artificial intelligence influencers

Top Artificial Intelligence Influencers To Follow in 2020

Top Artificial Intelligence Influencers To Follow in 2020

Muhammad Imran

Author 

January 27, 2020

artificial intelligence influencers

Sharing AI related news has become crucial in this era of digital transformation. Given the current wave, many AI researchers have turned into AI influencers to drive value and success to their respective field. These are the people who are driving conversations about AI across social media and other platforms. Here is a list of some of the most influential ones who are actively sharing information about AI and disruptive technologies:

Please note: This is not a ranking article.

Gregory Piatetsky-Shapiro

Gregory Piatetsky is a well-known expert in Big Data, Business Analytics, Data Mining, Data Science, and Machine Learning and is among top influencers in those fields. He is the president of KDnuggets, a leading site in the field, with over 500,000 visitors per month and over 250,000 subscribers/followers. Gregory is also the founder of KDD (Knowledge Discovery and Data Mining conferences) and co-founder and past chair of ACM SIGKDD, the professional association for Knowledge Discovery and Data Mining. You can follow him on LinkedIn.

Andrew NG

A Chinese/English computer scientist and entrepreneur. He was the co-founder of Google Brain and has worked as VP & Chief Scientist at Baidu. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University. He is also considered an early pioneer in online learning for co-founding of Coursera.

Twitter: @AndrewYNg

Fei Fei Li

An Associate Professor of Computer Science at Stanford University, she publishes under the name Li Fei-Fe. She is also the director of the Stanford Artificial Intelligence Lab (SAIL) and the Stanford Vision Lab. Her fields are computer vision and cognitive neuroscience. In 2017, she co-founded AI4ALL, a nonprofit working to promote diversity and inclusion in artificial intelligence.

Twitter: @drfeifei

Martin Ford

He is a futurist and author focusing on the social and economic effects of Artificial Intelligence and robotics. Out of the two books that he has written, his recent one, Rise of the Robots:  Technology and the Threat of a Jobless Future (2015), received McKinsey Business Book of the Year Award in 2015, won £30,000 from Financial Times and also became a New York Times bestseller. His other book, The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future (2009) dealt with the effects of automation, structural unemployment and increasing inequality.

Twitter: @MFordFuture

Nathan Benaich

He is an investor, who runs the Research and Applied AI Summit in London and focuses on intelligent systems and data-driven companies. Nathan also acts as an Advisor at TwentyBN, a video understanding company. He has a PhD from the University of Cambridge. 

Twitter: @NathanBenaich

Soumith Chintala

A Researcher at Facebook AI Research, he works on deep learning, generative image models, reinforcement learning and agents for video games. Prior to this, he worked at MuseAmi, where he built mobile deep learning models for music and vision. He holds a Masters in CS from NYU and has spent time in Yann LeCun’s NYU lab building deep learning models to facilitate pedestrian detection, natural image OCR and depth-images.

Twitter: @soumithchintala

Yoshua Bengio

Yoshua Bengio is a Canadian computer scientist, known for his work on artificial neural networks and deep learning. He was one of the recipients of the 2018 ACM A.M. Turing Award for his contribution in deep learning. He is a lecturer at the Department of Computer Science and Operations Research at the Université de Montréal. And also serves as the scientific director of the Montreal Institute for Learning Algorithms (MILA).

https://mila.quebec/en/yoshua-bengio/

Geoffrey Hinton

Geoffrey Everest Hinton is a cognitive psychologist and computer scientist, known for his work on artificial neural networks. Since 2013, he has been working for Google (Google Brain) and the University of Toronto.

Twitter: @geoffreyhinton

Yann LeCun

Yann LeCun is a French computer scientist, focused in the fields of machine learning, mobile robotics, computer vision and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University. And also holds the position of Vice President, Chief AI Scientist at Facebook.

Twitter: @ylecun

Nigel Willson

For almost twenty years, Nigel has been associated with Microsoft; whereby enabling clients to write the future. Most recently as their Global Strategist with a focus on innovation and near future trends. He is a frequent speaker at events on artificial intelligence & an esteemed social media contributor on AI / ML & related topics.

Twitter: @nigewillson

Nathan Benaich

A certified mover and shaker, Nathan is the founder of London AI. He posts about applied machine learning and A.I. conferences that he either organizes or attends. What makes his account interesting is that he provides insights and re-tweets valuable A.I.-related articles.

Twitter: @NathanBenaich

Adam Coates

Operating Partner at Khosla Ventures, Adam has received his PhD from Stanford University and has served as the director of the Silicon Valley AI Lab at Baidu Research until September 2017. 

Twitter: @adampaulcoates

Ronald van Loon

Director at Advertisement, an organisation that helps data-driven companies create business value. Based in the Netherlands, he is also on the advisory board for Simplilearn – an educator in cybersecurity, project management, cloud computing, digital marketing, data science and other related technologies.

Twitter: @Ronald_vanLoon

Mike Quindazzi

A business development leader and management consultant at PwC in Los Angeles, USA. He consults on emerging technology, including blockchain, 3D printing, drones, robotics, big data, virtual reality, mobile strategies, augmented reality, internet of things, predictive analytics, fintech, cybersecurity and insurtech.

Twitter: @MikeQuindazzi

Thomas Power

Based in the UK; he serves as a board member at several companies; including blockchain infrastructure company OST, data dashboard 9Spokes, Team Blockchain and the Blockchain Industry Compliance and Regulation Association. He is based in the UK.

Twitter: @thomaspower

Larry Kim

He is chief executive officer at MobileMonkey, a messenger marketing platform that amplifies Facebook advertising. Kim is based in Boston, USA. 

Twitter: @larrykim

Note:

The above list of influencers are selected on the basis of their research work, social media influence, funding goals and achievements, and their association with startups and innovation in the AI domain.

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best books on artificial intelligence

Best Books on Artificial Intelligence 2020 – Add These Must-Read to Your Bookshelf

Best Books on Artificial Intelligence 2020 – Add These Must-Read to Your Bookshelf

Muhammad Imran

Author 

January 20, 2020

best books on artificial intelligence

Artificial intelligence is now being considered as the future of economic and technological development. The topic of research and best books on Artificial Intelligence involves machine learning solutions, natural language, problem-solving, planning, and reasoning. Robotics, Automation and sophisticated programs and software seek their future in Artificial Intelligence.

