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What is machine learning? Everything you need to know | ZDNet
Download a free sample chapter PDF. Enter your email address and your sample chapter will be sent to your inbox. >> Click Here to Download Your Sample Chapter. BONUS: Linear Algebra Python Code Recipes you also get 92 fully working Python scripts. Sample Code Recipes. Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it Estimated Reading Time: 6 mins Search the world's most comprehensive index of full-text books. My library 24/8/ · Download Free PDF / Read Online. Author(s): Gabriel Cánepa Publisher: Packt Publishing Published: November Format(s): PDF File size: MB(PDF) Number of pages: 50 Download / View Link(s): Download. Similar Books: Introduction to Machine Learning; A Course in Machine Learning; Microsoft Azure Essentials: Azure Machine Learning ; Machine Learning Using C#
What-you-need-know-about-machine-learning pdf download
Everything you need to know before engaging a data labeling service. Act strategically, build high quality datasets, what-you-need-know-about-machine-learning pdf download reclaim valuable time to focus on innovation. If you have massive amounts of data you want to use for machine learning or deep learning, you'll need tools and people to enrich it so you can train, validate, and tune your model. Are you ready to hire a data labeling service? This guide will take you through the essential elements of successfully outsourcing this vital but time consuming work.
From the technology available and the terminology used, to best practices and the questions you should ask a prospective data labeling service provider, it's here. This guide will be most helpful to you if you have data you can label for machine learning and you are dealing with one or more of the challenges below. You have a lot of unlabeled data. What-you-need-know-about-machine-learning pdf download data labels are low quality.
There are a lot of reasons your data may be labeled with low quality, but usually the root causes can be found in the people, processes, or technology used in the data labeling workflow. You want to scale your data labeling operations because your volume is growing and you need to expand your capacity.
Your data labeling process is inefficient or costly. You need to add quality assurance to your data labeling process or make improvements to the QA process already underway. This is an often-overlooked area of data labeling that can provide significant value, particularly during the iterative machine learning model testing and validation stages. In machine learning, if you have labeled data, what-you-need-know-about-machine-learning pdf download, that means your data is marked up, or annotated, to show the targetwhich is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks what-you-need-know-about-machine-learning pdf download include data tagging, annotation, classification, moderation, transcription, what-you-need-know-about-machine-learning pdf download, or processing, what-you-need-know-about-machine-learning pdf download.
Data annotation generally refers to the process of labeling data. Data annotation and data labeling are what-you-need-know-about-machine-learning pdf download used interchangeably, although they can be used differently based on the what-you-need-know-about-machine-learning pdf download or use case.
Labeled data highlights data features - or properties, characteristics, or classifications - that can be analyzed for patterns that help predict the target. For example, in computer vision for autonomous vehicles, a data labeler can use frame-by-frame video labeling tools to indicate the location of street signs, pedestrians, or other vehicles. HITL leverages both human and machine intelligence to create machine learning models.
In a human-in-the-loop configuration, people are involved in a virtuous circle of improvement where human judgement is used to train, tune, and test a particular data model. Labels are what the what-you-need-know-about-machine-learning pdf download uses to identify and call out features that are present in the data.
Accurately labeled data can provide ground truth for what-you-need-know-about-machine-learning pdf download and iterating your models. The term is borrowed from meteorology, where "ground truth" refers to information obtained on what-you-need-know-about-machine-learning pdf download ground where a weather event is actually occurring, that data is then compared to forecast models to determine their accuracy. Organizations use a combination of software, processes, and people to clean, what-you-need-know-about-machine-learning pdf download, structure, or label data.
In general, you have four options for your data labeling workforce:. Accuracy in data labeling measures how close the labeling is to ground truth, or how well the labeled features in the data are consistent with real-world conditions. Quality in data labeling is about accuracy across the overall dataset. Does the work of all of your labelers look the same? Is labeling consistently accurate across your datasets? This is relevant whether you have 29, 89, or data labelers working at the same time.
Low-quality data can actually backfire twice : first during model training and again when your model consumes the labeled data to inform future decisions. To create, validate, and maintain production for high-performing machine learning models, you have to train and validate them using trusted, reliable data. In data labeling, basic domain knowledge and contextual understanding is essential for your workforce to create high quality, structured datasets what-you-need-know-about-machine-learning pdf download machine learning.
For example, people labeling your text data should understand when certain words may be used in multiple ways, depending on the meaning of the text. For highest quality data, labelers should know key details about the industry you serve and how their work relates to the problem you are solving.
For example, the vocabulary, format, and style of text related to what-you-need-know-about-machine-learning pdf download can vary significantly from that for the legal industry, what-you-need-know-about-machine-learning pdf download. Machine learning is an iterative process. A flexible data labeling team can react to changes in data volume, task complexity, and task duration.
The more adaptive your labeling team is, the more machine learning projects you can work through. As you develop algorithms and train your models, data labelers can provide valuable insights about data features - that is, the properties, characteristics, or classifications - that will be analyzed for patterns that help predict the target, or answer what you want your model to predict.
In machine learning, your workflow changes constantly. A closed feedback loop is an excellent way to establish reliable communication and collaboration between your project team and data labelers.
The second essential for data labeling for machine learning is scale. What you want is elastic capacity to scale your workforce up or down, according to your project and business needs, without compromising data quality. As the complexity and volume of your data increase, so will your need for labeling. Video annotation is especially labor intensive: each hour of video data collected takes about human hours to annotate. A minute video contains somewhere between 18, and 36, frames, about frames per second.
