New Jersey, United States,- Market Research Intellect sheds light on the market scope, potential, and performance perspective of the Global Machine Learning as a Service Market by carrying out an extensive market analysis. Pivotal market aspects like market trends, the shift in customer preferences, fluctuating consumption, cost volatility, the product range available in the market, growth rate, drivers and constraints, financial standing, and challenges existing in the market are comprehensively evaluated to deduce their impact on the growth of the market in the coming years. The report also gives an industry-wide competitive analysis, highlighting the different market segments, individual market share of leading players, and the contemporary market scenario and the most vital elements to study while assessing the global Machine Learning as a Service market.

The research study includes the latest updates about the COVID-19 impact on the Machine Learning as a Service sector. The outbreak has broadly influenced the global economic landscape. The report contains a complete breakdown of the current situation in the ever-evolving business sector and estimates the aftereffects of the outbreak on the overall economy.

Leading Machine Learning as a Service manufacturers/companies operating at both regional and global levels:

To get Incredible Discounts on this Premium Report, Click Here @ https://www.marketresearchintellect.com/ask-for-discount/?rid=195381&utm_source=3WN&utm_medium=888

The Machine Learning as a Service market report provides successfully marked contemplated policy changes, favorable circumstances, industry news, developments, and trends. This information can help readers fortify their market position. It packs various parts of information gathered from secondary sources, including press releases, web, magazines, and journals as numbers, tables, pie-charts, and graphs. The information is verified and validated through primary interviews and questionnaires. The data on growth and trends focuses on new technologies, market capacities, raw materials, CAPEX cycle, and the dynamic structure of the Machine Learning as a Service market.

This study analyzes the growth of Machine Learning as a Service based on the present, past and futuristic data and will render complete information about the Machine Learning as a Service industry to the market-leading industry players that will guide the direction of the Machine Learning as a Service market through the forecast period. All of these players are analyzed in detail so as to get details concerning their recent announcements and partnerships, product/services, and investment strategies, among others.

Sales Forecast:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Machine Learning as a Service market. Additionally, it includes a share of each segment of the Machine Learning as a Service market, giving methodical information about types and applications of the market.

Reasons for Buying Machine Learning as a Service Market Report

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It renders an in-depth analysis for changing competitive dynamics.

It presents a detailed analysis of changing competition dynamics and puts you ahead of competitors.

It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Machine Learning as a Service market.

This report helps the readers understand key product segments and their future.

Have Any Query? Ask Our Expert @ https://www.marketresearchintellect.com/need-customization/?rid=195381&utm_source=3WN&utm_medium=888

In the end, the Machine Learning as a Service market is analyzed for revenue, sales, price, and gross margin. These points are examined for companies, types, applications, and regions.

To summarize, the global Machine Learning as a Service market report studies the contemporary market to forecast the growth prospects, challenges, opportunities, risks, threats, and the trends observed in the market that can either propel or curtail the growth rate of the industry. The market factors impacting the global sector also include provincial trade policies, international trade disputes, entry barriers, and other regulatory restrictions.

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

Contact Us:

Mr. Steven Fernandes

Market Research Intellect

New Jersey ( USA )

Tel: +1-650-781-4080

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Machine Learning as a Service Market Growth By Manufacturers, Type And Application, Forecast To 2026 - 3rd Watch News

SEATTLE--(BUSINESS WIRE)--ExtraHop, the leader in cloud-native network detection and response, today announced that it has been named to the 2020 Forbes AI 50 list for its advances and innovation in the use of machine learning and artificial intelligence for cybersecurity.

Forbes evaluated hundreds of applications to recognize 50 private, U.S.-based companies for their innovative use of artificial intelligence to drive outcomes for customers and within their own organizations. ExtraHop was selected on the basis of company growth and revenue, as well as for its leadership in the use of machine learning and AI for cybersecurity threat detection, investigation, and response.

Increasingly advanced cybersecurity attacks, including those sponsored by nation-states, require a sophisticated approach to threat detection and response, said Arif Kareem, CEO of ExtraHop. This is not possible without machine learning and AI. At ExtraHop, weve applied these techniques to enable enterprises to detect and respond to cyber threats across complex enterprise and cloud environments at scale. Our inclusion on this list recognizes the strength of our approach.