AI is now ruling the world and most corporations are now using AI for a great variety of use cases and applications. These applications include fraud detection, predictive analysis, improving customer experience, process automation, etc.

In the coming years, AI programmers and engineers will witness a boost in career opportunities because AI offers more accuracy and efficiency without any human intervention. If you are someone who does not know anything about artificial intelligence but are interested in learning about AI and beginning your career in it, here are some must-read best books on artificial intelligence that you must read in order to gain a good amount of knowledge.

Best Books on Artificial Intelligence in 2020 – List

1) Artificial Intelligence by Example – Denis Rothman

Artificial Intelligence by Example - Denis Rothman

This is what you can call a starting point for developing a clear understanding of how Artificial intelligence takes help with scenarios of real life in order to work. One of the best ai books. This book will help you to learn the most advanced models of machine learning, application of AI to IoT and blockchain, development of emotional quotient in chatbots by making use of neural networks. You will understand the basics of artificial intelligence and will work through a variety of case studies that will contribute to the development of your business vision. This book will provide aid for building your adaptive thinking for solving AI cases in real-life. If you have some statistical knowledge and experience with Python, you can easily understand what this book holds for you.

2) Artificial Intelligence and Machine Learning – Chandra S.S.V

Artificial Intelligence and Machine Learning - Chandra S.S.V

This book is basically for the students who are postgraduates or undergraduates of engineering and computer science. This books will help you to bridge the gap between the complex context of machine learning and artificial intelligence. It has a large number of case studies along with easy to understand worked out examples. Apart from machine learning and artificial learning, this book offers knowledge about several types of learning like supervised, unsupervised, statistical, and reinforced learning. The book has algorithms that are well-explained and also pseudocodes for every topic making this book essential for students who are looking for the best artificial intelligence books.

3) Machine Learning – Tom M. Mitchell

Machine Learning - Tom M. Mitchell

This book has a lot about machine learning (a study of algorithms that enables programs of the computer to improve automatically through experience). This book is mainly intended for students who are undergraduates of the upper level and also for courses of beginner level graduates of machine learning.

4) Artificial Intelligence Basics: A Non-Technical Introduction – Tom Taulli

Artificial Intelligence Basics - A Non-Technical Introduction - Tom Taulli

This book will help you grasp the basic concepts of Artificial Intelligence and what impact does it have. It gives a non-technical and easy introduction to concepts like Machine Learning, Robotics, Natural Language Processing, Deep Learning, and many more. The author further discusses the impacts of artificial intelligence on the future including aspects like ethics, organizational structure, governments, social trends, and daily life.

5) A First Course in Artificial Intelligence – Deepak Khemani

A First Course in Artificial Intelligence - Deepak Khemani

This book is exceptional as compared to other ai books. It explains basic strategies and required problem-solving that is mainly on the intelligence part. This book has a great feature that is the introductory course it has to offer on artificial intelligence and an excellent knowledge-based approach.

6) Applied Artificial Intelligence: A Handbook for Business Leaders – Mariya Yao, Adelyn Zhou, Marlene Jia

Applied Artificial Intelligence A Handbook for Business Leaders - Mariya Yao, Adelyn Zhou, Marlene Jia

This book serves as a guide for business makers who are interested in leveraging the intelligence of a machine to maximize their organization’s productivity and improving the living standards in their communities. One of the main focuses of this book is on driving concrete decisions for business by using machine language and artificial intelligence applications. This will be a great read for those people who want to adopt the technology of machine learning in order to get a value.

7) Artificial Intelligence in Healthcare – Mahajan MD, Parag Suresh

Artificial Intelligence in Healthcare - Mahajan MD, Parag Suresh

Explore the role of artificial intelligence in the field of healthcare with the help of this book. Learn how AI is greatly revolutionizing all the basic aspects of the healthcare industry and get a guide on the present state and future applications of artificial intelligence in healthcare (also the underdeveloped ones). This is one of the best books on artificial intelligence. It gives a deep understanding of ethical concerns regarding the use of AI in the industry of healthcare, principles of artificial intelligence, how it is working, an important role in all medical specialties, and the role of corporates and start-ups in artificial intelligence in healthcare.

8) Life 3.0 Being Human in the Age of Artificial Intelligence – Max Tegmark

Life 3.0 Being Human in the Age of Artificial Intelligence - Max Tegmark

This book will help you understand what the future with artificial intelligence will look like and what impacts it will have on human existence. The author discusses most-asked questions including how to adopt technology without making people jobless, and how to know that the future world with AI will work perfectly without getting hacked or malfunctioning. He also discusses how our lives can be flourished with AI without giving all powers to machines that are lethal and autonomous.

9) The AI Advantage: How to Put the Artificial Intelligence Revolution to Work – Thomas H. Davenport

The AI Advantage How to Put the Artificial Intelligence Revolution to Work - Thomas H. Davenport

Thomas provides a guide on how to use artificial intelligence in business. He is open about the technologies that are available and how organizations can make use of these technologies in order to get a competitive advantage and maximized benefits from their businesses. He clearly explained how business can adopt artificial intelligence and put it to work.

10) Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp – Peter Norvig

Paradigms of Artificial Intelligence Programming Case Studies in Common Lisp - Peter Norvig

This book will teach you advanced Common Lisp techniques in terms of creating major artificial intelligence systems. It reconstructs complex, authentic programs of artificial intelligence by making use of Common Lisp, debugs and builds strong practical programs along with a demonstration of key concepts of AI and superior programming style. It’s one of the most popular artificial intelligence books and it is very useful for basic AI courses and proves to be a great source of reference for a professional programmer.

11) Python: Beginner’s Guide to Artificial Intelligence – Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Mathew Lamons

Python Beginner’s Guide to Artificial Intelligence - Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Mathew Lamons

This book provides you knowledge of real-world contextualization by using learning problems regarding application and research. It helps you to understand how you can implement and design machine intelligence by making use of AI-based real-life examples. The author has given loads of knowledge about data analysis, deep learning, machine learning, Python, and also basic knowledge about artificial intelligence.

12) Python: Advanced Guide to Artificial Intelligence – Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

Python Advanced Guide to Artificial Intelligence - Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

This is a guide that you can use to learn algorithms for machine learning. You will about the extraction of features from your dataset and by using libraries that are Python-based how to perform dimensionality reduction. You will also learn Tensorflow’s advanced features and implementation of various techniques relating to object detection, image sensation, object classification, and more. This book will help you to have a deep knowledge of Tensorflow.

