ANALYSIS

What You Need to Know

• Breakouts in the Machine learning predictive analytics are MATLAB, Regression analysis, Sentiment analysis. Seriously consider these technologies to gain a strategic advantage.
• The technologies who are at the peak of their interest are TensorFlow, Azure machine learning studio, KNIME.
• By far most employment needs are found in the MATLAB, Data science, Splunk technologies.
• These 3 fields have the most active practitioners who have the specific skill set or experience: Data science, Artificial Intelligence, learning management system.
• MATLAB, Splunk, OpenCV lead in searches for information online.
• These three technologies are receiving the highest investments to gain clients: Apache Mahout, Naive Bayes, Random forest.
• These three technologies have the most active advertisers: learning management system, Data science, Mobile Learning.
• In patents, these three technologies have the most coverage Linear regression, ANOVA, Artificial Intelligence.
• The most publications are available for Artificial Intelligence, Probability distribution, Regression analysis.
• Instruction and courseware availability is highest in these technologies: Data science, Artificial Intelligence, MATLAB.

The Machine learning report evaluates technologies and applications in terms of their business impact, adoption rate and maturity level to help users decide where and when to invest.

The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Machine learning’s Predictive Analysis. The link takes you to a corresponding product in The Art of Service’s store to get started.

The Art of Service’s predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications.

The platform monitors over six thousand technologies and applications for months, looking for interest swings in a topic, concept, technology or application, not just a count of mentions. It then makes forecasts about the velocity of the interest over time, with peaks representing it breaking into the mainstream. Data sources include trend data, employment data, employee skills data, and signals like advertising spent, advertisers, search-counts, Instruction and courseware available activity, patents, and books published.

Predictive Analytics Scores:

006795 – MATLAB
002411 – Regression analysis
001117 – Sentiment analysis
000785 – Splunk
000784 – Linear regression
000613 – Amazon Machine Learning
000605 – TensorFlow
000527 – Ensemble learning
000438 – Deep learning
000322 – Databricks
000306 – SeatMe
000269 – Convolutional neural network
000259 – Online machine learning
000221 – Random forest
000221 – Feature engineering
000207 – Logistic regression
000185 – KNIME
000163 – Apache Mahout
000162 – learning management system
000161 – Apache Spark
000155 – Functional programming
000153 – Scikit-learn
000153 – Cognitive model
000140 – ThreatMetrix
000139 – Topic modeling
000123 – Deeplearning4j
000105 – R (programming language)
000105 – Anomaly detection
000095 – RapidMiner
000080 – Data science
000079 – Natural language processing
000077 – Azure machine learning studio
000076 – Gaussian process regression
000073 – Supervised learning
000072 – Unsupervised learning
000065 – Restricted Boltzmann machine
000061 – Boltzmann machine
000058 – ANOVA
000051 – Feature learning
000047 – Time complexity
000046 – Decision tree
000044 – Sift Science
000044 – OpenCV
000042 – Overfitting
000040 – SPSS Modeler
000040 – Adaptive control
000036 – AODE
000034 – Wunderlist
000030 – Learning to rank
000029 – SLIQ
000028 – Mobile Learning
000028 – Autonomous car
000027 – Operational definition
000024 – Autoencoder
000022 – Artificial Intelligence
000020 – Recurrent neural network
000018 – Probability distribution
000015 – Generalized linear model
000010 – Recommender system
000010 – Naive Bayes
000009 – Loss function
000007 – Robot learning
000007 – Multinomial logistic regression
000005 – ArXiv
000000 – The Master Algorithm
000000 – Sparse coding
000000 – Scikit-image
000000 – OpenAI
000000 – ND4J
000000 – Naive Bayes classifier
000000 – Machine ethics

Full Machine learning Report Purchase Options

Evaluation Criteria Definitions:

Interest and Popularity:

Leaders:

097 – TensorFlow
095 – Azure machine learning studio
093 – KNIME

Google Trends data for gauging mindshare and awareness, the numbers are relative to their maximum (100), meaning number 100 has maximum interest right now and number 50 has half the interest it had from its peak of 100.

