Category Archives: Machine Learning
Machine Learning | Google Developers
Stay organized with collections Save and categorize content based on your preferences. Foundational courses
The foundational courses cover machine learning fundamentals and core concepts.
We recommend taking them in the order below.
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A course to help you map real-world problems to machine learning solutions.
The advanced courses teach tools and techniques for solving a variety of machine learning problems.
The courses are structured independently. Take them based on interest or problem domain.
Clustering is a key unsupervised machine learning strategy to associate related items.
Our guides offer simple step-by-step walkthroughs for solving common machine learning problems using best practices.
Become a better machine learning engineer by following these machine learning best practices used at Google.
This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
This comprehensive guide provides a walkthrough to solving text classification problems using machine learning.
This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
The glossary defines general machine learning terms.
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Machine Learning | Google Developers
What is Machine Learning as a Service? Benefits And Top MLaaS Platforms – MarkTechPost
- What is Machine Learning as a Service? Benefits And Top MLaaS Platforms MarkTechPost
- Data Science and Machine-Learning Platforms Market Research, Recent Trends and Growth Forecast 2028 The C-Drone Review The C-Drone Review
- Global Machine Learning-as-a-Service (MLaaS) Market Size and Overview Analysis 2022 by Value, Market Share, Recent Updates, New Technologies and Forecast to 2025 Digital Journal
- Machine Learning As A Service Market Growth, Future Prospects And Competitive Analysis 2022 openPR
- Machine Learning Artificial intelligence Market Focus on End User, Application, Solution, Component, and Range: AIBrain, Amazon, Anki, CloudMinds, Deepmind, Google Kashmir Telegraph Kashmir Telegraph
- View Full Coverage on Google News
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What is Machine Learning as a Service? Benefits And Top MLaaS Platforms - MarkTechPost
Outlook on the Machine Learning in Life Sciences Global Market to 2027 – Featuring Alteryx, Anaconda, Canon Medical Systems and Imagen Technologies…
DUBLIN, Oct. 12, 2022 /PRNewswire/ --The "Global Markets for Machine Learning in the Life Sciences" report has been added to ResearchAndMarkets.com's offering.
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This report highlights the current and future market potential for machine learning in life sciences and provides a detailed analysis of the competitive environment, regulatory scenario, drivers, restraints, opportunities and trends in the market. The report also covers market projections from 2022 through 2027 and profiles key market players.
The publisher analyzes each technology in detail, determines major players and current market status, and presents forecasts of growth over the next five years. Scientific challenges and advances, including the latest trends, are highlighted. Government regulations, major collaborations, recent patents and factors affecting the industry from a global perspective are examined.
Key machine learning in life sciences technologies and products are analyzed to determine present and future market status, and growth is forecast from 2022 to 2027. An in-depth discussion of strategic alliances, industry structures, competitive dynamics, patents and market driving forces is also provided.
Artificial intelligence (AI) is a term used to identify a scientific field that covers the creation of machines (e.g., robots) as well as computer hardware and software aimed at reproducing wholly or in part the intelligent behavior of human beings. AI is considered a branch of cognitive computing, a term that refers to systems able to learn, reason and interact with humans. Cognitive computing is a combination of computer science and cognitive science.
ML algorithms are designed to perform tasks such data browsing, extracting information that is relevant to the scope of the task, discovering rules that govern the data, making decisions and predictions, and accomplishing specific instructions. As an example, ML is used in image recognition to identify the content of an image after the machine has been instructed to learn the differences among many different categories of images.
There are several types of ML algorithms, the most common of which are nearest neighbor, naive Bayes, decision trees, a priori algorithms, linear regression, case-based reasoning, hidden Markov models, support vector machines (SVMs), clustering, and artificial neural networks. Artificial neural networks (ANN) have achieved great popularity in recent years for high-level computing.
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They are modeled to act similarly to the human brain. The most basic type of ANN is the feedforward network, which is formed by an input layer, a hidden layer and an output layer, with data moving in one direction from the input layer to the output layer, while being transformed in the hidden layer.
