Category Archives: Machine Learning

The editors at Solutions Review have compiled this list of the best machine learning courses and online training to consider for 2020.

Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications. It is especially prevalent in the fields of business intelligence and data management.

With this in mind, weve compiled this list of the best machine learning courses and online training to consider if youre looking to grow your AI or data science skills for work or play. This is not an exhaustive list, but one that features the best machine learning courses and training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.

Platform: Coursera

Description: This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Related paths/tracks: Machine Learning with Python (IBM), Machine Learning Specialization (University of Washington),Mathematics for Machine Learning Specialization (Imperial College London), Machine Learning with TensorFlow on Google Cloud Platform Specialization (Google Cloud)

Platform: DataCamp

Description: In this non-technical course, youll learn everything youve been too afraid to ask about machine learning. Theres no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? Theyre all covered.

Related paths/tracks: Machine Learning for Business, Machine Learning with Tree-Based Models in Python, Machine Learning with caret in R

Platform: Edureka

Description: Edurekas Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Nave Bayes and Q-Learning. This training exposes you to concepts of statistics, time series and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms. Throughout the course, youll be solving real-life case studies on media, healthcare, social media, aviation, and HR.

Related paths/tracks:Graphical Models Certification Training, Reinforcement Learning, Natural Language Processing with Python

Platform: edX

Description: Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

Related paths/tracks: Machine Learning for Data Science and Analytics (Columbia), Machine Learning Fundamentals (UC San Diego), Machine Learning with Python: from Linear Models to Deep Learning

Platform: Experfy

Description: As an introduction to machine learning, this course is presented at a level that is readily understood by all individuals interested in machine learning. This course provides a history of machine learning, defines data, and explains what is meant by big data; and classifies data in terms of computer programming. It covers the basic concept of numeral systems and the common numeral systems used by computer hardware to establish programming languages. Providing practical applications of machine learning.

Related paths/tracks: Machine Learning for Predictive Analytics, Feature Engineering for Machine Learning, Supervised Learning: Classification, Supervised Learning: Linear Regression, Unsupervised Learning: Clustering

Platform: Intellipaat

Description: This machine learning course will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Intellipaats machine learning certification training comes with 24/7 support, multiple assignments, and project work to help you gain real-world exposure.

Related path/track: Artificial Intelligence Course and Training

Platform: LinkedIn Learning

Description: In this course, we review the definition and types of machine learning: supervised, unsupervised, and reinforcement. Then you can see how to use popular algorithms such as decision trees, clustering, and regression analysis to see patterns in your massive data sets. Finally, you can learn about some of the pitfalls when starting out with machine learning.

Related paths/tracks: Essential Math for Machine Learning: Python Edition, Applied Machine Learning: Algorithms, Applied Machine Learning Foundations

Platform: Mindmajix

Description: Mindmajix Machine Learning Training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. You will gain in-depth knowledge of all the concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, etc.,through real-time industry use cases, and this will help you in clearing the Machine Learning Certification Exam.

Related path/track: Machine Learning with Python Training

Platform: Pluralsight

Description: Have you ever wondered what machine learning is? Thats what this course is designed to teach you. Youll explore the open-source programming language R, learn about training and testing a model as well as using a model. By the time youre done, youll have a clear understanding of exactly what machine learning is all about.

Related paths/tracks: Understanding Machine Learning with Python, Understanding Machine Learning with R, Machine Learning: Executive Briefing, How Machine Learning Works, Deploying Machine Learning Solutions

Platform: Simplilearn

Description: This machine learning online course offers an in-depth overview of machine learning topics including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time-series modeling. Learn how to use Python in this machine learning certification training to draw predictions from data.

Platform: Skillshare

Description: If youve got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry and prepare you for a move into this hot career path. This comprehensive course includes68 lecturesspanning almost9 hours of video, and most topics includehands-on Python code examplesyou can use for reference and for practice.

Related paths/tracks:Demystifying Artificial Intelligence: Understanding Machine Learning, Goal-Driven Artificial Intelligence and Machine Learning

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 the industry.

