Category Archives: Machine Learning

Machine learning has become an important part of our everyday lives and is used all around us. Data is key to our digital age, and machine learning helps us make sense of data and use it in ways that are valuable. Similarly, automation makes business more convenient and efficient. Machine learning makes automation happen in ways that are consumable for business leaders and IT specialists.

Machine learning is vital as data and information get more important to our way of life. Processing is expensive, and machine learning helps cut down on costs for data processing. It becomes faster and easier to analyze large, intricate data sets and get better results. Machine learning can additionally help avoid errors that can be made by humans. Machine learning allows technology to do the analyzing and learning, making our life more convenient and simple as humans. As technology continues to evolve, machine learning is used daily, making everything go more smoothly and efficiently. If youre interested in IT, machine learning and AI are important topics that are likely to be part of your future. The more you understand machine learning, the more likely you are to be able to implement it as part of your future career.

If you're interested in a future in machine learning, the best place to start is with an online degree from WGU. An online degree allows you to continue working or fulfilling your responsibilities while you attend school, and for those hoping to go into IT this is extremely valuable. You can earn while you learn, moving up the IT ladder at your own organization or enhancing your resume while you attend school to get a degree. WGU also offers opportunities for students to earn valuable certifications along the way, boosting your resume even more, before you even graduate. Machine learning is an in-demand field and it's valuable to enhance your credentials and understanding so you can be prepared to be involved in it.

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Machine Learning: Definition, Explanation, and Examples

Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it in a summarized way as:

With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance.

A machine has the ability to learn if it can improve its performance by gaining more data.

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem. The below block diagram explains the working of Machine Learning algorithm:

The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.

We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly.

Following are some key points which show the importance of Machine Learning:

At a broad level, machine learning can be classified into three types:

Supervised learning is a type of machine learning method in which we provide sample labeled data to the machine learning system in order to train it, and on that basis, it predicts the output.

The system creates a model using labeled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not.

The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering.

Supervised learning can be grouped further in two categories of algorithms:

Unsupervised learning is a learning method in which a machine learns without any supervision.

The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

In unsupervised learning, we don't have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms:

Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance.

The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning.

Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of our daily life. Machine learning is making our day to day life easy from self-driving cars to Amazon virtual assistant "Alexa". However, the idea behind machine learning is so old and has a long history. Below some milestones are given which have occurred in the history of machine learning:

Now machine learning has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc.

Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.

Before learning machine learning, you must have the basic knowledge of followings so that you can easily understand the concepts of machine learning:

Our Machine learning tutorial is designed to help beginner and professionals.

We assure you that you will not find any difficulty while learning our Machine learning tutorial. But if there is any mistake in this tutorial, kindly post the problem or error in the contact form so that we can improve it.

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Machine Learning Tutorial | Machine Learning with Python ...

For all that neural networks can accomplish, we still dont really understand how they operate. Sure, we can program them to learn, but making sense of a machines decision-making process remains much like a fancy puzzle with a dizzying, complex pattern where plenty of integral pieces have yet to be fitted.

If a model was trying to classify an image of said puzzle, for example, it could encounter well-known, but annoying adversarial attacks, or even more run-of-the-mill data or processing issues. But a new, more subtle type of failure recently identified by MIT scientists is another cause for concern: overinterpretation, where algorithms make confident predictions based on details that dont make sense to humans, like random patterns or image borders.

This could be particularly worrisome for high-stakes environments, like split-second decisions for self-driving cars, and medical diagnostics for diseases that need more immediate attention. Autonomous vehicles in particular rely heavily on systems that can accurately understand surroundings and then make quick, safe decisions. The network used specific backgrounds, edges, or particular patterns of the sky to classify traffic lights and street signs irrespective of what else was in the image.

The team found that neural networks trained on popular datasets like CIFAR-10 and ImageNet suffered from overinterpretation. Models trained on CIFAR-10, for example, made confident predictions even when 95 percent of input images were missing, and the remainder is senseless to humans.

Overinterpretation is a dataset problem that's caused by these nonsensical signals in datasets. Not only are these high-confidence images unrecognizable, but they contain less than 10 percent of the original image in unimportant areas, such as borders. We found that these images were meaningless to humans, yet models can still classify them with high confidence, says Brandon Carter, MIT Computer Science and Artificial Intelligence Laboratory PhD student and lead author on a paper about the research.

Deep-image classifiers are widely used. In addition to medical diagnosis and boosting autonomous vehicle technology, there are use cases in security, gaming, and even an app that tells you if something is or isnt a hot dog, because sometimes we need reassurance. The tech in discussion works by processing individual pixels from tons of pre-labeled images for the network to learn.

Image classification is hard, because machine-learning models have the ability to latch onto these nonsensical subtle signals. Then, when image classifiers are trained on datasets such as ImageNet, they can make seemingly reliable predictions based on those signals.