Report Includes
32 data tables and 28 additional tables
A comprehensive overview and up-to-date analysis of the global markets for machine learning in life sciences industry
Analyses of the global market trends, with historic market revenue data for 2020 and 2021, estimates for 2022, and projections of compound annual growth rates (CAGRs) through 2027
Highlights of the current and future market potential for ML in life sciences application, and areas of focus to forecast this market into various segments and sub-segments
Estimation of the actual market size for machine learning in life sciences in USD million values, and corresponding market share analysis based on solutions offering, mode of deployment, application, and geographic region
Updated information on key market drivers and opportunities, industry shifts and regulations, and other demographic factors that will influence this market demand in the coming years (2022-2027)
Discussion of the viable technology drivers through a holistic review of various platform technologies for new and existing applications of machine learning in the life sciences areas
Identification of the major stakeholders and analysis of the competitive landscape based on recent developments and segmental revenues
Emphasis on the major growth strategies adopted by leading players of the global machine learning in life sciences market, their product launches, key acquisitions, and competitive benchmarking
Profile descriptions of the leading market players, including Alteryx Inc., Canon Medical Systems Corp., Hewlett Packard Enterprise (HPE), KNIME AG, Microsoft Corp., and Phillips Healthcare
Key Topics Covered:
Chapter 1 Introduction
Chapter 2 Summary and Highlights
Chapter 3 Market Overview 3.1 Introduction 3.1.1 Understanding Artificial Intelligence in Healthcare 3.1.2 Artificial Intelligence in Healthcare Evolution and Transition
Chapter 4 Impact of the Covid-19 Pandemic 4.1 Introduction 4.1.1 Impact of Covid-19 on the Market
Chapter 5 Market Dynamics 5.1 Market Drivers 5.1.1 Investment in Ai Health Sector 5.1.2 Rising Chronic Diseases 5.1.3 Advanced, Precise Results 5.1.4 Increasing Research and Development Budget 5.2 Market Restraints and Challenges 5.2.1 Reluctance Among Medical Practitioners to Adopt Ai-Based Technologies 5.2.2 Privacy and Security of User Data 5.2.3 Hackers and Machine Learning 5.2.4 Ambiguous Regulatory Guidelines for Medical Software 5.3 Market Opportunities 5.3.1 Untapped Potential in Emerging Markets 5.4 Value Chain Analysis
Chapter 6 Market Breakdown by Offering 6.1 Software 6.1.1 Market Size and Forecast 6.2 Services 6.2.1 Market Size and Forecast
Chapter 7 Market Breakdown by Deployment Mode 7.1 Cloud 7.1.1 Market Size and Forecast 7.2 On-Premises 7.2.1 Market Size and Forecast
Chapter 8 Market Breakdown by Application 8.1 Diagnosis 8.1.1 Market Size and Forecast 8.2 Therapy 8.2.1 Market Size and Forecast 8.3 Healthcare Management 8.3.1 Market Size and Forecast
Chapter 9 Market Breakdown by Region 9.1 Global Market 9.2 North America 9.2.1 U.S. 9.2.1 Canada 9.3 Europe 9.3.1 Germany 9.3.2 U.K. 9.3.3 France 9.3.4 Italy 9.3.5 Spain 9.3.6 Rest of Europe 9.4 Asia-Pacific 9.4.1 China 9.4.2 Japan 9.4.3 India 9.4.4 Rest of Asia-Pacific 9.5 Rest of the World
Chapter 10 Regulations and Finance 10.1 Regulatory Framework 10.1.1 American Diabetes Association's Standards of Medical Care in Diabetes 10.1.2 Ata Guidelines for Artificial Intelligence 10.1.3 Indian Ai Guidelines, Strategy, and Standards
Chapter 11 Competitive Landscape 11.1 Overview 11.1.1 Development 11.1.2 Cloud 11.1.3 Users 11.1.4 Parent Market: Global Artificial Intelligence Market
Chapter 12 Company Profiles
For more information about this report visit https://www.researchandmarkets.com/r/qc8qjo
Media Contact:
Research and MarketsLaura Wood, Senior Managerpress@researchandmarkets.com
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Outlook on the Machine Learning in Life Sciences Global Market to 2027 - Featuring Alteryx, Anaconda, Canon Medical Systems and Imagen Technologies...
