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Artificial intelligence (AI) is one of the most in-demand and rapidly evolving fields in the world, with applications and opportunities across various industries and domains. Whether you are a beginner or an experienced professional, acquiring an AI certification can help you boost your skills, knowledge, and career prospects in this exciting and competitive field.

CareerFoundry is an online portal that provides career-change opportunities in a variety of technology sectors, including UX design, UI design, web programming, and data analytics.

Their AI for Everyone course is a beginner-friendly and project-based course that covers the fundamentals of AI, machine learning, and deep learning.

Coursera is one of the most popular and reputable online learning platforms, offering courses, specializations, and degrees from top universities and organizations around the world. Their AI for Everyone course is a non-technical and introductory course that covers the basics of AI, machine learning, and deep learning.

Google is one of the leading and most innovative companies in the field of AI, machine learning, and deep learning, offering various tools and services to support and advance the development and deployment of AI solutions. Their TensorFlow Developer Certificate is a professional certification that validates your ability to build, train, and deploy machine learning models using TensorFlow, an open-source and widely used machine learning library.

edX is another popular and reputable online learning platform, offering courses, professional certificates, and degrees from top universities and organizations around the world. Their Professional Certificate in Machine Learning and Artificial Intelligence by Microsoft is a comprehensive and intermediate-level program that covers the key concepts and techniques of machine learning and artificial intelligence.

Coursera also offers specializations, which are collections of courses that focus on a specific topic or skill. The Natural Language Processing Specialization by National Research University Higher School of Economics is a specialized and advanced-level program that covers the theory and practice of natural language processing (NLP).

Udacity is another popular and reputable online learning platform, offering nanodegrees, which are project-based and career-oriented programs that focus on a specific topic or skill. Their AI Engineer Nanodegree is a comprehensive and intermediate-level program that covers a wide range of AI topics and techniques, such as computer vision, natural language processing, reinforcement learning, generative AI, and more.

IBM is another leading and innovative company in the field of AI, machine learning, and deep learning, offering various tools and services to support and advance the development and deployment of AI solutions. Their AI Engineering Professional Certificate is a comprehensive and intermediate-level program that covers the fundamentals and applications of machine learning and deep learning, using Python, TensorFlow, Keras, PyTorch, and other tools and frameworks.

Coursera also offers specializations from deeplearning.ai, which is an online education platform founded by Andrew Ng, dedicated to teaching, and promoting deep learning, which is an area of AI that utilizes neural networks to learn from data and produce predictions or choices. Their Deep Learning Specialization is a foundational and intermediate-level program that covers the basics and applications of neural networks and deep learning.

edX also offers MicroMasters programs, which are collections of graduate-level courses that focus on a specific topic or skill. The MicroMasters Program in Artificial Intelligence by Columbia University is an advanced and rigorous program that covers the theory and practice of artificial intelligence, machine learning, and deep learning, using Python, TensorFlow, PyTorch, and other tools and frameworks.

Coursera also offers professional certificates from IBM, which are collections of courses that focus on a specific topic or skill. Their IBM Applied AI Professional Certificate is a beginner-friendly and practical program that covers the basics and applications of artificial intelligence, machine learning, and deep learning.

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Top AI Certification Courses to Enroll in 2024 - Analytics Insight

Introduction

Artificial Intelligence (AI) is a transformative technology that aims to mimic human intelligence and perform tasks that typically require human cognitive abilities. It encompasses various subfields, such as machine learning, natural language processing, computer vision, and robotics. AI systems are designed to analyze vast amounts of data, learn from patterns, make predictions, and automate complex processes. The potential applications of AI are vast, ranging from healthcare and finance to transportation and manufacturing.

The global Artificial Intelligence (AI) market is set to reach approximately USD 2,745 billion by 2032, marking a substantial increase from USD 177 billion in 2023, with a steady CAGR of 36.8%.

The AI market has been experiencing rapid growth, driven by advancements in technology, increased data availability, and the need for automation and intelligent decision-making. Organizations across industries are recognizing the value of AI in improving efficiency, enhancing customer experiences, and gaining a competitive edge. The AI market encompasses a wide range of solutions, including AI software platforms, AI-enabled hardware, and AI services.