ExtraHop Reveal(x) applies the scalable computing resources of the cloud for ML and AI. This cloud-scale machine learning is unique in the network detection and response (NDR) market, allowing Reveal(x) to apply millions of models to over 5,000 features of data derived from 4-plus petabytes of anonymized threat telemetry collected from more than 15 million devices and workloads worldwide every day. The sheer volume of data on which ExtraHops AI learns makes its detections focused, precise, and uniquely reliable as well as up to 95 percent faster.

To view the 2020 Forbes AI 50 List check out the entire list on Forbes.com.

ExtraHop was also listed as a 20 Best Cybersecurity Startups To Watch In 2020 on Forbes.com.

To learn more about ExtraHops approach to machine learning, visit https://www.extrahop.com/products/machine-learning/

About ExtraHop

ExtraHop delivers cloud-native network detection and response to secure the hybrid enterprise. Our breakthrough approach applies advanced machine learning to all cloud and network traffic to provide complete visibility, real-time threat detection, and intelligent response. With this approach, we give the world's leading enterprises including The Home Depot, Credit Suisse, Liberty Global and Caesars Entertainment the perspective they need to rise above the noise to detect threats, ensure the availability of critical applications, and secure their investment in cloud. To experience the power of ExtraHop, explore our interactive online demo or connect with us on LinkedIn and Twitter.

2020 ExtraHop Networks, Inc., Reveal(x), Reveal(x) 360, Reveal(x) Enterprise, and ExtraHop are registered trademarks or marks of ExtraHop Networks, Inc.

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ExtraHop Named to the Forbes AI 50 List for 2020 - Business Wire

This article was written for our sponsor, Consumer Education Services Inc.

According to a study by Northwestern Mutual, the average American has around $38,000 in personal debt exclusive of home mortgages.

As a growing debt load that stems from student loans, credit card bills and medical expenses begins to affect a greater number of Americans each year, some companies have started leveraging technology in an attempt to alleviate the pressure of the repayment process for consumers.

Typically, debt collection efforts include scheduled letters, emails and phone calls from the agency where the debt is owed. If an individual becomes delinquent on their debts, then the original creditor may engage a collection agency for more aggressive attempts to collect the balance. This can understandably be stressful for debtors, as theyre constantly reminded their outstanding payments are accumulating interest, affecting their credit scores, and impacting their financial future.

In many cases, machine learning technology can help alleviate the pressure of consumer debt repayment. By analyzing an individuals past repayment behavior, AI programs can improve the repayment process for the consumer by developing more effective repayment schedules and recommended payment amounts.

With machine learning technology, financial counselors can aggregate budget, income, debt load and savings data from a number of consumers and build models that can tell us, for example, whether or not an individual may be more successful using a bi-monthly payment program, explained Mike Croxson, CEO of non-profit credit counseling agency Consumer Education Services Inc. These are the kinds of things that machine learning can do to help consumers be more successful.

Although this specific type of technology is still in the preliminary stages, its already changing the way companies and consumers think about the debt repayment landscape. And its been in use for longer than you might think.

Ive been in this industry for more than 20 years and financial institutions such as banks and investment firms have already been using machine learning, said Diane Chen, executive director of the Institute of Consumer Money Management. Using that model, the traditional credit counseling industry can refine their process, which typically has a one-size-fits-all approach to payments. Machine learning can really help us offer a more individualized approach to client communication and repayment schedules.

Chens team at ICMM uses aggregated data from non-profit credit counseling agencies to analyze repayment trends by a variety of demographic categories. This helps build a scoring model to determine a more personalized approach to the debt repayment process.

The model for ICMMs machine learning initiative took 18 months to build. Once it was finished, the company teamed up with CESI to beta test the technology and refine the model using real-time analysis of client data.

Were using this technology in the financial counseling process to help us integrate communication and interventions to help clients be as successful as possible, said Croxon.

CESI has tested its machine learning initiative multiple times, which has yielded a model capable of generating substantial benefits, as well as more accurate predictive capabilities. The ability to test the accuracy of consumer behavior based on modeled assumptions has been a significant advantage of the collaboration between CESI and ICMM.

Information gleaned from the testing also revealed key information about the target audience for AI-based debt repayment technology.

We made the presumption that millennials would want to be communicated with by text message, whereas older generations like me would be more interested in traditional methods of communication, Croxson said. Ultimately, the data told us we were wrong everyone wanted more contemporary methods of communication instead of traditional means.

Currently, the principal data the model collects revolves around payment history, but Chen hopes in the future theyll be able to analyze things like monthly spending categories, incomes and more to improve the technologys predictive capabilities.