Related paths/tracks: Intro to Machine Learning with PyTorch,Intro to Machine Learning with TensorFlow

Platform: Udemy

Description: This course has been designed by two professional data scientists that can share their knowledge andhelp you learn complex theory, algorithms, and coding libraries in a simple way. The course will walk you step-by-step into the world of machine learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science.

Related paths/tracks:Python for Data Science and Machine Learning Bootcamp, Machine Learning, Data Science and Deep Learning with Python,Data Science and Machine Learning Bootcamp with R

Timothy is Solutions Review's Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.

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The 13 Best Machine Learning Courses and Online Training for 2020 - Solutions Review

Article by Snowflake senior sales engineer Rishu Saxena.

Once a technical novelty seen only in software development labs or enormous organisations, machine learning (ML) is poised to become an important tool for large numbers of Australian and New Zealand businesses.

Lured by promises of improved productivity and faster workflows, companies are investing in the technology in rising numbers. According to research firm Fortune Business Insights, the ML market will be worth US$117.19 billion by 2027.

Historically, ML was perceived to be an expensive undertaking that required massive upfront investment in people, as well as both storage and compute systems. Recently, many of the roadblocks that had been hindering adoption have now been removed.

One such roadblock was not having the right mindset or strategy when undertaking ML-related projects. Unlike more traditional software development, ML requires a flexible and open-ended approach. Sometimes it wont be possible to assess the result accurately, and this could well change during deployment and preliminary use.

A second roadblock was the lack of ML automation tools available on the market. Thanks to large investments and hard work by computer scientists, the latest generation of auto ML tools are feature-rich, intuitive and affordable.

Those wanting to put them to work no longer have to undertake extensive data science training or have a software development background. Dubbed citizen data scientists, these people can readily experiment with the tools and put their ideas into action.

The way data is stored and accessed by ML tools has also changed. Advances in areas such as cloud-based data warehouses and data lakes means an organisation can now have all its data in a single location. This means the ML tools can scan vast amounts of data relatively easily, potentially leading to insights that previously would have gone unnoticed.

The lowering of storage costs has further assisted this trend. Where an organisation may have opted to delete or archive data onto tape, that data can now continue to be stored in a production environment, making it accessible to the ML tools.

For those organisations looking to embrace ML and experience the business benefits it can deliver, there are a series of steps that should be followed:

When starting with ML, dont try to run before you walk. Begin with small, stand-alone projects that give citizen data scientists a chance to become familiar with the machine learning process, the tools, how they operate, and what can be achieved. Once this has been bedded down, its then easier to gradually increase the size and scope of activities.

To start your ML journey, lean on the vast number of auto ML tools available on the market instead of using open source notebook based IDEs that require high levels of skills and familiarity with ML.

There is an increasing number of ML tools on the market, so take time to evaluate options and select the ones best suited to your business goals. This will also give citizen data scientists required experience before any in-house development is undertaken.

ML is not something that has to be the exclusive domain of the IT department. Encourage the growth of a pool of citizen data scientists within the organisation who can undertake projects and share their growing knowledge.

To enable ML tools to do as much as possible, centralise the storage of all data in your organisation. One option is to make use of a cloud-based data platform that can be readily scaled as data volumes increase.

Once projects have been underway for some time, closely monitor the results being achieved. This will help to guide further investments and shape the types of projects that will be completed in the future.

Once knowledge and experience levels within the organisation have increased, consider tackling more complex projects. These will have the potential to add further value to the organisation and ensure that stored data is generating maximum value.

The potential for ML to support organisations, help them to achieve fresh insights, and streamline their operations is vast. By starting small and growing over time, its possible to keep costs under control while achieving benefits in a relatively short space of time.

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Why organisations are poised to embrace machine learning - IT Brief Australia

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The future is now. We've been discussing the fact that RPA truly transforms the costs, accuracy, productivity, speed and efficiency of your enterprise. That transformation is all the more powerful with cognitive solutions baked-in.

Our old friends at Automation Anywhere combine forces with our new friends at DataRobot to discuss the integration and convergence of RPA & Automated ML and how that combination can hurdle your enterprise further through this fourth industrial revolution.