Challenges:

Predictions:

In conclusion, AI is a transformative technology with immense potential to revolutionize various industries. The AI market is experiencing significant growth, driven by technological advancements and the increasing demand for intelligent automation and decision-making capabilities. Gathering data from reliable sources and staying informed about emerging trends can provide valuable insights into the AI market, enabling organizations to leverage AI effectively and drive innovation in their respective fields.

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Artificial Intelligence Market towards a USD 2,745 bn by 2032 - Market.us Scoop - Market News

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You may be hearing the word AI bubble a lot these days, especially regarding the stock market. Since OpenAI released its artificial-intelligence (AI) chatbot ChatGPT in Nov. 2022, it feels like every company in the world has been getting into the AI business.

Machine learning is a type of AI that allows computers to learn and reproduce how humans learn and use that to replicate their behaviors. As you might imagine, machine learning has the potential to decrease the cost and time of human tasks and eliminate redundant work.

Companies are set to save billions of dollars by integrating machine learning tools and software in their businesses. As investors, not only is it important to look at which companies are successfully using machine learning, but also the companies that are providing these tools to be used. This article will discuss three of the top machine-learning stocks to buy while the AI industry remains red-hot.

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NVIDIA (NASDAQ:NVDA) is the global leader when it comes to producing GPUs that can power machine-learning computers. The stock has been on a tear over the past year, returning north of 240% to shareholders, while surging up the list of the worlds most valuable companies. Despite such unprecedented growth, Yahoo Finance analysts still remain optimistic for with with a one-year target between an average of $852.10 to a high of $1,400.0.

When it comes to machine-learning GPUs, NVIDIA is second to none in the semiconductor industry. NVIDIA has more demand for its chips than it has supply even at elevated prices, an its customers include some of the most powerful companies in the world.

You might think that a stock that has risen by more than 240% in one year is overinflated. The fact is, that NVIDIAs revenue has grown so fast that its growth has kept pace with its stock valuation. Looking comparatively, NVIDIAs forward P/E ratio of 34.25x is still lower than the likes of Amazon and Tesla. As long as AI and machine learning are being adopted, NVIDIAs stock should continue to reap rewards for investors.

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Tesla (NASDAQ:TSLA) is a company that needs no introduction. It is the largest manufacturer of electric vehicles in the world and single-handedly revolutionized the auto industry. While its stock has lagged behind its other Magnificient 7 counterparts in 2024 due to high-interest rate environments, its consensus one-year price target still aims for a high of $345.00.

So, how does an electric vehicle company operate in the machine-learning industry? Tesla, led by CEO Elon Musk, has long been trying to master self-driving technology. Teslas FSD or full self-driving software has had some roadblocks from regulatory agencies like the NHTSA in America, but Musk remains confident that it will be available to all Tesla users in the future.

Teslas stock still trades at a premium, especially since the company has reported declining operating margins and fairly stagnant revenue growth. The forward P/E ratio of the stock shows that TSLA is trading at about 65x forward earnings, which is nearly double that of NVIDIA. As mentioned, Teslas stock could continue to struggle until interest rates begin to decline. Savvy long-term investors might be taking this period of consolidation as a time to load up on the high-growth stock.

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Palantir (NYSE:PLTR) is a data analytics and software company that has a very polarizing following on social media. At one time, Palantir was looked at as a meme stock, but the company has since proven to be profitable and has exhibited impressive growth.

While the operations of Palantir have always been shrouded in mystery, the company has made clear progress in growing its customer base over the past few years. One of the ways it has done this is by introducing its AIP or Artificial Intelligence Platform. AIP uses machine learning to help large-scale enterprises unluck insights from large sets of data. From this analysis, companies can identify inefficiencies and operate at a higher level.

