Category Archives: Machine Learning
Harnessing machine learning to help patients with ALS – The Irish Times
What inspired your interest in using machine learning in healthcare?
I studied computer science as an undergraduate in Athens, where I grew up, and I went on to do a masters degree in biostatistics in Glasgow. I liked that biostatistics applies to real-world problems, and my research there used machine learning to look at data from patients who had heart failure.
What prompted you to move to Ireland?
My partner and I moved to Dublin, and I got a PhD position at University College Dublin and FutureNeuro with Dr Catherine Mooney, to work more on how machine learning can analyse healthcare records.
The idea is that machine learning might be able to find less linear links between patient data and their needs, and this could help to support clinicians when they are planning care for the patient.
Tell us about the project you have been working on.
My project has been looking at patients with ALS, or motor neurone disease. Over the years, Prof Orla Hardiman and her team at Trinity College Dublin have worked with groups across Europe, and have gathered data about ALS patients with their consent.
With funding from the Health Research Board and other agencies I was able to interrogate these anonymised data, and additional information that the team was able to provide from consenting caregivers and patients, to explore what factors could be likely to affect their quality of life.
What did you find, using this machine learning approach?
There were some aspects for the patients like the timing of when the disease symptoms started and whether they have issues with breathing when lying down that could reduce their quality of life. Also for primary caregivers, how they view their role and purpose seemed to be linked to their quality of life.
How might the technology be used to help people with ALS?
The models that we made can be used as part of a clinical decision support system, which could automatically flag up to a nurse or doctor a pattern of patient or caregiver characteristics that suggests the patient or caregiver might be at risk of greater psychological stress or a lower quality of life. This would help them to build a personalised plan to support the patient and caregiver.?
What has kept you going through the research?
The human side of it. I was able to visit an MND clinic and observe some of the sessions with the consent of those attending, which gave me an important context these data arent just numbers I was working with on the computer, we are talking about real-world conditions and interactions.
Also we did a user study on a prototype clinical decision support system with clinicians, to see whether and how clinicians would use such a system, and it was encouraging to see our research being translated into a real-world context.
You recently wrote up your thesis, how did you find that?
It has been quite rewarding to see everything fitting together. I was also able to move back to Athens and I will defend my thesis online, which is easier for all the examiners than travelling.
And finally, how do you like to take a break?
I like to do creative things and work with my hands, to get a break from the computer. During the lockdown in Ireland I made and decorated cakes and I also did embroidery. I find its a good balance to sitting looking at a computer screen.
Futureneurocentre.ie
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Harnessing machine learning to help patients with ALS - The Irish Times
Robots and machine learning researchers combine forces to speed up the drug development process – TechRepublic
IBM Research and Arctoris announce a research collaboration to test a closed-loop platform.
Ulysses is the world's first fully automated drug discovery platform developed and operated by Arctoris based in Oxford, Boston and Singapore.
Image: Arctoris
IBM Research and Arctoris are bringing the power of artificial intelligence and robotic automation to the process of developing new drugs. The two companies aim to make smarter choices early on in the process, iterate faster and improve the odds of finding an effective treatment.
IBM Research contributed two platforms to the project. RXN for Chemistry uses natural language processing to automate synthetic chemistry and artificial intelligence to make predictions about which compound has the highest chance of success. That information is passed on to RoboRXN, an automated platform for molecule synthesis.
Arctoris, a drug discovery company, brought Ulysses to the project. The company's automated platform uses robots and digital data capture to conduct lab experiments in cell and molecular biology and biochemistry and biophysics. Experiments conducted with Ulysses generate 100 times more data points per assay compared to industry-standard manual methods, according to Arctoris.
IBM Research will design and synthesize new chemical matter that Arctoris will test and analyze. The resulting data will inform the next iteration of the experiment.
SEE: Drug discovery company works with ethnobotanists and data scientists
Thomas A. Fleming, Arctoris co-founder and COO, described this project as "a world-first closed-loop drug discovery project" that combines AI and robotics-powered drug discovery.
"This collaboration will showcase how the combination of our unique technology platforms will lead to accelerated research based on better data enabling better decisions," he said in a press release.