Employment Demand:

Leaders:

008701 – MATLAB
005529 – Data science
004494 – Splunk

Open employment opportunities on the date of this report for the subject. Indicator of the need of employers for this specific skill set and therefore their organization’s application of it in relation to the other subjects.

Active Practitioners:

Leaders:

00603446 – Data science
00244626 – Artificial Intelligence
00083792 – learning management system

Number of practitioners and professionals which have the subject skill set and experience. Indicator of the need of employers (and availability of employees) for this specific skill set and therefore their organization’s application of it in relation to the other subjects.

Monthly Searches:

Leaders:

368000 – MATLAB
110000 – Splunk
110000 – OpenCV

How often a month a search is performed for the keyword/phrase: and indicator of interest in the subject.

Cost per Click:

Leaders:

013.80 – Apache Mahout
012.19 – Naive Bayes
008.48 – Random forest

CPC: The average amount advertisers pay Google anytime someone clicks their own ad for this keyword. Indicator of the advertising being spent on the topic ergo investments being made to attract clients.

Active Advertisers:

Leaders:

048 – learning management system
042 – Data science
039 – Mobile Learning

This shows how many unique advertisers have appeared on this subject in the last 12 months. Indicator of the advertising being spent on the topic ergo investments being made to attract clients.

Patents:

Leaders:

00107000 – Linear regression
00104000 – ANOVA
00079100 – Artificial Intelligence

Patents issued for the specific subject. Indicator of R&D investments and innovation in the specific subject.

Books in print:

Leaders:

00044957 – Artificial Intelligence
00024782 – Probability distribution
00019057 – Regression analysis

Number of in-print books that cover the subject. Indicator of the need for knowledge sharing and its availability.

Instruction and courseware available content:

Leaders:

02790000 – Data science
01750000 – Artificial Intelligence
00549000 – MATLAB

Number of instruction and courseware available content that covers the subject. Indicator of the need for knowledge sharing and its availability.

Complete Overview:

Interest and Popularity

097 – TensorFlow
095 – Azure machine learning studio
093 – KNIME
093 – Deep learning
092 – Splunk
092 – Deeplearning4j
088 – Random forest
087 – Data science
086 – Online machine learning
085 – Apache Spark
084 – Databricks
083 – R (programming language)
078 – RapidMiner
076 – SeatMe
076 – Autonomous car
076 – Autoencoder
075 – Convolutional neural network
074 – OpenCV
069 – Time complexity
067 – Restricted Boltzmann machine
066 – Wunderlist
066 – SPSS Modeler
064 – Functional programming
063 – Sentiment analysis
062 – Overfitting
062 – ND4J
060 – ArXiv
059 – Amazon Machine Learning
055 – Scikit-learn
054 – Loss function
053 – Feature engineering
051 – Mobile Learning
051 – Apache Mahout
051 – ANOVA
050 – Topic modeling
050 – Sparse coding
049 – Learning to rank
047 – Gaussian process regression
046 – Ensemble learning
044 – AODE
043 – Sift Science
043 – Generalized linear model
042 – Unsupervised learning
042 – Supervised learning
042 – Recurrent neural network
042 – Boltzmann machine
041 – Recommender system
041 – Linear regression
041 – Adaptive control
040 – The Master Algorithm
040 – Machine ethics
040 – Logistic regression
039 – Decision tree
038 – SLIQ
038 – Naive Bayes
038 – MATLAB
038 – Cognitive model
037 – ThreatMetrix
037 – Probability distribution
037 – Feature learning
036 – Regression analysis
036 – Natural language processing
036 – learning management system
035 – OpenAI
034 – Naive Bayes classifier
033 – Artificial Intelligence
033 – Anomaly detection
032 – Robot learning
031 – Scikit-image
031 – Operational definition
031 – Multinomial logistic regression

Employment Demand:

008701 – MATLAB
005529 – Data science
004494 – Splunk
003767 – learning management system
001636 – Artificial Intelligence
001418 – Natural language processing
001013 – Functional programming
000793 – Deep learning
000718 – Apache Spark
000574 – Regression analysis
000558 – Logistic regression
000377 – ANOVA
000309 – OpenCV
000298 – Linear regression
000282 – Anomaly detection
000200 – Decision tree
000192 – Scikit-learn
000152 – Sentiment analysis
000149 – TensorFlow
000130 – Mobile Learning
000107 – Supervised learning
000093 – Random forest
000088 – Unsupervised learning
000086 – Feature engineering
000079 – RapidMiner
000073 – KNIME
000072 – Topic modeling
000054 – Adaptive control
000047 – R (programming language)
000044 – Apache Mahout
000036 – Databricks
000035 – SPSS Modeler
000024 – Naive Bayes
000022 – Time complexity
000020 – ThreatMetrix
000019 – Amazon Machine Learning
000015 – Probability distribution
000013 – SeatMe
000012 – Recommender system
000011 – Ensemble learning
000010 – Online machine learning
000009 – Cognitive model
000008 – Loss function
000008 – Feature learning
000006 – Convolutional neural network
000005 – Overfitting
000005 – Learning to rank
000005 – Gaussian process regression
000005 – Autonomous car
000004 – Recurrent neural network
000004 – Operational definition
000004 – Generalized linear model
000004 – Boltzmann machine
000004 – ArXiv
000003 – Wunderlist
000002 – SLIQ
000002 – Sift Science
000002 – Restricted Boltzmann machine
000002 – Multinomial logistic regression
000002 – AODE
000001 – Robot learning
000001 – Deeplearning4j
000001 – Azure machine learning studio
000001 – Autoencoder
000000 – The Master Algorithm
000000 – Sparse coding
000000 – Scikit-image
000000 – OpenAI
000000 – ND4J
000000 – Naive Bayes classifier
000000 – Machine ethics

Active Practitioners:

00603446 – Data science
00244626 – Artificial Intelligence
00083792 – learning management system
00064365 – Natural language processing
00052645 – Splunk
00052345 – OpenCV
00041911 – Functional programming
00037925 – Apache Spark
00032939 – ANOVA
00023561 – Mobile Learning
00016855 – Deep learning
00016855 – Decision tree
00010781 – Logistic regression
00008893 – Naive Bayes
00008873 – Anomaly detection
00006891 – Scikit-learn
00006510 – RapidMiner
00006168 – Supervised learning
00005829 – SPSS Modeler
00005511 – Adaptive control
00005162 – Recommender system
00005154 – ArXiv
00005144 – Unsupervised learning
00004866 – MATLAB
00004866 – Loss function
00003708 – Random forest
00003708 – R (programming language)
00003678 – KNIME
00003255 – Time complexity
00003101 – Probability distribution
00002581 – Topic modeling
00002389 – TensorFlow
00002062 – Feature engineering
00001558 – Linear regression
00001378 – Apache Mahout
00001352 – Autonomous car
00001135 – Generalized linear model
00000940 – Databricks
00000874 – Multinomial logistic regression
00000857 – Sentiment analysis
00000857 – Regression analysis
00000857 – Recurrent neural network
00000819 – Learning to rank
00000738 – Overfitting
00000598 – Sparse coding
00000585 – Wunderlist
00000579 – Feature learning
00000528 – ThreatMetrix
00000465 – Operational definition
00000436 – Robot learning
00000332 – Online machine learning
00000323 – SeatMe
00000316 – Autoencoder
00000311 – Gaussian process regression
00000276 – Boltzmann machine
00000260 – SLIQ
00000243 – AODE
00000224 – Cognitive model
00000207 – Restricted Boltzmann machine
00000196 – Sift Science
00000183 – Amazon Machine Learning
00000167 – Convolutional neural network
00000128 – Scikit-image
00000123 – Azure machine learning studio
00000118 – The Master Algorithm
00000100 – OpenAI
00000096 – Ensemble learning
00000086 – Machine ethics
00000075 – Deeplearning4j
00000014 – ND4J
00000014 – Naive Bayes classifier

Monthly Searches:

368000 – MATLAB
110000 – Splunk
110000 – OpenCV
110000 – Artificial Intelligence
110000 – ANOVA
090500 – ArXiv
049500 – Linear regression
040500 – Regression analysis
040500 – Logistic regression
027100 – Decision tree
022200 – Probability distribution
022200 – Deep learning
018100 – Operational definition
014800 – Natural language processing
014800 – learning management system
014800 – Functional programming
014800 – Data science
012100 – Random forest
009900 – Naive Bayes
005400 – Mobile Learning
004400 – Multinomial logistic regression
004400 – Apache Mahout
003600 – Unsupervised learning
003600 – Supervised learning
003600 – Generalized linear model
002900 – SPSS Modeler
002900 – Naive Bayes classifier
002900 – Convolutional neural network
002400 – AODE
002400 – Anomaly detection
001900 – Recurrent neural network
001900 – Adaptive control
001600 – Loss function
001300 – Recommender system
000000 – Wunderlist
000000 – Topic modeling
000000 – Time complexity
000000 – ThreatMetrix
000000 – The Master Algorithm
000000 – TensorFlow
000000 – Sparse coding
000000 – SLIQ
000000 – Sift Science
000000 – Sentiment analysis
000000 – SeatMe
000000 – Scikit-learn
000000 – Scikit-image
000000 – Robot learning
000000 – Restricted Boltzmann machine
000000 – RapidMiner
000000 – R (programming language)
000000 – Overfitting
000000 – OpenAI
000000 – Online machine learning
000000 – ND4J
000000 – Machine ethics
000000 – Learning to rank
000000 – KNIME
000000 – Gaussian process regression
000000 – Feature learning
000000 – Feature engineering
000000 – Ensemble learning
000000 – Deeplearning4j
000000 – Databricks
000000 – Cognitive model
000000 – Boltzmann machine
000000 – Azure machine learning studio
000000 – Autonomous car
000000 – Autoencoder
000000 – Apache Spark
000000 – Amazon Machine Learning

Cost per Click:

013.80 – Apache Mahout
012.19 – Naive Bayes
008.48 – Random forest
007.89 – learning management system
005.45 – Supervised learning
004.99 – Unsupervised learning
004.96 – Naive Bayes classifier
004.80 – Anomaly detection
004.72 – Mobile Learning
004.34 – Operational definition
002.86 – Natural language processing
002.68 – Multinomial logistic regression
002.66 – Loss function
002.00 – Decision tree
001.78 – Functional programming
001.32 – Recurrent neural network
001.27 – Data science
001.17 – Generalized linear model
001.15 – Regression analysis
001.08 – Deep learning
001.07 – Linear regression
001.06 – Artificial Intelligence
000.91 – AODE
000.90 – Probability distribution
000.62 – MATLAB
000.52 – Splunk
000.36 – ANOVA
000.27 – SPSS Modeler
000.24 – ArXiv
000.20 – OpenCV
000.16 – Adaptive control
000.10 – Convolutional neural network
000.04 – Logistic regression
000.02 – Recommender system
000.00 – Wunderlist
000.00 – Topic modeling
000.00 – Time complexity
000.00 – ThreatMetrix
000.00 – The Master Algorithm
000.00 – TensorFlow
000.00 – Sparse coding
000.00 – SLIQ
000.00 – Sift Science
000.00 – Sentiment analysis
000.00 – SeatMe
000.00 – Scikit-learn
000.00 – Scikit-image
000.00 – Robot learning
000.00 – Restricted Boltzmann machine
000.00 – RapidMiner
000.00 – R (programming language)
000.00 – Overfitting
000.00 – OpenAI
000.00 – Online machine learning
000.00 – ND4J
000.00 – Machine ethics
000.00 – Learning to rank
000.00 – KNIME
000.00 – Gaussian process regression
000.00 – Feature learning
000.00 – Feature engineering
000.00 – Ensemble learning
000.00 – Deeplearning4j
000.00 – Databricks
000.00 – Cognitive model
000.00 – Boltzmann machine
000.00 – Azure machine learning studio
000.00 – Autonomous car
000.00 – Autoencoder
000.00 – Apache Spark
000.00 – Amazon Machine Learning