13) Machine Learning – The New AI – Ethem Alpaydin

Machine Learning – The New AI - Ethem Alpaydin

It is machine learning’s precise overview that underlies various applications including face recognition, driverless cars, and recommendation systems. The author has used simple and easy language for a general reader to understand evolution. He has explained learning algorithms that are important and presented many applications as examples.

14) Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies (The MIT Press) – John D. Kelleher, Brian Mac Namee

Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies (The MIT Press) - John D. Kelleher, Brian Mac Namee

This is one of the best books on ai. It offers a useful introduction to the most important approaches to machine learning. Mathematical and technical material is proved with worked-out examples and this book contains case studies that illustrate how these models can be applied in businesses.

15) Machine Learning for Beginners – Chris Sebastian

Machine Learning for Beginners - Chris Sebastian

The books describe the development and evolution of machine learning from the beginning of computer learning to machines becoming smarter than human experts. It describes the importance of data and how a large amount of data helps ML programmers to gain information that they require for the development of learning algorithms. This book gives an in-depth view of the relation between machine learning and artificial intelligence.

16) The Hundred-Page Machine Learning Book – Andriy Burkov

The Hundred-Page Machine Learning Book - Andriy Burkov

It is one of the best books on artificial intelligence by Andriy Burkov. This book has been regarded as the best book by many experts. It offers a clear and simple introduction to the basics of machine learning. And it has a lot of practical recommendations as the author has great experience in the artificial intelligence field.

You will know about all important approaches to machine learning ranging from logistic regression and classical linear to vector machines, modern support, random forests, and deep learning.

Artificial Intelligence Books FAQs:

Who invented artificial intelligence according to the books?

The history of AI has great stories with many myths and rumors included. The existence of modern AI is because of classical philosophers who first explained the thinking process of humans as mechanical manipulation of symbols.

Is artificial intelligence is related to deep learning?

AI is a concept that is broader than it appears by the name. It encompasses everything from traditional and old-fashioned AI to modern and advanced technologies i.e. deep learning.

Is artificial intelligence related to robotics? Programming and neural networks?

To overcome symbolic AI’s certain limitations, symbolic methodologies including fuzzy systems, evolutionary computation, neural network, and other models for computation began to get popularity.  And it led to the introduction of the term ‘computational intelligence’ that emerged as a daughter-field of artificial intelligence.

To overcome symbolic AI’s certain limitations, symbolic methodologies including fuzzy systems, evolutionary computation, neural network, and other models for computation began to get popularity.  And it led to the introduction of the term ‘computational intelligence’ that emerged as a daughter-field of artificial intelligence.

Conclusion

Making a career in Artificial Intelligence is possible in various spheres including healthcare, education, public undertakings, arts, private organizations, military, government services etc. AI’s extent is advancing with each passing day. Those who have the skills and abilities to make significant human conclusions by translating those digital bits of data will be able to enjoy a successful career in the coming years. There are loads of best books on artificial intelligence that you read and improve your understanding of AI.

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Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

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best machine learning software

29 Best Machine Learning Software And Tools in 2020

29 Best Machine Learning Software and Tools in 2020

Muhammad Imran

Author 

January 14, 2020

best machine learning software

Machine learning systems have the ability to automatically learn and improve from experience without having to explicitly modify the program. Machine learning focuses on the development of computer programs that are capable of accessing data and using it to learn for themselves. Machine learning technologies in the recent past have become quite powerful and integration of best machine learning software into our daily lives have deepened over the these years. Our dependency on this technology is likely to increase, thereby benefiting the community at large. We at Folio3 have dedicated a considerable amount of resources to this technology and have delivered numerous end-to-end projects related to machine learning solution.

15 Best Machine Learning Software in 2020

1) Amazon Machine Learning (AML) AML is a cloud-based and one of the best machine learning tools that developers with varying levels of skill levels can use. It is a managed service that is used to create machine learning models and generate predictions. Moreover, it has the capability to integrate data from multiple sources such as Redshift, Amazon S3, or RDS. Features:

  • It offers wizards & visualization tools.
  • Supports three types of models, i.e., multi-class classification, binary classification and regression.
  • Allows users to use MySQL database and Amazon Redshift for data source object creation.  

Pros:

  • It can be used for ML models, Data sources, Real-time predictions, Evaluations, and Batch predictions.

Tool Cost/Plan Details: Free Download link: https://aws.amazon.com/free/machine-learning/ 2) Oryx 2 A realization of the lambda architecture and built on Apache Kafka and Apache Spark; Oryx 2 is widely used for real-time large-scale machine learning. This framework enables users to build end-to-end applications for regression, filtering, classification and clustering. Features:

  • It consists of three tiers: specialization on top providing ML abstractions, generic lambda architecture tier, end-to-end implementation of the same standard ML algorithms.
  • Oryx 2 is the upgraded version of Oryx 1 project.
  • It has three side-by-side cooperating layers, such as: speed layer, serving layer and batch layer.
  • There is also a data transport layer that shifts data between the layers and serves as a recipient of external data.

Tool Cost/Plan Details: Free Download Link: https://jar-download.com/artifacts/com.cloudera.oryx/oryx-ml/2.7.2 3) Apple`s Core ML Core ML is a data science software tool by Apple. It is a straightforward model that helps users integrate machine learning models into their mobile apps. The simple process requires users to drop the machine learning model file into their project and the Xcode automatically builds a Swift wrapper class or Objective-C. Features:

  • Domain-specific frameworks and functionality can be based on it.
  • Core ML easily supports Computer Vision for precise image analysis, GameplayKit for learned decision trees evaluation and Natural Language for natural language processing.
  • It is optimized for on-device performance.
  • It builds on top of low-level primitives.

Tool Cost/Plan Details: Free Download link: https://developer.apple.com/documentation/coreml 4) Scikit-learn Scikit-learn is for machine learning development in python; offers a library for the Python programming language. Features:

  • It facilitates data mining and data analysis processes.
  • It offers models and algorithms for Classification, Dimensional reduction, Regression, Clustering, Model selection and Pre-processing.

Pros:

  • Provides comprehensible documentation. 
  • When calling objects, parameters for specific algorithms can be changed.