Although these nonsensical signals can lead to model fragility in the real world, the signals are actually valid in the datasets, meaning overinterpretation cant be diagnosed using typical evaluation methods based on that accuracy.

To find the rationale for the model's prediction on a particular input, the methods in the present study start with the full image and repeatedly ask, what can I remove from this image? Essentially, it keeps covering up the image, until youre left with the smallest piece that still makes a confident decision.

To that end, it could also be possible to use these methods as a type of validation criteria. For example, if you have an autonomously driving car that uses a trained machine-learning method for recognizing stop signs, you could test that method by identifying the smallest input subset that constitutes a stop sign. If that consists of a tree branch, a particular time of day, or something that's not a stop sign, you could be concerned that the car might come to a stop at a place it's not supposed to.

While it may seem that the model is the likely culprit here, the datasets are more likely to blame. There's the question of how we can modify the datasets in a way that would enable models to be trained to more closely mimic how a human would think about classifying images and therefore, hopefully, generalize better in these real-world scenarios, like autonomous driving and medical diagnosis, so that the models don't have this nonsensical behavior, says Carter.

This may mean creating datasets in more controlled environments. Currently, its just pictures that are extracted from public domains that are then classified. But if you want to do object identification, for example, it might be necessary to train models with objects with an uninformative background.

This work was supported by Schmidt Futures and the National Institutes of Health. Carter wrote the paper alongside Siddhartha Jain and Jonas Mueller, scientists at Amazon, and MIT Professor David Gifford. They are presenting the work at the 2021 Conference on Neural Information Processing Systems.

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Nonsense can make sense to machine-learning models - MIT News

Supporters raise signs as Democratic presidential hopeful Bernie Sanders campaign rally in downtown ... [+] Grand Rapids, Michigan, on March 8, 2020. - Democratic presidential hopefuls Joe Biden and Bernie Sanders secured crucial endorsements Sunday from prominent black supporters just days ahead of the first round of voting to pit them in a head-to-head contest. (Photo by JEFF KOWALSKY / AFP) (Photo by JEFF KOWALSKY/AFP via Getty Images)

Technology is a democratic right. Thats not a legal statement, a core truism or even any kind of de facto public awareness proclamation. Its just something that we all tend to agree upon. The birth of cloud computing and the rise of open source have fuelled this line of thought i.e. cloud puts access and power in anyones hands and open source champions meritocracy over hierarchy, an action which in itself insists upon access, opportunity and engagement.

Key among the sectors of the IT landscape now being driven towards a more democratic level of access are Artificial Intelligence (AI) and the Machine Learning (ML) methods that go towards building the smartness inside AI models and their algorithmic strength.

Amazon Web Services (AWS) is clearly a major player in cloud and therefore has the breadth to bring its datacenters ML muscle forwards in different ways, in different formats and at different levels of complexity, abstraction and usability.

While some IT democratization focuses on putting complex developer and data science tools in the hands of laypeople, other democratization drives to put ML tools in the hands of developers not all of whom will be natural ML specialists and AI engineers in the first instance.

The recently announced SageMaker Studio Lab is a free service for software application developers to learn machine learning methods. It teaches them core techniques and offers them the chance to perform hands-on experimentation with an Integrated Development Environment (in this case, a JupyterLab IDE) to start creating model training functions that will work on real world processors (both CPU chips and higher end Graphic Processing Units, or GPUs) as well as the gigabytes of storage these processes also require.

AWS has twinned its product development with the creation of its own AWS AI & ML Scholarship Program. This is a US$10 million investment per year learning and mentorship initiative created in collaboration with Intel and Udacity.

Machine Learning will be one of the most transformational technologies of this generation. If we are going to unlock the full potential of this technology to tackle some of the worlds most challenging problems, we need the best minds entering the field from all backgrounds and walks of life. We want to inspire and excite a diverse future workforce through this new scholarship program and break down the cost barriers that prevent many from getting started, said Swami Sivasubramanian, VP of Amazon Machine Learning at AWS.

Founder and CEO of Girls in Tech Adriana Gascoigne agrees with Sivasubramanians diversity message wholeheartedly. Her organization is a global nonprofit dedicated to eliminating the gender gap in tech and she welcomes what she calls intentional programs like these that are designed to break down barriers.

Progress in bringing more women and underrepresented communities into the field of Machine Learning will only be achieved if everyone works together to close the diversity gap. Girls in Tech is glad to see multi-faceted programs like the AWS AI & ML Scholarship to help close the gap in Machine Learning education and open career potential among these groups, said Gascoigne.

The program uses AWS DeepRacer (an integrated learning system for users of all levels to learn and explore reinforcement learning and to experiment and build autonomous driving applications) and the new AWS DeepRacer Student League to teach students foundational machine learning concepts by giving them hands-on experience training machine learning models for autonomous race cars, while providing educational content centered on machine learning fundamentals.