Active Advertisers

048 – learning management system
042 – Data science
039 – Mobile Learning
024 – Sentiment analysis
015 – Apache Spark
012 – Deep learning
011 – Decision tree
008 – Artificial Intelligence
007 – Anomaly detection
006 – SPSS Modeler
005 – Recommender system
003 – Splunk
003 – SeatMe
002 – Unsupervised learning
001 – Wunderlist
001 – Regression analysis
001 – Random forest
001 – Natural language processing
001 – MATLAB
001 – AODE
001 – ANOVA
000 – Topic modeling
000 – Time complexity
000 – ThreatMetrix
000 – The Master Algorithm
000 – TensorFlow
000 – Supervised learning
000 – Sparse coding
000 – SLIQ
000 – Sift Science
000 – Scikit-learn
000 – Scikit-image
000 – Robot learning
000 – Restricted Boltzmann machine
000 – Recurrent neural network
000 – RapidMiner
000 – R (programming language)
000 – Probability distribution
000 – Overfitting
000 – Operational definition
000 – OpenCV
000 – OpenAI
000 – Online machine learning
000 – ND4J
000 – Naive Bayes classifier
000 – Naive Bayes
000 – Multinomial logistic regression
000 – Machine ethics
000 – Loss function
000 – Logistic regression
000 – Linear regression
000 – Learning to rank
000 – KNIME
000 – Generalized linear model
000 – Gaussian process regression
000 – Functional programming
000 – Feature learning
000 – Feature engineering
000 – Ensemble learning
000 – Deeplearning4j
000 – Databricks
000 – Convolutional neural network
000 – Cognitive model
000 – Boltzmann machine
000 – Azure machine learning studio
000 – Autonomous car
000 – Autoencoder
000 – ArXiv
000 – Apache Mahout
000 – Amazon Machine Learning
000 – Adaptive control

Patents:

00107000 – Linear regression
00104000 – ANOVA
00079100 – Artificial Intelligence
00076300 – Regression analysis
00069600 – MATLAB
00057900 – Probability distribution
00047400 – Adaptive control
00030700 – Decision tree
00026200 – Natural language processing
00020100 – Logistic regression
00017800 – Anomaly detection
00011800 – Supervised learning
00009610 – Time complexity
00008040 – Naive Bayes
00007290 – Unsupervised learning
00004900 – ArXiv
00004700 – Overfitting
00004440 – Recommender system
00004420 – Loss function
00004020 – OpenCV
00003760 – Random forest
00003290 – Functional programming
00002960 – Sentiment analysis
00002150 – Naive Bayes classifier
00002100 – Recurrent neural network
00001980 – Operational definition
00001830 – learning management system
00001620 – Splunk
00001560 – Generalized linear model
00001510 – Learning to rank
00001280 – Sparse coding
00000881 – Deep learning
00000821 – Convolutional neural network
00000797 – Data science
00000790 – Cognitive model
00000760 – Feature learning
00000701 – Boltzmann machine
00000673 – Ensemble learning
00000573 – Mobile Learning
00000570 – Topic modeling
00000440 – Gaussian process regression
00000430 – Multinomial logistic regression
00000378 – AODE
00000322 – Robot learning
00000295 – Restricted Boltzmann machine
00000245 – R (programming language)
00000208 – Autonomous car
00000199 – SLIQ
00000189 – The Master Algorithm
00000176 – Feature engineering
00000152 – ThreatMetrix
00000151 – Machine ethics
00000130 – SPSS Modeler
00000123 – Autoencoder
00000102 – RapidMiner
00000082 – Online machine learning
00000067 – Apache Mahout
00000064 – KNIME
00000055 – Scikit-learn
00000046 – Apache Spark
00000032 – OpenAI
00000024 – SeatMe
00000003 – TensorFlow
00000002 – Wunderlist
00000001 – Scikit-image
00000001 – Deeplearning4j
00000000 – Sift Science
00000000 – ND4J
00000000 – Databricks
00000000 – Azure machine learning studio
00000000 – Amazon Machine Learning

Books in print:

00044957 – Artificial Intelligence
00024782 – Probability distribution
00019057 – Regression analysis
00014228 – Linear regression
00010736 – Decision tree
00008886 – Logistic regression
00007422 – Natural language processing
00006350 – Loss function
00005751 – MATLAB
00004041 – Operational definition
00003652 – ANOVA
00003466 – Time complexity
00002909 – Cognitive model
00002781 – Supervised learning
00002405 – Functional programming
00002390 – learning management system
00002115 – Adaptive control
00002059 – Naive Bayes
00001771 – Generalized linear model
00001607 – Anomaly detection
00001581 – Unsupervised learning
00001344 – Random forest
00001330 – Recurrent neural network
00001286 – Deep learning
00001234 – Recommender system
00001220 – Sentiment analysis
00001176 – Naive Bayes classifier
00001127 – Data science
00001034 – ArXiv
00000851 – Mobile Learning
00000651 – Multinomial logistic regression
00000510 – Boltzmann machine
00000322 – Sparse coding
00000286 – Convolutional neural network
00000237 – Ensemble learning
00000234 – R (programming language)
00000211 – Topic modeling
00000210 – OpenCV
00000191 – Restricted Boltzmann machine
00000188 – Apache Spark
00000175 – Gaussian process regression
00000159 – Overfitting
00000153 – Autonomous car
00000141 – Scikit-learn
00000138 – Feature learning
00000119 – Learning to rank
00000086 – SPSS Modeler
00000083 – Splunk
00000079 – Feature engineering
00000070 – Robot learning
00000061 – Apache Mahout
00000060 – RapidMiner
00000058 – Machine ethics
00000053 – Autoencoder
00000034 – KNIME
00000032 – AODE
00000030 – Wunderlist
00000021 – Online machine learning
00000020 – The Master Algorithm
00000014 – TensorFlow
00000013 – Scikit-image
00000012 – SLIQ
00000011 – Databricks
00000010 – OpenAI
00000007 – ND4J
00000007 – Deeplearning4j
00000007 – Azure machine learning studio
00000007 – Amazon Machine Learning
00000004 – Sift Science
00000000 – ThreatMetrix
00000000 – SeatMe

Instruction and courseware available content:

02790000 – Data science
01750000 – Artificial Intelligence
00549000 – MATLAB
00154000 – OpenCV
00122000 – Deep learning
00111000 – Linear regression
00108000 – ANOVA
00043700 – Mobile Learning
00043000 – Apache Spark
00042300 – Functional programming
00040100 – learning management system
00039400 – Autonomous car
00038000 – Regression analysis
00031800 – Probability distribution
00030500 – Decision tree
00029100 – ArXiv
00022200 – Natural language processing
00022000 – Logistic regression
00021400 – Splunk
00015200 – Wunderlist
00014900 – SLIQ
00014700 – Sentiment analysis
00010100 – RapidMiner
00009820 – Random forest
00009560 – Scikit-learn
00008330 – Robot learning
00008150 – TensorFlow
00007580 – AODE
00007550 – Anomaly detection
00007210 – R (programming language)
00006920 – Time complexity
00006650 – Adaptive control
00006460 – Naive Bayes
00005230 – Supervised learning
00004990 – SPSS Modeler
00004570 – Unsupervised learning
00004540 – Feature learning
00004400 – Learning to rank
00003680 – Databricks
00003450 – KNIME
00003260 – Overfitting
00003230 – Recurrent neural network
00002560 – Recommender system
00002440 – Convolutional neural network
00002230 – Naive Bayes classifier
00001840 – Apache Mahout
00001680 – Amazon Machine Learning
00001590 – Topic modeling
00001560 – OpenAI
00001550 – Cognitive model
00001470 – Multinomial logistic regression
00001270 – Autoencoder
00001190 – Sparse coding
00001130 – Ensemble learning
00001010 – Generalized linear model
00000859 – Loss function
00000807 – The Master Algorithm
00000779 – Operational definition
00000747 – Boltzmann machine
00000712 – Scikit-image
00000676 – Feature engineering
00000659 – Gaussian process regression
00000590 – Restricted Boltzmann machine
00000478 – Azure machine learning studio
00000312 – SeatMe
00000294 – ND4J
00000292 – Online machine learning
00000289 – ThreatMetrix
00000261 – Deeplearning4j
00000249 – Sift Science
00000204 – Machine ethics

Full Machine learning Report Purchase Options

Start with MATLAB, Regression analysis, Sentiment analysis, and make the above top Predictive Analytics results happen:

Access all the Machine learning Prescriptive Analytics MATLAB, Regression analysis, Sentiment analysis, blueprints, presentations and templates and much more in The Art of Service LAB theartofservice.co/lab. Sign up for 7 days free today.