A research paper about closed-loop drug discovery describes the process as a centralized workflow controlled by machine learning. The system generates a hypothesis, synthesizes a lead drug candidate, tests it and then stores the data. This comprehensive process could "reduce bottlenecks and standards discrepancies and eliminate human biases in hypothesis generation," according to the paper.
Automating lab work results in better data which in turn means less rework and a savings of time and money, Poppy Roworth, head of laboratory at Arctoris, explained in a blog post. She described the benefits of automation this way: "I no longer have to manually pipette each well at a time of a 96 or 384 well plate, which is highly beneficial for my sanity when there is a stack of more than 5 or 10 to get through." By automating the protocol, scientists can use time previously spent in the lab on "planning the next experiment, designing new projects with clients, reading literature and keeping up to day with other projects."
Matteo Manica, a research scientist at IBM Research Europe, Zurich, is coordinating the project and said in a press release that this work is a unique opportunity to quantify the impact of AI and automation technologies in accelerating scientific discovery.
"In our collaboration, we demonstrate a pipeline to perform iterative design cycles where generative models suggest candidates that are synthesized with RoboRXN and screened with Ulysses," he said. "The data produced by Ulysses will then be used to establish a feedback loop to retrain the generative AI and improve the proposed leads in a completely data-driven fashion."
More than 3,000 researchers in 16 locations on five continents work for IBM Research. Arctoris is a biotech company headquartered in Oxford with offices in Boston and Singapore. The collaboration is ongoing.
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Robots and machine learning researchers combine forces to speed up the drug development process - TechRepublic
How Machine Learning and AI Are Transforming The Finance Industry – FinanceFeeds
Thanks to the wealth of data that are increasingly available to banks and the general public, sophisticated algorithms are enabling improved processes in many areas of finance.
Image Source: Canva Pro
A subfield of artificial intelligence (AI), machine learning (ML) enables systems to learn and improve independently without the need for explicit programming or human involvement. But ML only works when it has access to enormous volumes of data, allowing machines to be trained rather than meticulously programmed through line-by-line coding.
To do this, ML utilizes data on outcomes to figure out how to improve, make predictions, and describe information, which has led to major breakthroughs in almost every industry across the globe. Machine learning technology frees up a considerable amount of resources that would otherwise be spent on manual, repetitive tasks while increasing productivity, reducing errors, automating processes, and identifying trends and patterns.
Technologies such as the internet of things (IoT) and cloud computing are all growing implementations of ML. As a result, technology is changing the way financial businesses operate, as things that were once thought unimaginable have now been brought into the realms of possibility.
Unsurprisingly, one of the primary use cases for this new tech is in the financial sector, which greatly benefits from the ability to crunch huge data sets to secure important insights into market trends and forecasting fluctuations in financial assets.
With that said, the financial industry is finding a wide variety of use cases for AI and machine learning, from predicting cash flow events to detecting fraud and even improving the customer experience. On that note, lets take a look at a few of the most widely implemented applications.
Machine learning and artificial intelligence (AI) solutions are transforming risk management in the financial sector. With this technology, banks and financial institutions can significantly reduce their risk levels by analyzing a massive volume of data sources to identify potential problem areas and make better, more informed decisions.
Banks, for example, employ machine learning to evaluate vast amounts of personal data to improve the accuracy and effectiveness of credit scoring, analyzing data sets such as prior lending operations, debts, marital status, financial behaviour of applicants, and more to help them determine whether or not to issue loans and open lines of credit.
Artificial intelligence (AI) solutions can enhance customer experiences in the finance industry via chatbots, search engines, mobile banking, and financial health analytics. All of this helps provide more value to the customer, improve application processes, answer queries quickly, and reduce waiting times when trying to fix a problem.
AI solutions can also provide automated portfolio management and personalized product recommendations with little to no human supervision.
Through the use of sophisticated stock intelligence tools, machine learning-enabled technologies are able to provide advanced market insights that surface advanced data signals. These tools are far more efficient (and quicker) than traditional investment models, leading them to dramatically disrupt the investment banking industry.