The World Economic Forum estimates that technological advances and automation will create 97 million new technology jobs by 2025, including in the field of AI & ML. While the job opportunities in technology are growing, diversity is lagging behind in science and technology careers.

The University of Pennsylvania Engineering is regarded by many in technology as the birthplace of the modern computer. This honor and epithet is due to the fact that ENIAC, the worlds first electronic, large-scale, general-purpose digital computer, was developed there in 1946. Professor of Computer and Information Science (CIS) at the university Dan Roth is enthusiastic on the subject of AI & ML democratization.

One of the hardest parts about programming with Machine Learning is configuring the environment to build. Students usually have to choose the compute instances, security polices and provide a credit card, said Roth. My students needed Amazon SageMaker Studio Lab to abstract away all of the complexity of setup and provide a free powerful sandbox to experiment. This lets them write code immediately without needing to spend time configuring the ML environment.

In terms of how these systems and initiatives actually work, Amazon SageMaker Studio Lab offers a free version of Amazon SageMaker, which is used by researchers and data scientists worldwide to build, train, and deploy machine learning models quickly.

Amazon SageMaker Studio Lab removes the need to have an AWS account or provide billing details to get up and running with machine learning on AWS. Users simply sign up with an email address through a web browser and Amazon SageMaker Studio Lab provides access to a machine learning development environment.

This thread of industry effort must also logically embrace the use of Low-Code/No-Code (LC/NC) technologies. AWS has built this element into its platform with what it calls Amazon SageMaker Canvas. This is a No-Code service intended to expands access to Machine Learning to business analysts (a term that AWS uses to broadly define line-of-business employees supporting finance, marketing, operations and human resources teams) with a visual interface that allows them to create accurate Machine Learning predictions on their own, without having to write a single line of code.

Amazon SageMaker Canvas provides a visual, point-and-click user interface for users to generate predictions. Customers point Amazon SageMaker Canvas to their data stores (e.g. Amazon Redshift, Amazon S3, Snowflake, on-premises data stores, local files, etc.) and the Amazon SageMaker Canvas provides visual tools to help users intuitively prepare and analyze data.

Amazon SageMaker Canvas uses automated Machine Learning to build and train machine learning models without any coding. Businesspeople can review and evaluate models in the Amazon SageMaker Canvas console for accuracy and efficacy for their use case. Amazon SageMaker Canvas also lets users export their models to Amazon SageMaker Studio, so they can share them with data scientists to validate and further refine their models.

According to Marc Neumann, product owner, AI Platform at The BMW Group, the use of AI as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout its value chain, but has been working to expand upon its use.

We believe Amazon SageMaker Canvas can add a boost to our AI/ML scaling across the BMW Group. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production, said Neumann.

As we know, with all great power comes great responsibility and nowhere is this more true than in the realm of AI & ML with all the machine brain power we are about to wield upon our lives.

Enterprises can of course corral, contain and control how much ML any individual, team or department has access to - and which internal and external systems it can then further connect with and impact - via policy controls and role-based access systems that make sure data sources are not manipulated and then subsequently distributed in ways that could ultimately prove harmful to the business, or indeed to people.

There is no denying the general weight of effort being applied here as AI intelligence and ML cognizance is being democratized for a greater transept of society and after all who wouldnt vote for that?

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Machine Learning Democratized: Of The People, For The People, By The Machine - Forbes

PROVIDENCE, R.I.[Brown University] Over the past four decades of treating HIV/AIDS, two important facts have been established: HIV-positive patients need to be put on treatment as soon as theyre diagnosed and then kept on an effective treatment plan. This response can help turn HIV into a chronic but manageable disease and can essentially help people live normal, healthy lives, said Joseph Hogan a professor of public health and of biostatistics at Brown University, who has been researching HIV/AIDS for 25 years.

Hogan is one of the primary investigators on two recently awarded grants from the National Institute of Health, totaling nearly $4.6 million over five years, to support the creation and utilization of data-driven tools that will allow care programs in Kenya to meet these key treatment goals.

If the system works as designed, then we have confidence that well improve the health outcomes of people with HIV, Hogan said.

The first part of the project involves using data science to understand whats called the HIV care cascade, said Hogan, who is the co-director of the biostatistics program for Academic Model Providing Access to Healthcare (AMPATH), a consortium of 14 North American universities who collaborate with Moi University in Eldoret, Kenya, on HIV research, care and training.

Hogan will collaborate with longtime scientific partner Ann Mwangi, associate professor of biostatistics at Moi University, who received a Ph.D. in biostatistics from Brown in 2011. Using AMPATH-developed electronic health record database, a team co-led by Hogan and Mwangi will develop algorithm-based statistical machine learning tools to predict when and why patients might drop out of care and when their viral load levels indicate they are at risk of treatment failure.