We did mention Palantirs stock is trading at the high end of analyst estimates, right? Well, although it is a much smaller company, Palantirs valuation currently dwarfs that of both NVIDIA and Tesla. At its current price, Palantirs stock trades at about 25x sales and 79x future earnings. With the potential to be considered for S&P 500 inclusion later this year, and management guiding a FY2024 revenue of around $2.6 billion, Palantir is a worthy company to look into capitalizing off machine learning.

On the date of publication, Ian Hartana and Vayun Chugh did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Chandler Capital is the work of Ian Hartana and Vayun Chugh. Ian Hartana and Vayun Chugh are both self-taught investors whose work has been featured in Seeking Alpha. Their research primarily revolves around GARP stocks with a long-term investment perspective encompassing diverse sectors such as technology, energy, and healthcare.

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The Top 3 Machine Learning Stocks to Buy in March 2024 - InvestorPlace

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Machine learning and the prediction of suicide in psychiatric populations: a systematic review | Translational Psychiatry - Nature.com

In this video, Glenn T. Werneburg, MD, PhD, discusses the abstracts "Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort and "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection, which were presented at the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction 2024 Winter Meeting in Fort Lauderdale, Florida. Werneburg is a urology resident at Glickman Urological & Kidney Institute at Cleveland Clinic, Cleveland, Ohio.

We'll start with our efforts regarding overactive bladder. Overactive bladder is a common and costly condition. It includes symptoms of urgency, frequency, and urinary incontinence. Patients who don't respond to behavioral therapies or medical management generally require a third-line therapy. That third-line therapy is usually either sacral neuromodulation or onabotulinumtoxinA injection into the bladder. For these patients, many of them would do well with either of these therapies, but a subset would not respond to them. Our goal was to identify which patients would respond and which patients would not respond to these different treatments for medically refractory overactive bladder. To do so, we developed machine learning algorithms. We assembled a team, of course with urologists, but also with quantitative mathematicians, who have lots of experience making predictions regarding stock market fluctuations and things like this. And we developed these algorithms to predict the responder vs the nonresponder status of our patients. We defined how a patient responded based on whether they had a reduction in urge incontinence episodes following the treatment, and whether they perceived an improvement in their symptoms following the therapy. What we found was that we were able to identify patients who responded vs didn't respond to the treatment with a high degree of accuracy. The accuracy surpassed even that of human experts and other standard algorithms. We found this really encouraging. The algorithms even held up in an external validation set, so when we trained the algorithms on 1 data set, and then validated them on a very different group of patients, they still were able to predict who would respond vs not respond to the treatment.

Our other study was a very different application. We were looking at what antibiotics would be optimally suited for which particular patients. One of the major threats to humanity right now is antibiotic resistance. The World Health Organization tells us it's 1 of the top 10 greatest threats. One of the main drivers for antibiotic resistance is overuse and misuse of antibiotics. One of the issues in urology is that a urine culture, which is commonly used to diagnose urinary tract infection, takes 3 days to results. In that time period, the clinician provides the best treatment based on his or her clinical judgment. But we know from the literature that about 30% of the time, this isn't the optimal therapy, and this therapy needs to be changed once the final urine culture results come back. So we set out with a goal to optimally predict which antibiotic would be most suitable for which patient at the time the urine culture is ordered, so it equates to 3 days prior to the final results. The idea was that if we target the therapy very specifically, we can improve our antibiotic usage and improve the time to the resolution of symptoms for our patients. It's beneficial at the patient level and beneficial at the population level. We developed a series of algorithms. We focused on only the most clinically relevant antibiotics. We trained these algorithms on a large data set of our patients - about 6.6 million cases were used. And then we validated them on another set of patients and we tested how they would perform in terms of predicting which cultures would be sensitive and which cultures would be resistant to these different antibiotics. They held up very well. The algorithms' accuracy was really good, and it held up even in an external data set. We find this really encouraging. We have some more validation to do. We're really looking forward to being able to implement these algorithms clinically to improve patient care and also to improve our antibiotic stewardship and reduce our selection for antibiotic resistance at the population level.

This transcription was edited for clarity.

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Machine learning algorithms show applications in OAB, antibiotic resistance - Urology Times