Interestingly, as this technology becomes more widely available, it is no longer exclusive to hedge fund managers and larger financial institutions. Now, everyday traders are incorporating ML-based investment strategies in order to better predict the market and spot opportunities that would have been previously impossible to unearth at scale.
In the financial industry, robotic process automation (RPA) is an extremely useful tool that banks and other financial institutions use to replace human labour by automating repetitive activities with intelligent processes, leading to increased business productivity. This is one of the most widely used applications of AI and ML in the fintech sector and has been assisting businesses in gaining a competitive advantage over their competitors for quite some time. It is feasible to improve nearly any business activity by implementing this technology, resulting in improved customer experience, cost savings, and the capacity to scale up services.
In addition, according to McKinseys research, we are about to enter the second phase of AI-enabled automation. Its predicted that machines and software bots will carry out 10% to 25% of tasks across various bank processes, increasing total capacity and allowing employees to focus on higher-value projects and initiatives.
As AI and ML technologies continue to improve, its almost certain that we will begin to see them play an increasingly important role in different aspects of the financial industries, such as managing portfolios and predicting market movements, fine-tuning the customer experience, and preventing fraud and reducing risk.
Some experts even predicted that one day we could live in a world with a fully automated financial system, but it seems at this point we still have some way to go before that can be fully achieved.
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How Machine Learning and AI Are Transforming The Finance Industry - FinanceFeeds
The 8 Best AWS Machine Learning Courses and Online Training for 2021 – Solutions Review
Solutions Review editors compiled this list of the best AWS machine learning courses and online training to use when growing your skills.
With this in mind, weve compiled this list of the best AWS machine learning courses and online training to consider if youre looking to grow your cloud artificial intelligence and automation skills for work or play. This is not an exhaustive list, but one that features the best AWS machine learning courses and training from trusted online platforms. This list of the best AWS machine learning courses below includes links to the modules and our take on each.
Platform: Coursera
Description: This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.
Platform: edX
Description: This course will teach application developers how to use Amazon SageMaker to simplify the integration of machine learning into their applications. Key topics include an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMakers built-in algorithms and, using SageMaker to publish the validated model.
Platform: Pluralsight
Description: In this course, youll learn how to analyze, visualize, preprocess and feature engineer datasets to make them ready for subsequent machine learning steps. Youll also learn how to prepare your data for the machine learning pipeline by doing preprocessing and feature engineering.
Platform: Pluralsight
Description: First, youll explore what ML is and how it relates to artificial intelligence and deep learning. Next, youll learn how to identify and frame opportunities for machine learning. Then, youll discover the end-to-end machine learning process: fetching, cleaning, and preparing data, training and evaluating models, and deploying and monitoring models.
Platform: Udacity
Description: Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.
Platform: Udemy
Description: In addition to the9-hour video course, a 30-minutequick assessment practice examis included that consists of the same topics and style as the real exam. Youll also getfour hands-on labsthat allow you to practice what youve learned, and gain valuable experience in model tuning, feature engineering, and data engineering.
Platform: Udemy
Description: This course is designed for anyone who is interested in AWS cloud-based machine learning and data science. Learners should have familiarity with Python, an AWS account, basic knowledge of Pandas, Numpy, and Matplotlib. The ideal student for this course is willing to learn and participate in the course Q&A forum when help is needed.
Platform: Udemy
Description: With over 500 slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro! This course covers the most important concepts without any fillers or irrelevant information.
Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
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The 8 Best AWS Machine Learning Courses and Online Training for 2021 - Solutions Review
New Test Leverages Machine Learning to Diagnose and Predict Sepsis – Medical Device and Diagnostics Industry
Sepsis is a huge healthcare concern. You take every single cancer and all the deaths due to every single cancer and you add them all up together. More people die from sepsis worldwide than that, said Bobby Reddy, Jr., CEO of Prenosis, in an interview with MD+DI.
And even if patients survive, they can have lifelong consequences. Sepsis occurs when you have a very abnormal, unhealthy reaction to infection, Reddy said. This unregulated immune response can lead to organ dysfunction and even death.
Sepsis is treatable with antibiotics if it is diagnosed in time, but it can explode out of control in hours or days if left untreated. If [a patient] had just gotten a simple dose of antibiotics two days earlier, it wouldn't have been life-threatening, Reddy said. That's why the WHO has called this the number one cause of preventable death worldwide.