GBT is Researching the Development of a Machine Learning Driven, RF Cybersecurity System and Protocol – GlobeNewswire
Aiming to provide adaptable, synthesized, new era security for Radio Frequency networks through the use of AI technology
SAN DIEGO, Oct. 13, 2022 (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK:GTCH) ("GBT or the Company) is researching the development of a machine learning driven radio frequency (RF) cybersecurity system and protocol. Typical wireless security depends on a software and hardware identification for each wireless device. This fact creates a major cybersecurity vulnerability which can lead to a wireless devices attack or cloning. GBT is researching the development of a machine learning based system and protocol that will learn to recognize transmitters and receivers based on their unique RF fingerprint. GBT is analyzing a combination of hardware and software to be focused on learning transmitters/receivers RF features, identifying and categorizing their nature as safe or potential malicious attackers. The research is focused on developing a system to identify a potential intruder and then initiating an immediate RF fingerprint change to increase the networks security measures and also providing an incident response. In response to the out of network suspicious device, the communication system is expected to modify in real time, all other devices fingerprint to transmit in different waveforms, frequencies and other characteristics. The system under development is expected to automatically change all the wireless safe devices natural pattern to block and isolate the intruder. The ultimate goal of the system is to allow an entire networks security level to be enhanced in real-time to avoid data theft, damage and malicious attacks. This planned system will be learning to synthesize RF waveforms on-the-fly as a response to cyber threat. The system and protocol are planned to include AI technology to create an intelligent wireless communication method, maximizing the security of wireless networks by enabling RF spectrum analysis and recognition of RF devices fingerprint and signature. The wireless network under development will constantly monitor and study with the goal of identifying any abnormalities. In case of a possible cyber threat the system is expected to dynamically reconfigure its operating modes to isolate the intrusion and continue its normal, secured operation. GBT aims to equip the system with cognitive, adaptive capabilities in order to perform an automatic reconfiguration, enabling an intelligent, fast response and efficient cybersecurity technology for wireless networks. The system is planned to be autonomous and self-learning to increase RF networks cyber threats awareness, detection, identification and elimination.
It is GBTs goal to provide a new level of cybersecurity for wireless networks. Based on our experience in the RF domain we decided to develop an intelligent security system and protocol with the goal of being able to pick out and distinguish between RF signals, identifying the network members fingerprint and identifying the signals that do not belong. As our worlds RF spectrum is becoming highly occupied by numerous radio signals it becomes a great challenge to detect and identify intruders and attackers. Particularly with the rapid, constant expansion of theinternet of things (IoT)technology, there now exists a vast amount of RF data in a wide variety of fields. We identified the need for a higher level of cybersecurity in the RF domain to properly secure data, avoid damages and preventing malicious attacks. We are working on an intelligent RF cybersecurity system and protocol that collects important, relevant information about the networks devices, learning the networks patterns and provides an immediate response in case of suspicious fingerprints identification. The system will have RF awareness to the wireless network patterns, signatures, and characteristics. It will have cognitive capabilities to learn the features of each transmitter and receiver with the goal of providing immediate identification of out-of-networks intruding devices. It will have reasoning algorithms to reach rapid conclusions and to execute RF characteristics change within the networks members fingerprints and features. We are attempting to develop a system that will learn to understand the difference between safe and harmful signals detected in large bandwidths with the goal of building an efficient, intelligent network security awareness. We strongly believe that particularly in our times a robust RF cybersecurity technology must come of age to protect our normal daily lives, privacy and ultimately national security, stated Danny Rittman, the Companys CTO.
There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched and fully developed, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.
About Us
GBT Technologies, Inc. (OTC PINK: GTCH) (GBT) (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBTs mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBTs goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBTs vision is to produce this system as a low cost, secure, private-mesh-network between all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.