These algorithms, Hogan said, will then be integrated into the electronic health record system to deliver the information at the point of care, through handheld tablets that the physicians can use when sitting in the exam room with the patient. In consultation with experts in user interface design, the team will assess and test the most effective ways to communicate the results of the algorithm to the care providers so that they can use them to make decisions about patient care, Hogan said.

The predictive modeling system the team is developing, Hogan said, will alert a physician to red flags in the patients treatment plan at the point of care. This way, interventions can be developed to help a patient get to their treatment appointments, for example, before the patient needs to miss or cancel them. Or if a patient is predicted to have high viral load, Hogan said, a clinician can refer them for additional monitoring to identify and treat the increase before it becomes a problem.

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Grants totaling $4.6 million support the use of machine learning to improve outcomes of people with HIV - Brown University

(SACRAMENTO)

UC Davis Health, in partnership with SpectraPass, is evaluating a new type of rapid COVID-19 test. The research will involve about 2,000 people in Sacramento and Las Vegas.

The idea behind the new platform is a scalable system that can quickly and accurately perform on-site tests for hundreds or potentially thousands of people.

Nam Tran is a professor of clinical pathology in the UC Davis School of Medicine and a co-developer of the novel testing platform with SpectraPass, a Las Vegas-based startup.

Tran explained that the system doesnt look for the SARS-CoV-2 virus like a PCR test does. Instead, it detects an infection by analyzing the bodys response to it. When ill, the body produces differing protein profiles in response to infection. These profiles may indicate different types of infection, which can be detected by machine learning.

The goal of this study is to have enough COVID-19 positive and negative individuals to train our machine learning algorithm to identify patients infected by SARS-CoV-2, said Tran.

A study published by Tran and his colleagues earlier this year in Nature Scientific Reports found the novel method to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests.

In addition to identifying positive cases of COVID-19, the platform also uses next-generation sequencing to confirm multiple respiratory pathogens like the flu and the common cold.

The sequencing panel at UC Davis Health can detect over 280 respiratory pathogens, including SARS-CoV-2 and related variants allowing the study to train the machine-learning algorithms to differentiate COVID-19 from other respiratory diseases.

So far, the study has not seen any participants with the new omicron variant.

Our team has tested the system with samples from patients infected with delta and other variants of the SARS-CoV-2 virus. We are fairly certain that omicron will be detected as well, but we wont know for sure until we encounter a study participant with the variant, Tran said.

The Emergency Department (ED) at the UC Davis Medical Center is conducting the testing in Sacramento. Collection for testing in Las Vegas is conducted at multiple businesses and locations.

The team expects the study will continue until the end of winter. The results from the new study will be used to seek emergency use authorization (EUA) from the Food and Drug Administration.

The novel testing system uses an analytical instrument known as a mass spectrometer. Its paired with machine learning algorithms produced by software called the Machine Intelligence Learning Optimizer or MILO. MILO was developed by Tran, Hooman Rashidi, a professor in the Department of Pathology and Laboratory Medicine, and Samer Albahra, assistant professor and medical director of pathology artificial intelligence in the Department of Pathology and Laboratory Medicine.

As with many other COVID-19 tests, a nasal swab is used to collect a sample. Proteins from the nasal sample are ionized with the mass spectrometers laser, then measured and analyzed by the MILO machine learning algorithms to generate a positive or negative result.

In addition to conducting the mass spectrometry testing, UC Davis serves as a reference site for the study, performing droplet digital PCR (ddPCR) tests, the gold standard for COVID-19 testing, to assess the accuracy of the mass spectrometry tests.

The project originated with Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company and founder of SpectraPass. Gallagher is also a UC Davis alumnus and a longtime supporter of innovation and entrepreneurship at UC Davis.

In 2020, when the COVID-19 pandemic brought the travel and hospitality industries almost to a standstill, Gallagher began conceptualizing approaches to allow people to gather again safely. He teamed with researchers at UC Davis Health to develop the new platform and launched SpectraPass.

In addition to the novel testing solution, SpectraPass is also developing digital systems to accompany the testing technology. Those include tools to authenticate and track verified test results from the system so an individual can access and use them. The goal is to facilitate accurate, large-scale rapid testing that will help keep businesses and the economy open through the current and any future pandemics.

The official start of our multi-center study across multiple locations marks an important milestone in our journey at SpectraPass. We are excited to test and generate data on a broader scale. Our goal is to move the platform from a promising new technology to a proven solution that can ultimately benefit the broader population, said Greg Ourednik, president of SpectraPass.

New rapid COVID-19 test the result of university-industry partnership

Meet MILO, a powerful machine learning AI tool from UC Davis Health

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New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests - UC Davis Health