Tool Cost/Plan Details: Free Download link: https://scikit-learn.org/stable/install.html 5) Pytorch PyTorch is a Python machine learning library based on Torch, which is basically a Lua based scripting language, computing framework and machine learning library. Features:

  • It helps build neural networks through Autograd Module.
  • It offers a number of optimization algorithms for building neural networks.
  • It  can be used on cloud platforms.
  • PyTorch has distributed training, many tools and libraries.

Pros:

  • Users can easily create computational graphs.
  • Hybrid front-end makes it easy to use.

Tool Cost/Plan Details: Free Download link: https://pytorch.org/ 6) TensorFlow TensorFlow offers a JavaScript library which facilitates machine learning. Available APIs help you build and train the models. Features:

  • It helps in preparing and building your models. 
  • You can run your current models with the assistance of TensorFlow.js (a model converter). 
  • It helps in the neural network.

Pros:

  • It can be used in two ways, i.e. by script tags or by installing through NPM.
  • It can even facilitate human pose estimation.

Cons:

  • It is difficult to learn.

Tool Cost/Plan Details: Free Download link: https://www.tensorflow.org/install 7) Weka Weka enables machine learning algorithms that help in data mining. Features: It assists in Data preparation, Classification, Regression, Clustering, Visualization and Association rules mining. Pros:

  • Provides online courses for training.
  • Easy to understand algorithms.
  • It is good for students as well.

Cons:

  • Not much documentation and online support are available.

Tool Cost/Plan Details: Free Download link: https://sourceforge.net/projects/weka/ 8) KNIME KNIME is a tool for big data solution, reporting and integration platform. Based on the data pipelining concept, it combines different elements for machine learning and data mining. Features:

  • It can integrate the code of programming languages such as JavaScriptC, Java, C++, R, Python, etc.
  • It can be utilized for business intelligence, CRM and financial data analysis.

Pros:

  • It serves as a good SAS alternative.
  • It is easy to deploy, install and learn.

Cons:

  • It is not easy to build complicated models.
  • Offers limited exporting and visualization capabilities.

Tool Cost/Plan Details:  Annual Subscription  (based on 5 users and 8 cores) KNIME Server for Azure 25.000 EUR 29,000 USD KNIME Server for AWS 45.500 EUR 52,000 USD Download link: https://www.knime.com/downloads 9) COLAB Google Colab is a cloud service that supports Python. It utilizes PyTorch, Keras, TensorFlow, and OpenCV libraries in order to help you build machine learning applications. Features: It assists in machine learning education and research. Pros: You can access it via your google drive. Tool Cost/Plan Details: Free 10) APACHE Mahout Apache Mahout assists mathematicians, statisticians and data scientists in building and implementing their own algorithms. Features:

  • It offers algorithms for Pre-processors, Regression, Clustering, Recommenders and Distributed Linear Algebra. 
  • Java libraries are incorporated for regular math operations. 
  • It follows Distributed linear algebra framework.

Pros: It works for large data sets. Is simple and extensible.  Cons:

  • Limited documentation and algorithms.

Tool Cost/Plan Details: Free Download link: https://mahout.apache.org/general/downloads 11) Accord.Net Accord.Net offers Machine Learning libraries for ai image and audio processing. Features:

  • It provides algorithms for Numerical Linear Algebra, Numerical optimization, Statistics and Artificial Neural networks.
  • Plus, for image, audio & signal processing.
  • It also provides support for graph plotting and visualization libraries.

Pros: Libraries are made available from the source code and as well as executable installer & NuGet package manager. Cons:

  • It supports only Net supported languages.

Tool Cost/Plan Details: Free Download link: http://accord-framework.net/ 12) Shogun Shogun provides numerous algorithms and data frameworks for machine learning. These machine learning libraries can be used for research and education. Features:

  • It has support vector machines that can be used for regression and classification.
  • It assists in implementing Hidden Markov models.
  • It offers support for various languages including – Python, Scala, Ruby, Java, Octave, R and Lua.

Pros:

  • It is easy to use and can process large data-sets which have been used for edge analytics services.
  • It offers good customer support, features and functionalities. 

Tool Cost/Plan Details: Free Download link: https://www.shogun-toolbox.org/install 13) Keras.io Keras, written in Python is an API for neural networks that assists in carrying out quick research. Features:

  • It can be utilized for easy and fast prototyping.
  • It supports convolution networks and a combination of two networks.
  • It assists recurrent networks.
  • It can be run on the CPU and GPU.

Pros:

  • It is extremely user-friendly
  • It is both modular and extensible

Cons:

  • In order to use Keras, you must need TensorFlow, Theano, or CNTK.

Tool Cost/Plan Details: Free Download link: https://keras.io/ 14) Rapid Miner Rapid Miner is a platform for machine learning, deep learning, text mining, data preparation and predictive analytics. It is mostly used for research, education and application development. Features:

  • Through GUI, it helps in structuring and implementing systematic analytical workflows.
  • It assists with data preparation. 
  • Result visualization. 
  • Model approval and optimization.

Pros:

  • Extensible through plugins.
  • Simple to use.
  • Limited programming skills required.

Cons: Rapid Miner is costly. Tool Cost/Plan Details:

  • Free plan
  • Small: $2500 per year.
  • Medium: $5000 per year.
  • Large: $10000 per year.

Download link: https://rapidminer.com/get-started/

Best Machine Learning Framework/Technology in 2020

15) Tensorflow Framework TensorFlow is an open-source software library for data-based programming across tasks. This data science software tool is based on computational graphs which is basically a system of codes. Every nod represents a numerical activity that runs some basic or complex function. This framework is one of the best Machine Learning software, as it supports regressions, classifications and neural networks such as complicated tasks and algorithms. 16) FireBase ML Kit Firebase is another prominent machine learning framework that enables highly accurate and pre-trained deep models with minimal code. The framework offers models both on the Google cloud and locally. 17) CAFFE (Convolutional Architecture for Fast Feature Embedding) CAFFE framework provides the quickest solution to applying deep neural networks. It is the best Machine Learning framework known for its pre-trained model-Zoo, which is capable of performing a plethora of tasks including image classification, recommender system and machine vision. 18) Apache Spark Framework A cluster-computing framework, the Apache Spark machine learning is written in different languages like Java, Scala, Python and R. Spark’s Machine Learning library, MLlib has aided Spark’s success. Building MLlib on top of Spark enables it to tackle distinct needs of a single tool, as opposed to many disjointed ones. 19) Scikit-Learn Framework One of the best tools of Python community, Scikit-learn framework can efficiently handle data mining and support numerous practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework offers supervised, unsupervised learning algorithms and cross-validation. The Scikit is mostly written in Python with some core algorithms in Cython for enhanced performance.