Forward-Looking Statements
Certain statements contained in this press release may constitute "forward-looking statements". Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements because of various important factors as disclosed in our filings with the Securities and Exchange Commission located at their website (http://www.sec.gov). In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, governmental and public policy changes, the Companys ability to raise capital on acceptable terms, if at all, the Companys successful development of its products and the integration into its existing products and the commercial acceptance of the Companys products. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release.
Contact:
Dr. Danny Rittman, CTO press@gopherprotocol.com
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GBT is Researching the Development of a Machine Learning Driven, RF Cybersecurity System and Protocol - GlobeNewswire
Forensic Discovery Taps Reveal-Brainspace to Bolster its Analytics, AI and Machine Learning Capabilities – Business Wire
DENVER & CHICAGO--(BUSINESS WIRE)--Forensic Discovery, a leader in digital forensic and eDiscovery services for the legal industry and corporations, announced that it is expanding its technology offering with Reveal, the global provider of the leading AI-powered eDiscovery and investigations platform. Reveal uses adaptive AI, behavioural analysis, and pre-trained AI model libraries to help uncover connections and patterns buried in large volumes of unstructured data.
Forensic Discovery is excited to offer next generation Artificial Intelligence to its hosted review and data analytics services through use of Reveal, said Trent Walton, founder of Forensic Discovery. Our clients, which range from the Am Law 100 to the Fortune 500, will greatly benefit from having the power to investigate, review and produce their data in new ways, thereby reducing litigation costs.
Forensic Discovery will leverage the platform globally to unlock intelligence that will help clients mitigate risks across a range of areas including litigation, investigations, compliance, ethics, fraud, human resources, privacy and security.
As we continue to expand the depth and breadth of Reveals marketplace offerings, we are excited to partner with Forensic Discovery, a demonstrated leader in digital forensics and eDiscovery, said Wendell Jisa, Reveals CEO. By taking full advantage of Reveals powerful platform, Forensic Discovery now has access to the industrys leading SaaS-based, AI-powered technology stack, helping them and their clients solve their most complex problems with greater intelligence.
For more information about Reveal-Brainspace and its AI platform for legal, enterprise and government organizations, visit http://www.revealdata.com.
About Forensic Discovery
Forensic Discovery is a litigation case management firm with expertise in Digital Forensics, eDiscovery, and Expert Testimony. The company has developed a proprietary workflow that allows its clients to forensically collect, filter, review, and produce electronic evidence using a hosted review platform. With offices in Colorado, California and Texas, Forensic Discovery is a leader in digital forensic and eDiscovery services for the legal industry and corporations. Learn more about the companys offerings by visiting http://www.forensicdiscovery.expert.
About Reveal
Reveal-Brainspace is a global provider of the leading AI-powered eDiscovery platform. Fuelled by powerful AI technology and backed by the most experienced team of data scientists in the industry, Reveals cloud-based software offers a full suite of eDiscovery solutions all on one seamless platform. Users of Reveal include law firms, Fortune 500 corporations, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premises, an intuitive user design and multilingual user interfaces, Reveal is modernizing the practice of law, saving users time and money and offering them a competitive advantage. For more information, visit http://www.revealdata.com.
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Forensic Discovery Taps Reveal-Brainspace to Bolster its Analytics, AI and Machine Learning Capabilities - Business Wire
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A Day in the Life of a Machine Learning Engineer – KDnuggets
It is good to get a better insight into what other peoples day-to-day looks like. Many students are more focused on the skills, courses, and knowledge level they need to ensure they are as good as they can get.
But sometimes, all you need is to hear it from the horse's mouth. For those of you who have never heard of that idiom, it means If you hear something straight from the horse's mouth, you hear it from the person who has direct personal knowledge of it.
Lets learn from Ibrahim Mukherjee
Ibrahim Mukherjee is an LSE Graduate in BSc Management (Hons) and a data scientist. After graduating from the LSE in 2008, Ibrahim joined the oil and gas industry as a financial analyst working in Trinidad, Singapore, UK (Aberdeen, Reading, London), Norway, Malaysia, Tunisia, and Romania.
