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TheMachine Learning in Retail Marketreport is one of the most comprehensive and important data about business strategies, qualitative and quantitative analysis of Global Market. It offers detailed research and analysis of key aspects of the Machine Learning in Retail market. The market analysts authoring this report have provided in-depth information on leading growth drivers, restraints, challenges, trends, and opportunities to offer a complete analysis of the Machine Learning in Retail market.

Global Machine Learning in Retail Market is presented to the readers as a holistic snapshot of the competitive landscape within the given forecast period. It presents a comparative detailed analysis of the all regional and player segments, offering readers a better knowledge of where areas in which they can place their existing resources and gauging the priority of a particular region in order to boost their standing in the global market.

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Top Key Players Present in Global Machine Learning in Retail Market Are:IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze

Different leading key players have been profiled in this research report to get a clear idea of successful strategies carried out by top-level companies. On the basis of geographical segmentation, the global Machine Learning in Retail Market has been fragmented across several regions such asNorth America, Latin America, Asia-Pacific, Africa, and Europe.This Market research report highlights those leading players who are planning to expand opportunities in the global market.

The Machine Learning in Retail Market research report presents a comprehensive assessment of the market and contains thoughtful insights, facts, historical data and statistically-supported and industry-validated market data and projections with a suitable set of assumptions and methodology. It provides analysis and information by categories such as market segments, regions, and product type and distribution channels.

The Global Machine Learning in Retail Market is gaining pace and businesses have started understanding the benefits of analytics in the present day highly dynamic business environment. The market has witnessed several important developments over the past few years, with mounting volumes of business data and the shift from traditional data analysis platforms to self-service business analytics being some of the most prominent ones.

By ApplicationApplication A, Application B, Application C

By Products:Cloud Based, On-Premises

For the future period, sound forecasts on market value and volume are offered for each type and application. In the same period, the report also provides a detailed analysis of market value and consumption for each region. These insights are helpful in devising strategies for the future and take necessary steps. New project investment feasibility analysis and SWOT analysis are offered along with insights on industry barriers. Research findings and conclusions are mentioned at the end.

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Table of Content

Chapter 1Industry Overview

Chapter 2Major Segmentation (Classification, Application and etc.) Analysis

Chapter 3Production Market Analysis

Chapter 4Sales Market Analysis

Chapter 5Consumption Market Analysis

Chapter 6Production, Sales and Consumption Market Comparison Analysis

Chapter 7Major Manufacturers Production and Sales Market Comparison Analysis

Chapter 8Marketing Channel Analysis

Chapter 9Industry Chain Analysis

Chapter 10Global and Regional Market Forecast

Chapter 11Major Manufacturers Analysis

Chapter 12New Project Investment Feasibility Analysis

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Machine Learning in Retail Market Size is set to Grow at a Remarkable Pace in the Coming Years - Apsters News

Mastering machine learning is not easy, even if youre a crack programmer. Ive seen many people come from a solid background of writing software in different domains (gaming, web, multimedia, etc.) thinking that addingmachine learningto their roster of skills is another walk in the park. Its not. And every single one of them has been dismayed.

I see two reasons for why the challenges of machine learning are misunderstood. First, as the name suggests, machine learning is software that learns by itself as opposed tobeing instructed on every single rule by a developer. This is an oversimplification that many media outlets with little or no knowledge of the actual challenges of writing machine learning algorithms often use when speaking of the ML trade.

[Read:How the Dutch government uses data to predict the weather and prepare for natural disasters]

The second reason, in my opinion, are the many books and courses that promise to teach you the ins and outs of machine learning in a few hundred pages (and the ads on YouTube that promise to net you a machine learning job if you pass an online course). Now, I dont what to vilify any of those books and courses. Ivereviewed severalof them(and will review some more in the coming weeks), and I think theyre invaluable sources for becoming a good machine learning developer.

But theyre not enough. Machine learning requires both good coding and math skills and a deep understanding of various types of algorithms. If youre doing Python machine learning, you have to have in-depth knowledge of many libraries and also master the many programming and memory-management techniques of the language. And, contrary to what some people say, you cant escape the math.

And all of that cant be summed up in a few hundred pages. Rather than a single volume, the complete guide to machine learning would probably look like Donald Knuths famousThe Art of Computer Programmingseries.

So, what is all this tirade for? In my exploration of data science and machine learning, Im always on the lookout for books that take a deep dive into topics that are skimmed over by the more general, all-encompassing books.

In this post, Ill look atPython for Data AnalysisandPractical Statistics for Data Scientists, two books that will help deepen your command of the coding and math skills required to master Python machine learning and data science.

Python for Data Analysis, 2nd Edition,is written by Wes McKinney, the creator of the pandas, one of key libraries using in Python machine learning. Doing machine learning in Python involves loading and preprocessing data in pandas before feeding them to your models.