Symptoms of sepsis can be vague and thus hard to diagnose. The current standard of care [for determining sepsis] is literally a human being, he said. Reddy explained that a physician or nurse typically uses four parameters to suspect sepsis: temperature, white blood cell count, lactate, and their overall impression of a patient.
That's how some doctors have been trained for the last 20 to 25 years, he said. Unfortunately, that just doesn't work. It's one of the reasons why there remains such a high mortality rate with sepsis.
Prenosis has developed Immunix, an assisted intelligence system that uses holistic input data from 23 parameters and a machine learning algorithm that provides an ImmunoScore, which gives a rating of a patients chances of sepsis, 30-day mortality, elongated hospital stay, and 30-day readmission to the hospital.
One unique aspect of this product is that it forces the data to be clean at that critical snapshot of time so that you canaccurately diagnose [sepsis], Reddy said. Clean data, Reddy said, means that the system checks to see if the all theneeded data is available and to see if it has any errors. For example, at this point in time, maybe they've done your bloodpressure and took your temperature, but they didn't do a heart rate measurement, he said. The system requires anymissing parameters to be filled in with the order of an additional test or additional measurement. This type of assistedintelligence can create better, cleaner data, resulting in better and more precise diagnostics, said Reddy.
The second unique aspect, he said, is that typically not all of these 23 parameters are ordered at the same time. For these patients in particular, the three biomarkers that Immunix looks at help profile the patient's underlying biological state accurately. The biomarkers are Interleukin-6, procalcitonin, and C-reactive protein.
Reddy stressed that this system is what he called assisted intelligence, as opposed to artificial intelligence, as it can be used as a tool to help guide the physician, rather than diagnosing alone. We really like to think of ourselves as a GPS as opposed to a self-driving car, he said. It's really about working with the doctor.
The Immunix system can address desperate hospital needs, said Reddy. Hospitals lose an average of $29,118 per septic patient in the United States. But according to Prenosis, based on a 1,300-patient multistudy, greater than $9.9 B can be realized in potential annual cost savings if ImmunoScore were implemented across the United States.
The Immunix system is expected to received FDA clearance by the second half of 2022.
To increase knowledge about the condition, the Sepsis Alliance has designated September as Sepsis Awareness Month. More information can be found at http://www.sepsis.org.
SambaNova makes a mark in the AI hardware realm – TechTarget
As a young startup, SambaNova Systems is already making a mark in the fast-growing AI hardware industry.
The vendor, based in Palo Alto, Calif., started in 2017 with a mission of transforming how enterprises and research labs with high compute power needs deploy AI, and providing high-performance and high-accuracy hardware-software systems that are still easy to use, said Kunle Olukotun, co-founder and chief technologist.
Its technology is being noticed. SambaNova has attracted more than $1.1 billion in venture financing. With a valuation of $5.1 billion, it is one of the most well-funded AI startups and it is already competing with the likes of AI chip giant Nvidia.
SambaNova's hallmark is its Dataflow architecture. Using the extensible machine learning services platform, enterprises can specify various configurations, whether grouping kernelstogether on asingle chip, or on multiple chips, in a rack or on multiple racks in the SambaNova data center.
Essentially, the vendor leases to enterprise clients the processing power of its proprietary AI chips and creates machine learning models based on domain data supplied by the customer, or customers can buy SambaNova chips and run their own AI systems on them.
While other vendors have offered either just chips or just the software, SambaNova provides the entire rack, which will make AI more accessible to a wider range of organizations, said R "Ray" Wang, founder and principal analyst at Constellation Research.
"The irony of AI automation is that it's massively manual today," Wang said. "What [SambaNova is] trying to do is take away a lot of that manual process and a lot of the human error and make it a lot more accessible to get AI."
Wang added that SambaNova offers AI chips that are among the most powerful on the market.
While it's known in some ways as an AI hardware specialist, SambaNova prides itself in taking a "software-defined approach" to building its AI technology stack.
