller batches of data. The results you obtain from a quick Excel mining can provide pertinent insights in choosing the right direction and machine learning algorithm to tackle the problem at hand.
Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics.
This story originally appeared on Bdtechtalks.com. Copyright 2020
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