For the time being, CESI and ICMM hope theyve positioned themselves to be uniquely ahead of the machine learning debt repayment curve, and they believe this trend will likely become the norm.

The credit counseling industry is important for consumers financial well-being, and that means we need to have an eye toward the technology of the future, Croxson said. Somebody in our industry needs to lead the effort. If not us, then who?

This article was written for our sponsor, Consumer Education Services Inc.

Read the rest here:
Can technology predict your debt? How AI is changing the repayment landscape - WRAL Tech Wire

New Jersey, United States,- Market Research Intellect sheds light on the market scope, potential, and performance perspective of the Global Machine Learning in Manufacturing Market by carrying out an extensive market analysis. Pivotal market aspects like market trends, the shift in customer preferences, fluctuating consumption, cost volatility, the product range available in the market, growth rate, drivers and constraints, financial standing, and challenges existing in the market are comprehensively evaluated to deduce their impact on the growth of the market in the coming years. The report also gives an industry-wide competitive analysis, highlighting the different market segments, individual market share of leading players, and the contemporary market scenario and the most vital elements to study while assessing the global Machine Learning in Manufacturing market.

The research study includes the latest updates about the COVID-19 impact on the Machine Learning in Manufacturing sector. The outbreak has broadly influenced the global economic landscape. The report contains a complete breakdown of the current situation in the ever-evolving business sector and estimates the aftereffects of the outbreak on the overall economy.

Leading Machine Learning in Manufacturing manufacturers/companies operating at both regional and global levels:

To get Incredible Discounts on this Premium Report, Click Here @ https://www.marketresearchintellect.com/ask-for-discount/?rid=380663&utm_source=3WN&utm_medium=888

The Machine Learning in Manufacturing market report provides successfully marked contemplated policy changes, favorable circumstances, industry news, developments, and trends. This information can help readers fortify their market position. It packs various parts of information gathered from secondary sources, including press releases, web, magazines, and journals as numbers, tables, pie-charts, and graphs. The information is verified and validated through primary interviews and questionnaires. The data on growth and trends focuses on new technologies, market capacities, raw materials, CAPEX cycle, and the dynamic structure of the Machine Learning in Manufacturing market.

This study analyzes the growth of Machine Learning in Manufacturing based on the present, past and futuristic data and will render complete information about the Machine Learning in Manufacturing industry to the market-leading industry players that will guide the direction of the Machine Learning in Manufacturing market through the forecast period. All of these players are analyzed in detail so as to get details concerning their recent announcements and partnerships, product/services, and investment strategies, among others.

Sales Forecast:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Machine Learning in Manufacturing market. Additionally, it includes a share of each segment of the Machine Learning in Manufacturing market, giving methodical information about types and applications of the market.

Reasons for Buying Machine Learning in Manufacturing Market Report

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It renders an in-depth analysis for changing competitive dynamics.

It presents a detailed analysis of changing competition dynamics and puts you ahead of competitors.

It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Machine Learning in Manufacturing market.

This report helps the readers understand key product segments and their future.

Have Any Query? Ask Our Expert @ https://www.marketresearchintellect.com/need-customization/?rid=380663&utm_source=3WN&utm_medium=888

In the end, the Machine Learning in Manufacturing market is analyzed for revenue, sales, price, and gross margin. These points are examined for companies, types, applications, and regions.

To summarize, the global Machine Learning in Manufacturing market report studies the contemporary market to forecast the growth prospects, challenges, opportunities, risks, threats, and the trends observed in the market that can either propel or curtail the growth rate of the industry. The market factors impacting the global sector also include provincial trade policies, international trade disputes, entry barriers, and other regulatory restrictions.

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

Contact Us:

Mr. Steven Fernandes

Market Research Intellect

New Jersey ( USA )

Tel: +1-650-781-4080

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Machine Learning in Manufacturing Market Growth By Manufacturers, Type And Application, Forecast To 2026 - 3rd Watch News

NEW YORK (PRWEB) July 03, 2020

ModalityIQ, a community-based knowledge sharing and learning platform, is pleased to announce the acquisition of The Machine Learning Conference (MLconf and MLconf.com), a leader in serving the Machine Learning and Artificial Intelligence community.

Founded by Courtney and Shon Burton in 2012, MLconf has gathered thousands of members of the machine learning community to coalesce and share lessons learned through a series of 20+ conferences that showcase the latest innovations in machine learning tools, techniques and algorithms attracting Data Scientists, Machine Learning professionals and others who have a vested interest in ML/AI.