"We didn't build some hardware thinking: 'OK, now developers go out and figure it out,'" said Marshall Choy, vice president of product at SambaNova. Instead, he said the vendor focused on the problems of scale, performance, accuracy and ease of use for machine learning data flow computing. Then they built the infrastructure engine to support those needs.
The irony of AI automation is that it's massively manual today. R 'Ray' WangFounder and principal analyst, Constellation Research
SambaNova breaks up its customers into two groups: the Fortune 50 and the "Fortune everybody else." For the first group, SambaNova's data platform enables enterprise data teams to innovate and generate new models, Choy said.
The other group is made up of enterprises that lack the time, resources or desire to become experts in machine learning and AI. For these organizations, SambaNova offers Dataflow as a service.
SambaNova says this approach helps smaller enterprises by reducing the complexities of buying and maintaining hardware infrastructure and selecting, optimizing and maintaining machine learning models.
This creates a "greater AI equity and accessibility of technology than has previously been held in the hands of only the biggest, most wealthy tech companies," Choy said.
SambaNova has already attracted some big-name customers.
Oneis the U.S. Department of Energy's Argonne National Laboratory in Illinois.
Using SambaNova's DataScale system, Argonne trained a convolutional neural network (CNN) with images beyond 50k x 50k resolution. Previously, when Argonne tried to train the CNN on GPUs, they found that the images were too large and had to be resized to 50% resolution, according to SambaNova.
"We're seeing new ways of computing," Wang said. "This approach to getting to AI is going to be one of many. I think other people are going to try different approaches, but this one seems very promising."
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SambaNova makes a mark in the AI hardware realm - TechTarget
Beef up your knowledge on AI and machine learning with this expert-led bundle – The Next Web
Technology is the future were surrounded by it and have made leaps and strides in how weve integrated it into our everyday lives. Artificial Intelligence (or AI) has constantly been evolving through the years and has made options that were only accessible to a select few available for even those who are part of the general public.
If youre looking to work or already work in the field of computer science, The Premium Machine Learning Artificial Intelligence Super Bundle may be for you. Whether youre an undergrad student or a seasoned professional, its never too early or too late to enhance your skills or learn new ones in your spare time. Its available on sale for 98% off for a limited time.
This expert-led bundle comes packed with 12 courses and over 400 lessons that cover everything from machine learning, data science, to algorithms and even the different frameworks you can use in your day-to-day. Using Pythona programming language used widely by practitioners in the programming community due to its features and independent platform each course is structured to equip you with the necessary foundational education of machine learning.
With step-by-step training, youll learn everything you need to knowfrom frameworks to theories that you can put into practice. Youll even get to try your hand at creating AI-powered apps from scratch. You dont have to worry about missing classes as the courses in this bundle are all available on-demand, 24/7. With lifetime access, you can always go back and review topics and redo courses that you may need a refresher on.
Whats more, this bundle comes with a Machine Learning and Data Science Developer Certification Program, which can be a great booster to any resume. Of course, the courses are taught by experts, too, including programmer John Bura, digital entrepreneur Juan Galvan, and web developer and coding instructor Kalob Taulien.
The Premium Machine Learning Artificial Intelligence Super Bundle normally retails for $2,388, but you can get it on sale for $36.99 for a limited time.
Prices subject to change.
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Beef up your knowledge on AI and machine learning with this expert-led bundle - The Next Web
Machine Learning, AI & Big Data Analytics in the Travel & Hospitality Industry: Applications, Scopes, and Im.. – ETCIO.com
In terms of travel and tourism, India also ranked 10th among 185 countries in the world. There are billions of transactions and booking of trips done on a daily basis by the travellers. The travel queries also grew by 53% in 2017 and are increasing rapidly. So, there is a huge amount of data that has to be handled to provide customer satisfaction, good customer experience and a lot more where the future of technologies like machine learning, AI and big data analytics play a very crucial role.
Applications of AI, ML & Big Data Analytics
Payment fraud is the most popular type of fraud done by people in which scammers use stolen credit card to book or use for their own accommodation while some scammers claim the card to be stolen and ask for a chargeback. Keeping these situations in mind travel and hospitality industry have created a customized machine learning model and implemented AI technology to detect and predict fraud.
