Machine learning PREDICTIVE ANALYTICS REPORT

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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 ten 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


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