Sharing lessons learned that help advance the ML/AI community is at the core of ModalityIQs mission and is why the acquisition of MLconf made sense for us, said Richard Rivera, Founder & CEO of ModalityIQ. Courtneys years of service and her dedication to providing a platform for the ML/AI community to share, learn, and inspire one another made her the obvious person for us to partner with. We look forward to working with her and the MLconf team to continue to facilitate the sharing of knowledge within the ML/AI community today and in the years to come.

In addition to hosting annual conferences, MLconf also has a vibrant online presence through a community blog and job board at mlconf.com, a monthly newsletter, and via social media in addition to a prolific YouTube channel with hundreds of videos highlighting current and past MLconf presentations.

For some time, Ive seen areas of opportunity for MLconf to further engage the ML/AI community and expand our focus to facilitate the sharing of knowledge and lessons learned and I believe partnering with ModalityIQ will help elevate and enrich our mission, said Courtney Burton. ModalityIQs founding principle of fostering learning through collaboration is the same principle I founded MLconf on and I am excited to join the ModalityIQ team.

Brian DeCicco from Berkery Noyes served as the exclusive financial advisor to MLconf.

About ModalityIQModalityIQ is a community-based knowledge sharing and learning platform founded to facilitate the sharing of knowledge and know-how across the advanced and emerging technology landscape by providing an environment for exchanging ideas and inspiring open dialogue to advance technological capabilities and knowledge-gain in business and in people. For more information, visit http://www.modalityiq.com.

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ModalityIQ Announces Acquisition of The Machine Learning Conference - PR Web

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Machine learning algorithms trained on large data sets have proven useful for spotting past patterns. Examples include stable environments like image databases or board games.

When it comes to messy, real-world data, however, those same ML algorithms often fall short, critics say, rigid and unable to adapt. Machine learning algorithms perform remarkably poorly on time-series predictions, assert researchers at causalLens, a platform developer that offers real-time economic predictions.

Current machine learning platforms largely fail to provide time-series predictions because correlations that have held in the past may simply not continue to hold in the future, the London-based company notes. Thats a particular problem in areas like finance and business where time-series data types are ubiquitous.

Those correlations tend to be single data points, unsuited to capturing context or complex relationships. In one example, an algorithm can be given access to a data set about dairy commodity prices to predict the price of cheese. The algorithm may conclude that butter prices as a guide to predicting the cost of limburger.

Eluding the algorithm is a fundamental assumption about the cost of dairy products: the hidden common cause of price spikes for cheese and butter is the cost of milk. Therefore, a sudden change in the price of butterconsumers preference for olive oil, for instanceis unrelated to milk prices. Hence, the faulty correlation between butter and cheese cant be used to predict the latters price.

The company touts its causal AI framework as looking beyond correlations to learn obvious relationships and then propose plausible hypotheses about more obscure chains of causality, it noted in a recent research bulletin. The approach allows data scientists to add domain knowledge and real-world context to improve predictive analytics.

Indeed, new open source libraries have emerged that seek to help data scientists and domain experts develop adaptable models based on causal relationships rather than data correlations alone. For example, the CausalNex library released earlier this year allows data dependencies to be expressed in network graphs that can be scanned by domain experts to eliminate spurious correlations in machine learning models.

CausalNex is the second open source release of a causal AI data set after Kedro, a library aimed at production ML code. The new library applies what-if analysis to Bayesian networks on the assumption that a probabilistic model is more intuitive in describing causality than traditional ML frameworks based on correlation analysis and pattern recognition.

Causal AI proponents also argue their approach makes better use of data to come up with more accurate predictions through the frameworks ability to simulate different scenarios.

Conventional machine learning approaches are, quite literally, stuck in the past, the company concludes. They are fooled by illusory patterns and are unable to quickly adapt to new conditions.

Related

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

Link:
Machine Learning Is Living in the Past - EnterpriseAI

Align Technology uses DevSecOps tactics to keep complex projects on track and align business and IT goals.

Image: AndreyPopov/Getty Images/iStockphoto

Align Technology's Chief Digital Officer Sreelakshmi Kolli is using machine learning and DevOps tactics to power the company's digital transformation.

Kolli led the cross-functional team that developed the latest version of the company's My Invisalign app. The app combines several technologies into one product including virtual reality, facial recognition, and machine learning. Kolli said that using a DevOps approach helped to keep this complex work on track.