Watch the session on demand now.

The Convergence of RPA and Automated Machine LearningGreg van Rensburg, Director, Solutions Consulting,Automation AnywhereColin Priest, Vice President, AI Strategy,DataRobot

Robotic Process Automation (RPA) has disrupted repetitive business processes across a variety of industries. The combination of RPA, cognitive automation, and analytics is a game changer for unstructured data-processing and for gaining real-time insights. The next frontier? A truly complete, end-to-end process automation with AI-powered decision-making and predictive abilities. Join Automation Anywhere and DataRobot at this session to learn how organisations are using business logic and structured inputs, through a combination of RPA and Automated Machine Learning, to automate business processes, reduce customer churn and transform to digital operating models.

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The Convergence of RPA and Automated Machine Learning - AiiA

Machine Learning Operationalization Software market research report bestows clients with the best results and for the same, it has been produced by using integrated approaches and the latest technology. With this market report, it becomes easier to establish and optimize each stage in the lifecycle of an industrial process that includes engagement, acquisition, retention, and monetization. This market report gives a wide-ranging analysis of the market structure and the evaluations of the various segments and sub-segments of this industry. Not to mention, several charts and graphs have been used effectively in the Machine Learning Operationalization Software Market report to represent the facts and figures in a proper way.

In this winning Machine Learning Operationalization Software market research report, industry trends are plotted on macro-level which helps clients and the businesses comprehend the market place and possible future issues. In this business report, market drivers and market restraints are studied carefully along with the analysis of the market structure. In no doubt, businesses are significantly relying on the different segments covered in the market research report hence Machine Learning Operationalization Software market document presents them with better insights to drive the business into the right direction. The report also offers great inspiration to seek new business ventures and evolve better.

Access insightful study with over 100+ pages, list of tables & figures, profiling 10+ companies. Ask for Free Sample Copy @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-machine-learning-operationalization-software-market&skp

Major Industry Competitors:Machine Learning Operationalization Software Market

The Major Players Covered In The Machine Learning Operationalization Software Report Are The Mathworks, Inc, Sas Institute Inc, Microsoft, Parallelm, Inc, Algorithmia Inc, Tibco Software Inc, Sap, Ibm Corporation, Seldon Technologies Ltd, Actico Gmbh, Rapidminer, Inc And Knime Ag Among Other Domestic And Global Players. Market Share Data Is Available For Global, North America, Europe, Asia-Pacific, Middle East And Africa And South America Separately. Dbmr Analysts Understand Competitive Strengths And Provide Competitive Analysis For Each Competitor Separately.

Market Analysis:Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Market Is Expected To Gain Market Growth In The Forecast Period Of 2020 To 2027. Data Bridge Market Research Analyses The Market Growing At A Cagr Of 44.2% In The Above-Mentioned Forecast Period.

The 2020 Annual Machine Learning Operationalization Software Market offers:

=> 100+ charts exploring and analysing the Machine Learning Operationalization Software market from critical angles including retail forecasts, consumer demand, production and more=> 10+ profiles of top Machine Learning Operationalization Software producing states, with highlights of market conditions and retail trends=> Regulatory outlook, best practices, and future considerations for manufacturers and industry players seeking to meet consumer demand=> Benchmark wholesale prices, market position, plus prices for raw materials involved in Machine Learning Operationalization Software type

Some extract from Table of Contents

Overview of Global Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Size (Sales Volume) Comparison by Type

Machine Learning Operationalization Software Size (Consumption) and Market Share Comparison by Application

Machine Learning Operationalization Software Size (Value) Comparison by Region

Machine Learning Operationalization Software Sales, Revenue and Growth Rate

Machine Learning Operationalization Software Competitive Situation and Trends

Strategic proposal for estimating availability of core business segments

Players/Suppliers, Sales Area

Analyse competitors, including all important parameters of Machine Learning Operationalization Software

Global Machine Learning Operationalization Software Manufacturing Cost Analysis

The most recent innovative headway and supply chain pattern mapping

Get Detailed TOC with Tables and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-machine-learning-operationalization-software-market&skp

Rapid Business Growth Factors

In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.