Best Open Source Machine Learning Tools in 2020

20) Uber Ludwig – Open Source Machine Learning Tool for Non-Programmers It is a toolbox built on top of TensorFlow that allows users to train and test deep learning models without having to write any code. It enables users to build complex models that they can tweak before implementing it into code, with minimal input. 21) MLFlow –  Open Source Machine Learning Tool for Model Deployment It can seamlessly work with any machine learning library or algorithm, enabling it to manage the entire lifecycle, including experimentation, reproducibility and deployment of machine learning models. One of the best machine learning tools, MLFlow is currently in alpha and its three components are: projects, tracking and models. 22) Hadoop – Open Source Machine Learning Tool for Big Data Hadoop project is a prominent and relevant tool for working with Big Data. It is a framework that enables distributed processing of large datasets across clusters of computers using simple programming models. It has the capability to scale up from a single server to thousands of machines, each offering local computation and storage. 23) SimpleCV – Open Source Machine Learning Tool for Computer Vision SimpleCV enables access to several high-powered computer vision libraries like OpenCV – making computer vision relatively easy. This can be done without having to learn about bit depths, file formats, color spaces, buffer management, eigenvalues, or matrix versus bitmap storage. 24) Reinforcement Learning – Open Source Tool for Reinforcement Learning RL is a popular phenomenon in Machine Learning and its goal is to train smart agents that can automatically interact with their environment and solve complex tasks. Real-world applications of this technology include robotics and self-driving cars, amongst others.

Best Machine Learning Software Alternatives

25) ServiceNow Platform Its intelligent engine is combined with Machine Learning to create contextual workflows and automate business processes. This helps reduce costs and speed time-to-resolution.  26) Qubole It delivers optimized responses of Big Data Analytics built on Amazon, Microsoft and Google Clouds. 27) Weka Weka contains tools for data pre-processing, classification, regression, clustering, association rules and visualization. It is also be utilized for developing new Machine Learning schemes. 28) IBM Watson Machine Learning It allows you to create, train and deploy machine learning models using your own data; enabling you to grow intelligent business applications.  29) BigML It is easier to set-up and enables users to enjoy the benefits of Programmatic Machine Learning.

Best Machine Learning Software FAQs:

Is Tensorflow framework used for Machine Learning only?

A creation of Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. It can be used for a wide range of Machine Learning and deep learning applications. It combines machine learning and deep learning (aka neural networking) models/algorithms, making them useful by way of a common metaphor.

How to build a simple stock prediction software using Machine Learning?

Basic machine learning models on stock market data to predict future trends can be built using a single attribute i.e stock price to analyze the trend of stock. Or it can be achieved by simply opting for ARIMA, which is one such statistical model for time-series and atm cash forecasting that stands for Autoregressive Integrated Moving Average. And assumes that time-series information is data points measured at constant time intervals, such as hourly, weekly so and so forth.

Best way to incorporate Machine Learning to your App software?

Data forms the basis and the more data is provided to the algorithm and simpler the model, the better the accuracy of predictions. Hence, it is best to avoid subsampling. The success of the project depends on choosing the most appropriate Machine Learning method and right parameters. Proper data collection and understanding data features also impact learning processes and predictability. Additionally, you need to consider your business model and product capacities. Also keep in mind that algorithms need to be tested, which could increase the time and cost. 

How much does a Machine Learning software engineer make?

Machine learning software engineers are in high demand, which reflects in their salary and benefits packages. It is without a doubt one of the best jobs out there, outpacing many other technology jobs. Average machine learning salary, according to Indeed’s research, is approximately $146,085 (which has increased by 344% since 2015). An entry-level machine learning engineer with 0-4 years of experience would on an average make approximately $97,090. This can go up to $130,000 if there is profit-sharing or bonuses involved.  Mid-level machine learning engineers with 5-9 years of experience command an average salary of $112,095. The number can rise to $160,000 or more, depending on bonuses and profit-sharing. Senior machine learning engineers with over a decade of experience are the industry’s unicorns and command the best remuneration packages in the field. The are likely to make an average salary of $132,500, surpassing $181,000 annually with bonuses and profit-sharing.

Why You Need Machine Learning Software For Every Industry

Machine learning software is used to automate various company processes, across industries to boost productivity, such as enabling customer interactions to be carried out without human input. These machines are algorithms designed to process large amounts of information and make logical decisions. The machines are therefore programmed to learn and complete tasks without requiring any further programming. 1) Transportation Industry We are all aware of how unsmart traffic lights are, but with Machine Learning as a service and Artificial intelligence algorithms they can efficiently predict, monitor and manage the traffic. Best machine learning tools are also being utilized by car manufacturers like Tesla to introduce self-driving cars that can regulate speed, change lanes and park – without human assistance.  2) Healthcare Industry From radiology to diagnostics purposes, intelligent softwares are being employed to not only predict the likelihood of a disease occurring but also to suggest the best possible way to prevent/cure the disease. These are faster in detecting and scanning information, enabling them to produce results much more quickly than humans. 3) Finance Industry Machine Learning applications such as Robo-Advisors are already being used in the finance industry to simplify the investment process and as a cheaper alternative to hiring a human financial advisor. They employ Machine Learning algorithms to automate financial guidance to manage portfolios. Moreover, trading platforms such as High-Frequency Trading (HFT) is being employed by investment banks, pension funds and mutual funds, whereby allowing them to benefit from minute price differences that surface for a fraction of a second.  4) Agriculture Industry We are all aware of approaches that predict crop yields based on historical data and multi-parametric approaches that help optimize productivity; but now algorithms are being utilized to understand the crop quality, identify diseases and detect weeds. Machine Learning is also being utilized to  study soil moisture and temperature of fields to understand the dynamics of ecosystems and obstacles. Machine Learning based apps are successfully enabling farmers make better use of irrigation by providing accurate estimates of evapotranspiration. 5) Education Industry Machine Learning is being used by learning platforms like Udemy, Teachable and WizIQ to provide personalized academic lesson recommendations; similar to how YouTube does it. It is also helping educators become more efficient by automating tasks such as classroom management and scheduling.