"The feasibility and proof of concept phase gives us an understanding of how the technology drives revenue and/or customer experience," she said. "Modular architecture and microservices allows incremental feature delivery that reduces risk and allows for continuous delivery of innovation."

SEE: Research: Microservices bring faster application delivery and greater flexibility to enterprises (TechRepublic Premium)

The customer-facing app accomplishes several goals at once, the company said:

More than 7.5 million people have used the clear plastic molds to straighten their teeth, the company said. Align Technology has used data from these patients to train a machine learning algorithm that powers the visualization feature in the mobile app. The SmileView feature uses machine learning to predict what a person's smile will look like when the braces come off.

Kolli started with Align Technology as a software engineer in 2003. Now she leads an integrated software engineering group focused on product technology strategy and development of global consumer, customer and enterprise applications and infrastructure. This includes end user and cloud computing, voice and data networks and storage. She also led the company's global business transformation initiative to deliver platforms to support customer experience and to simplify business processes.

Kolli used the development process of the My Invisalign app as an opportunity to move the dev team to DevSecOps practices. Kolli said that this shift represents a cultural change, and making the transition requires a common understanding among all teams on what the approach means to the engineering lifecycle.

"Teams can make small incremental changes to get on the DevSecOps journey (instead of a large transformation initiative)," she said. "Investing in automation is also a must for continuous integration, continuous testing, continuous code analysis and vulnerability scans." To build the machine learning expertise required to improve and support the My Invisalign app, she has hired team members with that skill set and built up expertise internally.

"We continue to integrate data science to all applications to deliver great visualization experiences and quality outcomes," she said.

Align Technology uses Amazon Web Services to run its workloads.

The My Invisalign app accomplished several goals for the company: connecting patients with doctors, creating a new marketing tool with the SmileView feature, and evolving the software development process.

Kolli said that IT leaders should work closely with business leaders to make sure initiatives support business goals such as revenue growth, improved customer experience, or operational efficiencies, and modernize the IT operation as well.

"Making the line of connection between the technology tasks and agility to go to market helps build shared accountability to keep technical debt in control," she said.

Align Technology released the revamped app in late 2019. In May of this year, the company released a digital version tool for doctors that combines a photo of the patient's face with their 3D Invisalign treatment plan.

This ClinCheck "In-Face" Visualization is designed to help doctors manage patient treatment plans.

The visualization workflow combines three components of Align's digital treatment platform: Invisalign Photo Uploader for patient photos, the iTero intraoral scanner to capture data needed for the 3D model of the patient's teeth, and ClinCheck Pro 6.0. ClinCheck Pro 6.0 allows doctors to modify treatment plans through 3D controls.

These new product releases are the first in a series of innovations to reimagine the digital treatment planning process for doctors, Raj Pudipeddi, Align's chief innovation, product, and marketing officer and senior vice president, said in a press release about the product.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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My Invisalign app uses machine learning and facial recognition to sell the benefits of dental work - TechRepublic

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This post is part ofAI education, a series of posts that review and explore educational content on data science and machine learning. (In partnership withPaperspace)

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 adding machine learning to 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 to being 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.

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. Ive reviewed several of 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 famous The Art of Computer Programming series.

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 at Python for Data Analysis and Practical 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.

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.

In Python 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.

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 expect Python for Data Analysis to 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 up Python for Data Analysis after going through one of the introductory or advanced books on data science or machine learning. Having that introductory background on working with Python machine learning libraries will help you better grasp the techniques introduced in the book.

While Python for Data Analysis improves 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.

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.

But again, the key here is specialization.

On the one hand, the depth that Practical Statistics for Data Scientists brings 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.

Like Python for Data Analysis, Practical Statistics for Data Scientists can 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: Read Practical Statistics for Data Scientists after 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 count Python for Data Analysis and Practical Statistics for Data Scientists as 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.

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2 books to strengthen your command of python machine learning - TechTalks

A research report on the Global Machine Learning in Communication Market provides the overall growth forces and present scenario of the Machine Learning in Communication industry. The research report also integrates the significant insights for the number of investors that are seeking to increase their market status in the past and upcoming industry scenario. In addition, the study extensively studies the numerous factors which are likely to influence the trend of the market over the forecast period. The global Machine Learning in Communication market report offers a holistic view of the industry along with the several factors which are limiting and driving the expansion of the global Machine Learning in Communication market. Similarly, to assess the complete market size, this study offers an accurate analysis of the market players landscape and a corresponding detailed study regarding the manufacturers functioning in the Machine Learning in Communication market. Furthermore, the Machine Learning in Communication industry report offers quantitative and qualitative evaluation which helps in understanding the past, current, and potential market scenario.