InPython for Data Analysis, McKinney takes you through the entire functionality of pandas and manages to do so without making it read like a reference manual. There are lots of interesting examples that build on top of each other and help you understand how the different functions of pandas tie in with each other. Youll go in-depth on things such as cleaning, joining, and visualizing data sets, topics that are usually only discussed briefly in most machine learning books.Most books and courses on machine learning provide an introduction to the main pandas components such as DataFrames and Series and some of the key functions such as loading data from CSV files and cleaning rows with missing data. But the power of pandas is much broader and deeper than what you see in a chapters worth of code samples in most books.

Youll also get to explore some very important challenges, such as memory management and code optimization, which can become a big deal when youre handling very large data sets in machine learning (which you often do).

What I also like about the book is the finesse that has gone into choosing subjects to fit in the 500 pages. While most of the book is about pandas, McKinney has taken great care to complement it with material about other important Python libraries and topics. Youll get a good overview of array-oriented programming with numpy, another important Python library often used in machine learning in concert with pandas, and some important techniques in using Jupyter Notebooks, the tool of choice for many data scientists.

All this said, dont expectPython for Data Analysisto be a very fun book. It can get boring because it just discusses working with data (which happens to be the most boring part of machine learning). There wont be any end-to-end examples where youll get to see the result of training and using a machine learning algorithm or integrating your models in real applications.

My recommendation:You should probably pick upPython for Data Analysisafter going through one of theintroductory or advanced books on data scienceor machine learning. Having that introductory background on working with Python machine learning libraries will help you better grasp the techniques introduced in the book.

WhilePython for Data Analysisimproves your data-processing and -manipulation coding skills, the second book well look at,Practical Statistics for Data Scientists, 2nd Edition,will be the perfect resource to deepen your understanding of the core mathematical logic behind many key algorithms and concepts that you often deal with when doing data science and machine learning.

But again, the key here is specialization.The book starts with simple concepts such as different types of data, means and medians, standard deviations, and percentiles. Then it gradually takes you through more advanced concepts such as different types of distributions, sampling strategies, and significance testing. These are all concepts you have probably learned in math class or read about in data science and machine learning books.

On the one hand, the depth thatPractical Statistics for Data Scientistsbrings to each of these topics is greater than youll find in machine learning books. On the other hand, every topic is introduced along with coding examples in Python and R, which makes it more suitable than classic statistics textbooks on statistics. Moreover, the authors have done a great job of disambiguating the way different terms are used in data science and other fields. Each topic is accompanied by a box that provides all the different synonyms for popular terms.

As you go deeper into the book, youll dive into the mathematics of machine learning algorithms such as linear and logistic regression, K-nearest neighbors, trees and forests, and K-means clustering. In each case, like the rest of the book, theres more focus on whats happening under the algorithms hood rather than using it for applications. But the authors have again made sure the chapters dont read like classic math textbooks and the formulas and equations are accompanied by nice coding examples.

LikePython for Data Analysis,Practical Statistics for Data Scientistscan get a bit boring if you read it end to end. There are no exciting applications or a continuous process where you build your code through the chapters. But on the other hand, the book has been structured in a way that you can read any of the sections independently without the need to go through previous chapters.

My recommendation:ReadPractical Statistics for Data Scientistsafter going through an introductory book on data science and machine learning. I definitely recommend reading the entire book once, though to make it more enjoyable, go topic by topic in-between your exploration of other machine learning courses. Also keep it handy. Youll probably revisit some of the chapters from time to time.

I would definitely countPython for Data AnalysisandPractical Statistics for Data Scientistsas two must-reads for anyone who is on the path of learning data science and machine learning. Although they might not be as exciting as some of the more practical books, youll appreciate the depth they add to your coding and math skills.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published July 8, 2020 09:49 UTC

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These 2 books will strengthen your command of Python machine learning - The Next Web

Machine learning is critical to effective and scalable virtual care; allowing clinicians to simultaneously improve outcomes and reduce the cost of care.

With the proliferation of sensors and wearables in the home setting, a new host of data is now available for clinicians. And yet, no human can feasibly and economically make sense of this deluge of data. Enter machine learning, which according to Nature, is already showing significant promise augmenting clinicians ability to treat Type II Diabetes, analyze skin lesions, and electrocardiograms. According to Accenture, machine learning will save $150 Billion a year in healthcare costs by 2026.

And yet, todays care model, as is perpetuated by telehealth providers, struggles to adapt and learn from each patient interaction as any learned knowledge that can benefit a population, is effectively lost when clinicians press end call after each session.

Machine learning will unlock clinicians' ability to deliver personalized care at population scale.

Effectively bridging a capacity gap, machine learning is critical to understanding the deluge of data coming from multivariate sensors in the patients home.

Enabling clinicians to scale personalized care to thousands of patients, machine learning will not only allow clinicians to practice top of license it will also foster a learning system that gets smarter with each patient interaction.

Underpinning this opportunity must be an emphasis on transparency and accountability into the drivers of recommendations coming from any machine learning system.