Start Gowing with Folio3 AI Today.

We are the Pioneers in the Cognitive Arena – Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@folio3.ai

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best r machine learning packages

21 Best R Machine Learning Packages to Look Out for in 2020

21 Best R Machine Learning Packages in 2020 – The Ultimate Guide

Muhammad Imran

Author 

January 6, 2020

best r machine learning packages

Artificial intelligence is an emerging technology. It is impacting the interactive activities of users through the internet. It possesses the ability to change the way that humans interact, not only with the digital and electronic world but also with each other. It is basically a form of technology with human-like intelligence that can learn, perceive, plan, and process languages. There are two categories of AI, which include “narrow AI” or “general AI.” Narrow AI is domain-specific that we interact with today. By domain-specific, we mean, for instance, language translation. General AI is theoretical and not domain-specific. Just to remain in the context, we would focus only on narrow AI. It is a machine language – a field of computer science for the development of new algorithms and models. Conversely, we can say machine language is an application of Artificial Intelligence. The list of machine languages is vast, with several different categories. Here we are going to discuss R – a programming language in detail. The language falls in the category of Array languages. So, what is R? R comprises many properties. It is a machine language that provides an open-source environment for statistical programming and visualizing. There are many R machine learning packages, which we will be discussing in detail. You can find seamlessly designed AI solutions such as R learning packages in the market by machine learning solution companies such as Folio3.

21 Best R Machine Learning Packages in 2020

The most commonly asked question by potential data scientists is, “What is the best programming language for Machine Learning as a service?” The answer to this question always ends up in a debate about whether to choose R, Python, or MATLAB for Machine Learning. Choosing the programming language for machine learning depends on the requirements of a data problem, the preferences of the data scientist. According to the Kaggle survey, open-source R is the preeminent choice among data specialists who want to understand and explore data by using statistical methods and graphs. There are many R machine learning packages and their advanced implementations for the top machine learning algorithms. Every data specialist must be familiar about them to explore, model, and create a prototype of the given data. Since R is an open-source language, so people can approach it from anywhere in the world. From data collection to reproducible research, you can find a Black Box written by someone else that you can use in your program directly. The Black Box is nothing but a Package in R, which is a collection of pre-written reusable codes. Here are the few basic R learning machine packages:

1) CARAT

The CARAT package denotes as Classification and Regression Training. The purpose of this package is to integrate training and model prediction. It allows data specialists to run several different algorithms for a given problem. It also facilitates in investigating the ideal parameters for an algorithm with measured experiments.

best r machine learning packages is carat

CARAT Features And USP: The grid search method of this package explores parameters with the help of a combination of various methods to evaluate the performance of a given model. After having a look at all the trial combinations, the grid search method discovers the combination that gives the best results. CARAT package is among the best machine learning packages in R. After installing the CART package; a developer can run names (getModelInfo()) to see that 217 possible methods require a single package to operate or run. To build any predictive model, CARAT uses train() function; The syntax of train function is train(formula, data, method). CARAT Documentation Download: CARET package is not only for building models, but it also takes care of splitting your data into test and train, alteration, etc.

2) Random Forest

The concept behind its name is to “combine multiple trees to build your forest.” A Random Forest algorithm is the most widely used algorithm in Machine Learning. Its application includes the creation of a large number of decision trees, and then each observation is entered into the decision tree. The common output attained for a maximum of the observations is measured as the final output. randomforest r machine learning package

In other words, it takes random samples. Observations are arranged into the decision tree. While using the randomForest algorithm, data specialists have to confirm that the variables must be numeric or factors. Factors cannot have more than 32 levels when applying randomForest. This package allows for solving regression and classification tasks. Training missing values and outliers is one of its many applications. The syntax of this function is: randomForest(formula=, data=)

3) E1071

The name of this package seems a junk value, but this is not the case. It is a very significant package in the R machine learning package. It has very specified functions for implementing Naïve Bayes (conditional probability), SVM, Fourier Transforms, Bagged Clustering, Fuzzy Clustering, etc. E1071 R package implemented the first R interface for SVM. E1071 Documentation Download: The easy way to understand its concept is, let’s suppose if a data specialist is trying to find out what is the probability that a person who buys an iPhone 8 also buys an iPhone 8 Case. It is a type of investigation which depends on conditional probability, so data scientists use an e1071 R package that has specialized functions for implementing the Naive Bayes Classifier. e1071 r machine learning package

Data scientists use Support Vector Machines (SVM) when they have a dataset that is impossible to separate in the given dimensions, and there is a need to promote that data to higher dimensions to classify or regress it. The syntax for SVM is: SVM(Species ~Sepal.Length + Sepal.Width, data=iris)

4) RPart

Rpart stands for recursive partitioning and regression training. Classification and regression are the two primary purposes of this package. It involves the two-stage procedure. The output model is then represented in the form of binary trees. The general way to plot any function using the Rpart package is to call plot() function. The results might not be appealing by just applying the basic plot() function, so there is a substitute that is the PRP() function. It is an influential and flexible function in rpart.plot package. It is frequently denoted as the authentic Swiss army knife for plotting regression trees. RPart r machine learning package

rpart() function helps to create a relationship between dependent and independent variables so that a business can understand the difference in the dependent variables based on the independent variables. The syntax is: rpart(formula, data=, method=,control=) This formula implies as follows: Data implies the name of the dataset. The method implies the objective. Control implies the system requirement.

5) KernLab

KernLab is a package for SVM, kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, and a spectral clustering algorithm. KernLab’s most common use is for SVM implementations. It comes in use when it is difficult to solve clustering, classification, and regression problems.  It has several kernel functions that include tanhdot (hyperbolic tangent kernel Function), polydot (polynomial kernel function), laplacedot (laplacian kernel function) and many more to perform pattern recognition problems. KernLab has its predefined kernels, but the user has the flexibility to create and use their kernel functions.