Request a sample of Machine Learning in Communication Market report @ https://www.orbisresearch.com/contacts/request-sample/4629300?utm_source=Bis

The global Machine Learning in Communication market report also covers present trends across various regions with a number of opportunities that are there for the service providers across the region. In addition, the study offers a concise overview of the manufacturing plan of the key companies which comprises an extensive analysis of the manufacturing unit, research & development capacity, as well as suppliers of the raw materials. This report delivers a complete analysis of the industry segmentation and the growth factors that are impacting the market. The Machine Learning in Communication market study also provides other significant data such as cost structure, value chain analysis, and Porters Five analysis which offers market outlook.

Major companies of this report:

AmazonIBMMicrosoftGoogleNextivaNexmoTwilioDialpadCiscoRingCentral

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The global Machine Learning in Communication market report delivers the absolute mapping of the market providers that are functioning in the Machine Learning in Communication market with their market status on the basis of business developments as well as various product offerings that offers the complete competitive landscape of the market. In addition to this, the research report majorly focuses on the expansive analysis of the entire strategic overview along with the various activities of the market players such as merger & acquisition, partnerships, collaborations, agreements, and others which offers a clear idea of their present market scenario. Similarly, the global Machine Learning in Communication market report emphasizes on the major economies such as Asia Pacific, Europe, North America, and the Middle East and Africa.

Machine Learning in Communication Market Segmentation by Type:

Cloud-BasedOn-Premise

Machine Learning in Communication Market Segmentation by Application:

Network OptimizationPredictive MaintenanceVirtual AssistantsRobotic Process Automation (RPA)

This Machine Learning in Communication Market research study provides the business landscape of the prominent players with their revenue, industry overview, and product portfolio by segment and regional outlook. This report also covers a complete analysis of the major strategies adopted by the service providers in order to increase a market footprint against other service providers.

Major Points from Table of Content:Section 1 Machine Learning in Communication Product DefinitionSection 2 Global Machine Learning in Communication Market Manufacturer Share and Market OverviewSection 3 Manufacturer Machine Learning in Communication Business IntroductionSection 4 Global Machine Learning in Communication Market Segmentation (Region Level)Section 5 Global Machine Learning in Communication Market Segmentation (Product Type Level)Section 6 Global Machine Learning in Communication Market Segmentation (Industry Level)Section 7 Global Machine Learning in Communication Market Segmentation (Channel Level)Section 8 Machine Learning in Communication Market Forecast 2019-2024Section 9 Machine Learning in Communication Segmentation Product TypeSection 10 Machine Learning in Communication Segmentation IndustrySection 11 Machine Learning in Communication Cost of Production Analysis

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View original post here:
Machine Learning in Communication Market 2020 Trends, Growth Factors, Detailed Analysis and Forecast to 2024: Amazon, IBM, Microsoft, Google, Nextiva...

Originally published in Chip Huyen Blog, June 22, 2020

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:

After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that arent being actively developed, and tools that nobody uses, I got 202 tools. See the full list. Please let me know if there are tools you think I should include but arent on the list yet!

Disclaimer

This post consists of 6 parts:

I. OverviewII. The landscape over timeIII. The landscape is under-developedIV. Problems facing MLOpsV. Open source and open-coreVI. Conclusion

I. OVERVIEW

In one way to generalize the ML production flow that I agreed with, it consists of 4 steps:

I categorize the tools based on which step of the workflow that it supports. I dont include Project setup since it requires project management tools, not ML tools. This isnt always straightforward since one tool might help with more than one step. Their ambiguous descriptions dont make it any easier: we push the limits of data science, transforming AI projects into real-world business outcomes, allows data to move freely, like the air you breathe, and my personal favorite: we lived and breathed data science.

I put the tools that cover more than one step of the pipeline into the category that they are best known for. If theyre known for multiple categories, I put them in the All-in-one category. I also include the Infrastructure category to include companies that provide infrastructure for training and storage. Most of these are Cloud providers.

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Excerpt from:
What I Learned From Looking at 200 Machine Learning Tools - Machine Learning Times - machine learning & data science news - The Predictive...