Key Points of this Report:

The depth industry chain includes analysis value chain analysis, porter five forces model analysis and cost structure analysis

It describes present situation, historical background and future forecast

Comprehensive data showing Machine Learning Operationalization Software capacities, production, consumption, trade statistics, and prices in the recent years are provided

The report indicates a wealth of information on Machine Learning Operationalization Software manufacturers

Machine Learning Operationalization Software market forecasts for next five years, including market volumes and prices is also provided

Raw Material Supply and Downstream Consumer Information is also included

Any other users requirements which is feasible for us

What Porters Five Forces of Competitive Analysis Provides?

Supplier power: An assessment of how easy it is for suppliers to drive up prices. This is driven by the: number of suppliers of each essential input; uniqueness of their product or service; relative size and strength of the supplier; and cost of switching from one supplier to another.

Buyer power: An assessment of how easy it is for buyers to drive prices down. This is driven by the: number of buyers in the market; importance of each individual buyer to the organisation; and cost to the buyer of switching from one supplier to another. If a business has just a few powerful buyers, they are often able to dictate terms.

Competitive rivalry: The main driver is the number and capability of competitors in the market. Many competitors, offering undifferentiated products and services, will reduce market attractiveness.

Threat of substitution: Where close substitute products exist in a market; it increases the likelihood of customers switching to alternatives in response to price increases. This reduces both the power of suppliers and the attractiveness of the market.

Threat of new entry: Profitable markets attract new entrants, which erodes profitability. Unless incumbents have strong and durable barriers to entry, for example, patents, economies of scale, capital requirements or government policies, then profitability will decline to a competitive rate.

Five forces analysis helps organizations to understand the factors affecting profitability in a specific industry, and can help to inform decisions relating to: whether to enter a specific industry; whether to increase capacity in a specific industry; and developing competitive strategies.

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Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.

Why Is Data TriangulationImportantin Qualitative Research?

This involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve completeness and confirmation of data using multiple methods

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Machine Learning Operationalization Software Market Size, Trends, Analysis, Demand, Outlook And Forecast 2027 The Mathworks, Inc, Sas Institute Inc,...

One of the challenges in scaling up meat production are issues of disease for the animals. Take bovine respiratory disease (BRD), for example. This contagious infection is responsible for nearly half of all feedlot deaths for cattle every year in North America. The industrys costs for managing the disease come close to $1 billion annually.

Preventative measures could significantly decrease these costs, and a small team comprising a data scientist, a college student and two entrepreneurs spent the past weekend at the Forbes Under 30 Agtech+ Hackathon figuring out a concept for better managing the disease.

Their solution? Tag-Ag, a conceptual set of predictive models that could take data already routinely gathered by cattle ranchers and tracked using ear tags to both identify cows at risk for BRD to focus prevention efforts; and to trace outbreaks of BRD to provide more focused treatment and management decisions.

By providing these insights, we can instill confidence in both big consumers such as McDonalds or Wal-Mart, and small consumers like you and me, that their meat is sourced from a healthy and sustainable operation, said team member Natalie McCaffrey, an 18 year-old undergraduate at Washington & Lee University at the Hackathons final presentations on Sunday evening.

McCaffrey was joined by Jacob Shields, 30, a senior research scientist at Elanco Animal Health; Marya Dzmiturk, 28, cofounder of TK startup Avanii and an alumnus of the 2020 Forbes Under 30 list in Manufacturing & Industry; and Shaina Steward, 29, founder of The Model Knowledge Group & Ekal Living.

They joined a larger group of hackathoners who brainstormed a variety of concepts related to animal health on Friday night before settling on three different ideas, at which point the group split into the smaller teams. The initial pitch for the Tag-Ag team was the use of AI & Big Data to help producers keep animals healthy.

As the Tag-Ag team began its research and development process on Saturday, one clear challenge was the scope of potential animal health issues, as well as a potentially intense labor process in collecting useful information. They settled on cattle because, McCaffrey says, big ranchers are already electronically collecting data on cattle, and because BRD by itself makes a huge impact on the industry.