Intelligent Travel Assistant
AI has gained a rapid pace in various sectors of the industries as convenience is all that people ask for. The intelligent programming bots are trained in such a way to perform tasks on the basis of users request. More than 55% of the consumers like communicating with bots. Travel booking is just one of the fields wherein automated machine learning algorithms are used.
Customer Support
Apart from the hospitality sector, airlines too utilize the power of artificial intelligence to process customer support since most of the consumers demand for quick responses to their inquiries. Chat-bot and AI play a key role in customer service and support. This use of customer support not only helps businesses in brand loyalty to grow but also increases the business output and performance.
Meta search fields enable online travel agencies to work in a smart and efficient way by tracking down and sending alerts about the changing and varying hotel prices and flight fare to attract customers to book more trips. The online travel bookings hit $755 billion in 2019 and according to estimations more than 700 million people would adopt online hotel bookings by 2023. This programme uses machine learning algorithms to forecast the future price on the basis of factors such as demand growth, special offers, seasonal trends and many other deals.
Recommendation Engines
Online travel booking providers suggest the customers various options based on their recent searches and bookings while it also provides alternative destinations so that one can visit as their next trip. This automated recommendation is solely based on the customers data. Engaging in these engines increases sales, keep loyal customers coming back and also upsells.Scopes of AI, ML & Big Data Analytics
The future scope of machine learning, AI and big data analysis depends on data from the past and the present to predict a better future. The offers can be presented to the individuals based on their preferences by analysing the data from various sources like weather, flight fares and much more. More than 71% of the travellers in India share their personal details for a personalized experience. So, the future of travel and hospitality is about the abilities to manage the data with a range of technologies like AI and machine learning for a transformative and memorable experience.
Impact on the Job Market
Technological Challenges
The travel and hospitality sector have many sub-sectors consisting of a lot of complex data which creates an impact on the companies as they face challenges when it comes to establishing insights from the database. This creates a rise for data professionals in organisations as having them gives an assurance that the complex data is being used effectively with the use of data lending and cross departmental collaboration within the company.
Economic Impact
As travellers nowadays want more individual attention and do not wish to be treated as one of many as they want to experience the view pertaining to their own needs this creates a rise for millions of job opportunities to provide them with the customized experiences they prefer.
Fusion of skills
This industry demands a lot of soft skills but technological skills cannot be completely ignored as it plays a key role in the travel and hospitality industry. Job opportunities in this industry demand fusion of both the skills equally as it keeps the balance of traditional ways of working with the flow of emerging technological aspects.
The new and the future emerging technologies have a huge impact in the travel and hospitality sector because of its various applications and future scopes. In the coming years, the sector will surely flourish if the right fusions are created.
Sachin Gupta is the Chancellor of Sanskriti University
How to Create an Awesome Machine Learning Portfolio That Will Get You a Job? – Analytics Insight
Follow the steps and create your machine learning portfolio that will easily get you hired.
Portfolios are a great way to exhibit the accomplishments you would list on a resume or talk about in an interview. People always believe what you can show and not what you tell. Similarly, when you are applying for machine learning jobs without a portfolio your value lessens down. During a job search, the machine learning portfolio will display your work to potential employers.
In your portfolio, you should essentially mention the various projects that show the technical adeptness of your machine learning skills. Even experienced machine learning professionals create and update their machine learning portfolio to keep up and stay relevant to their machine learning skills.
For the machine learning portfolio, you can use GitHub or a personal website or blog. A personal blog or GitHub profile is a strong indicator that you are a potent machine learning engineer. To exhibit the machine learning projects that you have worked on it is important to have an active GitHub account. Besides this, having a personal blog channel can be beneficial too. You can advertise your machine learning skills by writing blogs with project presentations and also writing about your experience working with machine learning tools.
If using GitHub or any other code repository as your portfolio, make sure it is always supported with a readme file for each project which contains the purpose and findings of the project along with graphs, visuals, videos, and reference links, if any. Also, make it easy for others to re-run the project by providing clear instructions on how to download the project and reproduce the results.
Along with the presentation and blog the most important thing that you should always remember is that when you are presenting the projects and experiences you must explain them, this will draw the interviewers attention. You should briefly explain all your projects rather than just writing down the projects. The projects in the portfolio should narrate the story of your work and experience.