All this while a latent interest in behavioral economics and neuroscience reading the works of Daniel Kahneman (Thinking Fast and Slow) and Dan Ariely (Predictably irrational) has led to a second career in Machine Learning and Artificial Intelligence.
Ibrahim is interested in how the brain abstracts meaning from events and how human cognition differentiates from general machine learning and pattern recognition. Apart from work, Ibrahim likes reading about the philosophy of science, religious psychology, cognitive neuroscience, human responses to stress, Bayesian methods, and writing software.
As an ML engineer, I spend a large amount of time working on 3 main tasks:
Although these may sound relatively easy, as the organization you work for gets large, so does the complexity of the data that is gathered and the results that are generated.
Lets look at each of these in turn:
Cesar Hidalgo in his book, Why Information Grows, makes a very interesting observation if you look at a city from above when an airline is about to land, it looks remarkably similar to a circuit board inside a computer (a CPU) zoomed in. A city is a computational unit, and so is any business. It can be abstracted as an algorithm there is an input, some computation where we process that input, and there is an output.
When it comes to a business, the computation is the product or service the business produces. For a barber, the raw material or input may be scissors, the rental space for the barbershop, mirrors, chairs, barricades, etc. the product is a haircut. Money in this case is the store of the value of that output. The higher the quality and/or quantity of output, the higher the value of the output is usually. There are exceptions to this things like negative externalities of the combustion engine (which may be increasingly taxed by the government), and charities that produce effective goodwill. However, this stands as a general result.
The job of a data scientist, rather than an expert data scientist is to understand the business proposition. What is the input, what is the output?
Then the data scientist would work categorically and systematically to understand the problems in the business. What can improve the offering of the company, improve the price received by the company, or improve the procurement of raw materials, or any aspect of the logistics that starts from the input and ends in the output of the company?
Before I delve into this, a word of caution for the unwary data scientist best explained in a famous exercise in caution from WW2. When the allied forces were looking to reinforce the planes for bombing raids they looked at the frequency of bullet holes in the planes that returned. Most data scientists or operations research executives working at the time were of thought that they needed to reinforce the areas of the plane which had more bullet holes.
One Hungarian-born mathematician, Abraham Wald, thought otherwise. He looked at reinforcing the parts of the plane which had no bullet holes. The reason being, that the aircraft that got hit in those areas, never really came back. They were downed.
Data, therefore, is only part of the story. Without a good cogent understanding of the mechanics of the business, data doesnt do much. It can lead to erroneous decisions in large businesses where solutions can be small in scale in terms of the quantum of improvement or efficiency they provide. In those cases, having a solid understanding of the business is critically important.
Data gathering takes the form of speaking to lots of business stakeholders and getting to understand the data in the business. Data can hide very well in silos within the business and its the data scientist's job to get to a single source of truth, scour through the different data points provided to understand the data and choose the most relevant and appropriate parts for analysis. Not all data is required and part of the skill is to be able to discern what is important and what is not. To separate the signal from the noise. Adding data incrementally to an existing piece of analysis is always possible so is removing data sets. However, the key is to find a smaller number of variables that are important to solve the business need.
Businesses in the end are money-making propositions in a capitalist framework. If the analysis does not provide a way to either save money or make money its worthless. That is then not allowed at all. This is important and key to the whole proposition of data science. It should provide a key action point or direction to the management and/or stakeholders to create a monetary value add either directly in terms of saving costs or making more profit or in soft terms such as marketing or CSR.
The data scientist must also be a storyteller. As Steve Jobs said the most powerful person in the world is the storyteller. To be able to communicate the value generated to the business is of huge importance. Unless stakeholders see the value it's almost a moot point that the analysis creates value because they wont be able to or willing to put it into action.
Storytelling the value proposition is therefore as important as creating the value. A data scientist must therefore be very good at communicating these insights.
I would like to thank Ibrahim Mukherjee for taking the time to explain to us a day in his life as a Machine Learning Engineer. Having an understanding of people's approaches to their careers and how they may differ from yours or others is important to improve and better your career
I hope this helps! Thank you again, Ibrahim Mukherjee!
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
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A Day in the Life of a Machine Learning Engineer - KDnuggets