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The value of context and meaning: Virtual care's transformative potential for patient insights - ModernHealthcare.com

A recent study found that two-thirds of health systems have experienced a significant decline in income, with 27% losing money in at least one of the three years studied. The drop in earnings from fiscal year (FY) 2015 to FY 2017 represented $6.8 billion, a startling 44% reduction. There was some improvement in FY 2018, with revenue growth exceeding expenses by 0.1% for the first time since 2015 but growth remains lackluster.

In order for hospitals to advance or maintain standards of care, and to grow in ways that best serve patient needs, they have to get smarter about resource utilization. Operating rooms (ORs) should be a top priority. Not only do ORs represent the majority of a hospitals margin, they are also ripe with ways to increase efficiency and improve capacity. (Read: Top 20 AI Use Cases: Artificial Intelligence in Healthcare)

Multiple factors go into OR utilization: how long procedures are expected to take, which provider is using the room, unforeseen complications that occur during surgeries, last-minute additions or removals to the schedule, and much more. Scheduling ORs is not an exact science, but thanks to advances in data analytics, its getting pretty close.

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New machine learning (ML) solutions can take a variety of data points gleaned from every procedure that happens in every OR in a health system. They analyze this information and make predictions that can be used to effect improvements and adjustments for the future whether its a slight shift in booking times, hiring more staff, or establishing new policies for rooms.

These solutions identify portions of time that can be repurposed, collecting underutilized blocks of time without impacting existing case volume. They also erase countless hours of manual research, conducting queries in mere seconds. (Read: How AI in Healthcare is Identifying Risks and Saving Money.)

Additionally, the insights they yield can save health systems considerable expense, while opening up more treatment opportunities, and thus increase revenue all while improving patient access and the overall patient experience.

Getting the right insights to the right people in OR environments can be an enormous challenge, especially when different groups are looking for answers to different questions. One unified data analytics platform can help, enabling each constituency to find exactly what they need to improve decision-making, which ultimately leads to advancing standards of care, through machine learning.

New machine learning solutions can uncover the various drivers behind high-level trends in volume and utilization across an entire group. For example, from one interface, an executive at a large health group could look at whats happening at a specific hospital or region over a designated time range (such as over the last quarter). The executive might want to generate a holistic view of measures such as OR turnover ratio, add-on ratio, on-time starts, and more.

With this information, it becomes much easier to see how the business of the facility is actually performing. Is it growing? How is its utilization trending?

Based on these results, the executive might want to take a more in-depth look, viewing certain categories year-over-year, grouped by month, in order to compare, spot trends, and get an aggregate historical progression.

Additionally, he could check case volume to look for drivers behind volume increases and which service lines (orthopedics, general surgery, or cardiac care, for example) have experienced the most growth.

Robots are valuable assets, so much so that many groups have committees focused on robots to establish how well they are being used. Robotic program leaders can leverage new data analytics solutions to monitor robot usage and ensure that assets are being fully utilized. With the right insights, committees can figure out if they need to buy additional robots or adjust policies around usage.

Reports can also separate out only robot cases or look at which providers do the most with robots. This helps robotic program leaders know how often ORs equipped with robots are being used for robotics cases only versus other cases, advancing standards of care through machine learning. (Read: What Do Patients Want From Healthcare Technology?)

They also can view specific rooms where robots live by day of the week and hour of the day. Maybe on Mondays a robot is not being used to its maximum potential or a particular provider who isnt using the robot consistently blocks the room. Insights such as these help robotic program leaders figure out where rooms are blocked and why so that they can be leveraged to the fullest.

New solutions can be invaluable to nurse managers as well, giving them the mechanism to make data-driven decisions about staffing across teams and service lines. They can use technology to see historical data (such as room occupancy) to help make future assignments.

Nurse managers might want to view aggregate room data for an entire facility, every hour of each day of the week over the last three months. This way they can spot the busiest (or least busy) times and staff accordingly.

Additionally, a nurse manager may hear from someone on staff that she is always working late. The manager can then run a query to validate if a problem exists by checking times in blocks that are running over and whether certain providers tend to take more time than budgeted. In this instance, the nurse manager could adjust staff hours to keep expectations in line with the likely reality.

These are just some examples of how hospitals are advancing standards of care through machine learning and data analytics, particularly in terms of their ORs. But in order for data to make an impact, it needs to be credible, timely and actionable. If it can be harnessed in this way, insights can change hospitals trajectories.

Data analytics and machine learning unlock the capacity of scarce assets by transforming core processes. Health systems are able to reduce operating costs and potentially defer the need for a facility or staff expansion and most importantly, they can reduce wait time for patients and increase access with greater OR availability. By making operations smarter, the profit-to-expense ratio moves closer to ideal, and organizations can grow efficiently into the future.

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Advancing Standards of Care Through Machine Learning - Techopedia

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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Originally published March 2017, updated May 2020

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What is Machine Learning? A definition - Expert System