The content is the most important thing for portfolios. The quality of the content matters more than the quantity. You cannot just pick up random projects and work on them and add them to your portfolio. You must keep the focus on your domain expertise and accordingly work on the machine learning projects that are relevant. You cannot be an expert in all fields so choose your field very carefully and then choose your projects and work on them to add to the portfolio. It will not matter if you have worked on a few projects to the point, it is worthy and based on your domain.
You must be having confusion about what type of projects you should pick? So, on that note always try to select innovative projects to create your portfolio. Innovation always excites people and therefore it will surely excite the interviewer making him want to know more about the project. You should not work on machine learning projects that are common like spam detection or intrusion detection. For instance, if you are a final engineering student who knows about CNN and Deep Learning, you can build an automated attendance system that the interviewer would be excited to know more about like how you did the face recognition, how much data was required, and more. In short, pick a project that has an interesting application and also requires effort to collect data.
Data preparation, data pre-processing, data visualization, and storytelling are the main categories on which you should emphasize. Make sure that the machine learning portfolio has at least one project in each of these categories showcasing your well-rounded set of machine learning skills to the prospective employer along with at least one end-to-end machine learning project implementation right from conceptual understanding to a real-world model evaluation.
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How to Create an Awesome Machine Learning Portfolio That Will Get You a Job? - Analytics Insight
Learning Python? Here are 5 cool jobs to consider in 2021 – The Next Web
Choosing which programming language to specialize in is a moment that can really define your career. Whether youre a newbie or youre picking up a second language, you need to know which ones are most in demand and what you can do with them. Thats why were doing a series on the top jobs for the most common programming languages, starting with Python.
There are so many programming languages out there, but for anyone who wants to break into the world of engineering, knowledge of Python can open a lot of doors. The beauty of Python is that its general, meaning that there are a number of career paths. At the same time, this can be a bit overwhelming.
Not to worry. Whether youre just starting your career or youre in need of a change, weve broken down five brilliant jobs you can consider if you decide to learn Python.
GIS (Geographic Information Systems) analysts work in the place where data analysis meets programming meets cartography. Their primary duties include analyzing spatial data through mapping software and designing digital maps with geographic data and various other data sets. So where does Python come in? Well, Pythons scripting prowess allows GIS users to streamline their data analysis and management by removing redundancies and automating the process.
The role of a software developer engages in identifying, designing, installing and testing a software system theyve built for a company from the ground up. It can range from creating internal programmes that can help businesses be more efficient to producing systems that can be sold. When software developers deliver a software system, they also maintain and update the programme to ensure that all security problems are fixed, and it operates with new databases. Python is a common language used in the software development process, making knowledge of the language key to landing a job as a software developer.
A QA engineer is responsible for the creation of tests to identify issues with software before a product launch. QA Engineers identify and analyze any bugs and errors found during the test phase and document them for review after. Other tasks include developing and running new tests, reporting on the results and collaborating with software developers to fix program issues. Depending on the internal organizational structure, QA engineers may progress to a managerial or executive position. Proficiency in computer programming languages like Python is a must for a QA role, along with extensive experience in software development and testing.
A Full Stack Developer is someone who works with the Back End of an application as well as the Front End. Full Stack Developers have to have some skills in a wide variety of coding niches, from databases to graphic design and UI/UX management in order to do their job well. Theyre something of a Jack of all trades, ready to help wherever needed in the process. The Full Stack Engineer job description usually includes using a range of different technologies and languages to develop applications. Full Stack Developers approach software holistically since they cater to both user experience and functionality.
A machine learning engineer is the person in IT who focuses on researching, building and designing self-running artificial intelligence systems to automate predictive models. Machine learning engineers design and create the AI algorithms capable of learning and making predictions that define machine learning. Or, if youre already a machine learning specialist, consider how you can transition your skills to deep learning.
These are just a few things you can do with Python skills but there are so many options out there and new applications being created every day. Check out our job board to see the hottest python jobs open now or set up a job alert to get fresh new jobs as they go on the market.
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Learning Python? Here are 5 cool jobs to consider in 2021 - The Next Web