6) Nnet

Data scientists use this package when there is a need to use an artificial neural network (ANN) as it is based on an understanding of how a human brain functions. It is one of the widely used and easy to implement a package of neural networks, but it is restricted to a single layer of nodes. According to several studies, more nodes are not required because they do not contribute to improving the performance of the model but rather increase the calculation time and complexity of the model. This package does not offer any particular set of methods for finding the number of nodes in the hidden layer. Thus, when data specialists apply nnet, it is most likely to recommend that they arrange it in such a way that a value falls between the number of input and output nodes. nnet r machine learning package

The syntax for this package is: nnet(formula, data, size) To view the documentation of its function, go to this link: https://www.rdocumentation.org/packages/nnet/versions/7.3-12/topics/nnet

7) DPLYR

Dpylr is one of the most popular packages in the field of data science. It provides feasible, fast, and stable functions for data handling. This package contains some set of verbs as functions like mutate(), select(), filter(), and arrange(). The following code is used to install this package: install.packages(“dplyr”) To load this package following syntax is used: library(dplyr)

8) GGPlot2

Ggplot2 is another package for data science. It is the most sophisticated and artistic graphic framework among R packages. The syntax for the installation of this data science package is  install.packages(“ggplot2”)

9) Word Cloud

Word cloud, as the name indicates, it consists of thousands of words in a single image. Conversely, we can say that it is a visualization of text data. One great example is speech to text software. It is one of the best machine learning packages in R that creates a representation of words. Data specialists can customize the Worldcloud according to his choice. Like he can place the words randomly in his desired position, or he can place the same kind of words together, etc.  machine learning packages python In the R machine learning package, two types of libraries are there to make Worldcloud that are Worldcloud and Worldcloud2. Here we will present the syntax of Worldcloud2. The installation syntax for Worldcloud2 is:

  1. require(devtools)
  2. install_github(“chiffon/wordcloud2”)

Or you can write it directly as: a library(wordcloud2).

10) Tidyr

It is another popular R package for data science. The function of the tidyr package for data is science is to tidy up the data. It is done by placing variables in the column, observation in the row, and the value in the cell. This package is used to define the standard way of organizing data.  For installation syntax, use this code: install.packages(“tidyr”) For loading the package, use this code: library(tidyr)

11) Shiny

Shiny is an R package, the use of which stretches to web application frameworks for data science. It provides an effortless solution for building up web applications. There are two options, the developer can install the software on every client system, or he can host a webpage. In addition, the developer can create dashboards or can implant them in R Markdown documents. Furthermore, Shiny apps can be used with various scripting languages like HTML widgets, CSS themes, and JavaScript actions. Conversely, this package is considered as a combination of the computational power of R with the interactivity of the modern web.

12) Tm

tm is a machine learning package of R that gives a framework for solving text mining tasks. Text mining is an evolving application of natural language processing these days.  Text mining application involves sentiment analysis or news classification. There are several jobs for a developer that he has to perform in this package, such as eliminating unwanted and unrelated words, eliminating punctuation signs and ending words, etc. This package has many adaptable functions to provide ease. Some of them are as follows:

  • removeNumbers(): to remove Numbers from the given text document.
  • weightTfIdf(): for term Frequency and reverse document frequency.
  • tm_reduce(): to combine transformations.
  • removePunctuation() to remove punctuation signs from the given text document.

13) MICE Package

MICE package refers to Multivariate Imputation via Chained Sequences. One of the many uses of this package includes imputing the missing values. It is a common problem that developers usually face. Generally, when a developer faces a problem of missing values, he applies basic imputations like replacing with 0, mean or mode, etc. These solutions are not flexible and may result in possible data inconsistency. Therefore MICE package facilitates developers to impute missing values with the help of multiple techniques according to the type of given data. ai packages in r This package consists of various functions like inspecting missing data patterns, diagnosis of the quality of the imputed value, analyses completed dataset, store and export imputed data in different structures, etc.  For the package documentation, click on this link https://www.rdocumentation.org/packages/mice/versions/3.6.0/topics/mice

14) iGraph

igraph is one of the top machine learning R packages for data science used for network analysis. It is a combination of powerful, professional, accessible, and portable network analysis tools. Moreover, it is an open-source and free package and can be programmed on Python, C/C++, and Mathematica. This package consists of numerous functions that help to produce random and regular graphs, visualization of a graph, etc in computer vision. By using this R package, developers can work on their huge graphs.  There are some specific requirements to use this package for Linux. It needs a C and a C++ compiler. r packages for data science The installation syntax for this package is: install.packages(“igraph”) The loading syntax for this package is: library(igraph)

15) ROCR

This data science R package helps in visualizing the performance of scoring classifiers. This package is flexible and feasible. It requires only three commands and default values for optional parameters. This package facilitates in developing cutoff-parameterized 2D performance curves. This package comprises of various functions such as:  prediction() – used to create prediction objects performance() – used to create performance objects, etc. For package documentation, view this link https://www.rdocumentation.org/packages/ROCR/versions/1.0-7

16) Data Explorer

The DataExplorer package is among the widely used and top machine learning R package for data science. This package serves in the field of exploratory data analysis (EDA), which is one of the predictive analytics tasks. As the name implies, in this package, the data analyst has to be more attentive in data analysis. It is not easy to handle data manually. For this purpose, the DataExplorer package provides automation for data exploration. This package helps in scanning and analyzing every variable to visualize them. It is beneficial for a huge dataset. The data analysis can easily obtain the hidden knowledge of data. You can use this mentioned code to install the package from CPAN: install.packages(“DataExplorer”) To load this package, the following code is needed: library(DataExplorer)

17) MLR

The mlr package of machine learning is one of the most amazing packages. It provides encryption to various machine learning tasks. It can perform several tasks with the help of only a single package rather than to use three packages for three different tasks. mlr package provides coherence for various classification and regression techniques. The techniques involve machine-readable parameter descriptions, clustering, generic re-sampling, filtering, and feature extraction, etc. It can also perform parallel operations. For installation, this code is used: to install.packages(“mlr”) The code for loading this package is; library(mlr)

18) Arules

The arules package arules refers to Mining association rules and Frequent Itemsets. It is also a widely used R machine learning package. This package helps in performing several operations that include representation and transaction analysis of data and patterns and data manipulation. It also provides the C implementations of Apriori and Eclat association mining algorithms.