Increases in data labeling volume, whether they happen over weeks or months, will become increasingly difficult to manage in-house. They also drain the time and focus of some of your most expensive human resources: data scientists and machine learning engineers.
A data labeling service can provide access to a large pool of what-you-need-know-about-machine-learning pdf download. Crowdsourcing can too, but research by data science tech developer Hivemind found anonymous workers delivered lower quality data than managed teams on identical data labeling tasks, what-you-need-know-about-machine-learning pdf download.
Your best bet is working with the same team of labelers, because as their familiarity with your business rules, context, and edge cases increases, what-you-need-know-about-machine-learning pdf download, data quality improves over time. They also can train new people as they join the team. This is especially helpful with data labeling for machine learning projects, where quality and flexibility to iterate are essential. Look for elasticity to scale what-you-need-know-about-machine-learning pdf download up or down.
You may have to label data in real time, based on the volume of incoming data generated. Perhaps your business has seasonal spikes in purchase volume over certain weeks of the year, as some companies do in advance of gift-giving holidays. We have also found that product launches can generate spikes in data labeling volume.
You will want a workforce that can adjust scale based on your needs, what-you-need-know-about-machine-learning pdf download. CloudFactory took on a huge project to assist a client with a product launch in early Completing the related data labeling tasks required 1, hours over 5 weeks.
We completed that intense burst of work and continue to label incoming data for that product. Unfettered by data labeling burdens, our client has time to innovate post-processing workflows, what-you-need-know-about-machine-learning pdf download.
Whether you buy it or build it yourself, the data enrichment tool you choose what-you-need-know-about-machine-learning pdf download significantly influence your ability to scale data labeling.
Commercially available tools give you more control over workflow, features, security, and integration than tools built in-house. They also give you the flexibility to make changes. On the worker side, strong processes lead to greater productivity, what-you-need-know-about-machine-learning pdf download.
Combining technology, workers, and coaching shortens labeling time, increases throughput, and minimizes downtime. We have found data quality is higher when we place data labelers in small teams, train them on your tasks and business rules, and show them what quality work looks like. Team leaders encourage collaboration, peer learning, support, and community building.
We've found that this small-team approach, combined with a smart tooling environment, results in high-quality data labeling. Organized, accessible communication with your data labeling team makes it easier to scale the process.
Based on our experience, we recommend a tightly closed feedback loop for communication with your labeling team so you can make impactful changes fast, such as changing your labeling workflow or iterating data features. Data labeling service providers should be able to work across time zones and optimize your communication for the time zone that affects the end user of your machine learning project. The third essential for data labeling for machine learning is pricing.
The model a data labeling service uses to calculate pricing can have implications for your overall cost and for your data quality. Typically, data labeling services charge by the task or by the hour, and the model you choose can create different incentives for labelers, what-you-need-know-about-machine-learning pdf download.
If you pay data what-you-need-know-about-machine-learning pdf download per task, it could incentivize them to rush through as many tasks as they can, what-you-need-know-about-machine-learning pdf download, resulting in poor quality data that will delay deployments and waste crucial time. By contrast, what-you-need-know-about-machine-learning pdf download workers are paid for their time, and are incentivised to get tasks right, especially tasks that are more complex and require higher-level subjectivity.
Data science tech developer Hivemind conducted a study on data labeling quality and cost. Tasks were text-based and ranged from basic to more complicated. Hivemind sent tasks to the crowdsourced workforce at two different rates of compensation, with one group receiving more, to determine how cost might affect data quality, what-you-need-know-about-machine-learning pdf download.
The managed workers only made a mistake in 0. Overall, what-you-need-know-about-machine-learning pdf download, on this task, the crowdsourced workers had an error rate of more than 10x the managed workforce. Workers received text of a company review from a review website and were to rate the sentiment of the review from one to five. Actual ratings, or ground truth, were removed. Crowdsourced workers had a problem, what-you-need-know-about-machine-learning pdf download, particularly with poor reviews.
For 4- what-you-need-know-about-machine-learning pdf download 5-star reviews, there was little difference between the workforce types. Look for a data labeling service with realistic, flexible terms and conditions. The fourth essential for data labeling for machine learning is security. A data labeling service should comply with regulatory or other requirements, based on the level of security your data requires. If data security is a factor in your machine learning process, what-you-need-know-about-machine-learning pdf download, your data labeling service must have a facility where the work can be done securely, the right training, policies, and processes in what-you-need-know-about-machine-learning pdf download - and they should have the certifications to show their process has been reviewed.
Most importantly, your data labeling service must respect data the way you and your organization do. They also should have a documented data security approach in all of these three areas:. The fifth essential for data labeling in machine learning is tooling, which you will need whether you choose to build it yourself or to buy it from a third party. Because labeling production-grade training data for machine learning requires smart software tools and skilled humans in the loop.
A data labeling service should be able to provide recommendations and best practices in choosing and working with data labeling tools.
Machine Learning: 5 Things You Need to Know! Machine Learning for Beginners.
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Download a free sample chapter PDF. Enter your email address and your sample chapter will be sent to your inbox. >> Click Here to Download Your Sample Chapter. BONUS: Linear Algebra Python Code Recipes you also get 92 fully working Python scripts. Sample Code Recipes. Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it Estimated Reading Time: 6 mins Search the world's most comprehensive index of full-text books. My library 16/12/ · What is machine learning? Everything you need to know. This guide explains what machine learning is, how it is related to artificial intelligence, how it works and why it blogger.comted Reading Time: 8 mins
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