Another advantage of using data already being collected, adds Shields, is that tools exist to build a model for the concepts predictive analytics based on whats out there. For supervised machine learning algorithms, the more inputs the better, he says. I dont believe well need additional studies to support this case, unless we knew of a handful of data points that werent being collected that really would help with the predictability.

For a business model, the Tag-Ag team suggests a subscription-based model, with a one-time implementation fee for any hardware needs. They believe that theres definitely room to raise capital, pointing to the size of the market loss theyre addressing plus the $500 million in venture capital invested in AgTech companies in 2019 alone.

Investors and institutions are recognizing opportunities in the AgTech space, McCaffrey says, and beyond that, she adds, our space of AI and data has space for additional players.

Team members: Natalie McCaffery, undergraduate, Washington & Lee University; Jacob Shields, senior research scientist, Elanco Animal Health; Marya Dzmiturk, cofounder, Avanii; Shaina Steward, 29, founder, The Model Knowledge Group and Ekal Living.

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Using Machine Learning To Predict Disease In Cattle Might Help Solve A Billion-Dollar Problem - Forbes

Morningstar had a problem.

Or rather, its millions of users did: The star-rating system, which drives huge volumes of assets, is inherently backwards-looking. These make-or-break badges label how good (or bad) a fund has performed, not how it will perform.

Morningstars solution was analysts: humans who dig deep into the big and popular fund products, then assign them forward-looking ratings. For analyzing the lesser or niche products, Morningstar unleashed the algorithms.

But the humans still have an edge, academic researchers found except in productivity.

We find that the analyst report, which is usually 4 or 5 pages, provides very detailed information, and is better than a star rating, as it claims to be, said Si Cheng, an assistant finance professor at the Chinese University of Hong Kong, in an interview. She and her co-authors of a just-published study also found that the forward-looking algorithmic analysis doesnt do as much as an analyst rating. If we look at very similar funds rated by human and machine, theyre quite different even though you have two-forward looking ratings.

[II Deep Dive: AQRs Problem With Machine Learning: Cats Morph Into Dogs]

The most potent value in all of these Morningstar modes came from the tone of human-generated reports assessed using machine-driven textual analysis.

Tone is likely to come from soft information, such as what the analyst picks up from speaking to fund management and investors. That deeply human sense of enthusiasm or pessimism matters when it comes through in conflict with the actual rating, which the analysts and algos based on quantitative factors.

Most of Morningstars users are retail investors, but only professionals are tapping into this human-quant arbitrage, discovered Cheng and her Peking University co-authors Ruichang Lu and Xiajun Zhang.

We do find that only institutional investors are taking advantage of analysts reports, she told Institutional Investor Tuesday. They do withdraw from a fund if the fund gets a gold rating but a pessimistic tone.

Cheng, her coauthors, and other academic researchers working in the same vein highlight cost one major advantage of algorithmic analysis over the old-fashioned kind. After initial set up, they automatically generate all of the analysis at a frequency that a human cannot replicate, Cheng said.

As Anne Tucker, director of the legal analytics and innovation initiative at Georgia State University, cogently put it, machine learning is leveraging components of human judgement at scale. Its not a replacement; its a tool for increasing the scale and the speed. On the legal side, almost all of our data is locked in text: memos, regulatory filings, orders, court decisions, and the like.

Tucker has teamed up with GSU analytics professor Yusen Xia and associate law professor Susan Navarro Smelcer to gather the text of fund filings and turn machine-learning programs onto them, searching for patterns and indicators of future risk and performance. The project is underway, and detailed in a recent working paper.

We have complied all of the investment strategy and risk sections from 2010 onwards, and are using text mining, machine learning, a suite of other computational tools to understand the content, study compliance, and then to aggregate texts in order to model emerging risks, Tucker told II. If we listen to the most sophisticated investors collectively, what can we learn? If we would have had these tools before 2008, would we have been able to pick up tremors?

Maybe but they wouldnt have picked up the Covid-19 crisis, early findings suggest.

There were essentially no pandemic-related risk disclosures before this happened, Tucker said.

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Machine Learning Is Cheaper But Worse Than Humans at Fund Analysis - Institutional Investor