19) Mboost

mboost is another R machine learning package for data science. It is a package that depends majorly on model boosting. It has an operative gradient descent algorithm for enhancing general risk functions. It uses regression trees or component-wise least squares estimates. Also, it provides an interaction model to potentially high-dimensional data

20) Party

The applications of Party package in R machine learning extends to recursive partitioning. This package imitates the continuous development of collective methods. Party is another package like a randomForest package to make a decision tree, which is based on the Conditional Inference algorithm. The main function of this package is ctree() that reduces the training time and bias. The syntax code for ctree() is: ctree(formula,data)

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Folio3 Is Your Best Custom Machine Learning As a Service Partner

Folio3 is a leading software company, providing customized solutions in machine learning. With the prolonged experience of decades, Folio3 has helped many partner companies from various industries. It has the expertise of AI and machine learning in these following processes.

  • Product Conceptualization
  • Predictive Engineering and maintenance analysis
  • Design and Automation
  • Data Acquisition and analytics
  • Product planning and commissioning
  • Utilizing operational data to improve processes

Folio3 can also alter and customize the development process according to your requirements. Their professional and talented team follows the predefined and standard process based on best practices :

  • Submission of hypothesis
  • Scope and feasibility of the project defined Delivery of a Proof of Concept
  • Algorithm development with regular touchpoints Final delivery and live deployment

Folio3 is capable of understanding customer needs and hence able to create machine learning solutions accordingly. Artificial Intelligence and machine learning help to develop great projects, but it is also important to provide the best user experience. So, Folio3 supports regular interaction with customers and related stakeholders. Folio3 has delivered its efficient services in many industries. Here we are mentioning nine among them and their related use cases:

1) Banking Sector – ATM Cash Forecasting 

Folio3 has provided its assistance to the multinational and large commercial banks of Pakistan. It served them with the service of deep learning for ATM cash Forecasting. It helps banks in avoiding both out of cash and overstock situations at their ATMs across the world. It also provides automated analysis of previous transactions for predicting the required amount of money for individual ATMs.

2) Health Sector – Breast Cancer HER2 Subtype Identification

Folio3 provided the solution of computer vision for Breast Cancer HER2 subtype identification to one of the most popular universities of Pakistan, Dow University of Health Sciences. The solution provides an automated pipeline for cell segmentation and spot counting from a Computer Vision-based diagnostic-aid for the Fluorescent In-Situ Hybridization test. For this reason, Folio3 developed a computer-supported assistance system that allowed specialists to perform the test efficiently and accurately and also allows them to digitize and store the images for future practice.

3) Transportation Sector – Road traffic Analysis 

It was a propriety service of deep learning by Folio3 for the transportation sector. They built an AI-powered Road & Safety solution, which allowed the analysis of road and traffic situations by making use of advanced deep learning. It can precisely distinguish between various types of vehicles and perform a total count.

4) Trading Sector  – Completion Time Estimation 

Folio3 has provided its services to the largest bookseller company of the United States in the area of machine learning. The solution has enabled the customer’s Digital Marketing Team to considerably improve the effective delivery through accurate schedules and outcomes of marketing campaigns while meeting the weekly related deadlines.

5) Medical Diagnosis – Thalassemia Identification

Folio3 again served the Dow University of Health Sciences for Thalassemia Identification, a project of image analysis and ai image processing. The solution provides automated analysis of gel electrophoresis images to predict thalassemia and test for mutant gene expression with fine-granularity medical image analysis.

6) Food Sector – Automated Authentication for Drive-Thrus 

Folio3 served the product development company of California named “Dashcode.” They created the automated authentication for a drive-thru, which is a project of deep learning.  The solution offers multi-modal automated authentication for drive-thrus through fine-grained car’s make/model classification and person identification using deep learning.

7) Speech to Text App – Converse Smartly

It was a proprietary application by Folio3. It allows you to make your conversations smart, intelligent, and productive with the use of machine learning, artificial intelligence, and Natural Language Processing (NLP).

8) Technology Sector – Facial Recognition system 

It was also a propriety product built by Folio3. This solution offers a highly accurate facial recognition system that provides real-time results based on Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN). 

9) IT sector – Customer Churn Prediction 

Folio 3 has served as the leading tech company of Pakistan with the facility of customer churn prediction, a predictive modeling solution. The Customer Churn Prediction offers a data-driven insight for businesses, helping them to recognize potentially unsatisfied and inactive customers in real-time with business process validation. For detailed solutions and services, Check these urls: Terrain Mapping Livestock Management Amazon Transcribe Service Google Speech to Text Service IBM Watson Consulting Service Azure Machine Learning Service Big Data Solution Robotic Process Automation Solution Edge Analytics Services Fraud Detection Solution

Top R Machine Learning Packages Conclusion:

All R machine learning packages are the eminent choice based on their features and functions, and every package best fits according to the given data requirements. There are some default values related to every package in R. Prior to the implementation of an algorithm, a data specialist or a developer must know about its numerous options available. By entering default values will give some outcomes, but that outcome is not likely to be accurate or optimized. In other words, by using the definite functions of R, one can develop an efficient machine learning or data science model. Hence, the R machine learning package is an amazing open-source RStudio tool, providing everyone to avail of the opportunity to use.  If you find our blog useful and informative, please share it with your friends and family. If you have any further suggestions or queries, please leave a comment in our comment section. FAQs: Is r used for machine learning and iot as well? Yes, R can be used for both because R itself a machine language applicable in machine learning for data science tasks. As we know that Artificial Intelligence is associated with IoT, and we refer narrow AI to machine learning that includes R. Hence there is a strong relation of R machine learning in IoT applications. Advantages of r packages in machine learning? There are several advantages of R packages in machine learning. Some of them include Open Source – This means everyone has the chance to avail of these packages without the need for license and registration process. Ideal support for data scattering – It allows scattered data to transform in a structured and organized form. Variety of packages – there is a variety of packages for different data issues. Provides quality plotting and graphing – The packages like ggplot2 provides visual tools for plotting and graphing the data. Highly compatible – It is highly compatible and can be combined with many programming languages like C, C++, Python, and Java. Platform independent – It is independent of any platform that means it is cross-platform machine language that can be compatible with Windows, Linux, Mac. Machine learning operations – It supports many machine learning operations like classification, regression, and development of artificial neural networks. Constantly growing – It is progressing continuously with the addition of new features that provide developers to work efficiently without any delay. Which packages in r provides machine learning functionality for beginners? Here is the categorized list of R packages that are useful for beginners:
To manipulate data:

  • dplyr
  •  tidyr

To visualize data:

  • ggplot2

To model data:

  • CARAT

 To report results:

  • Shiny

Many other R machine learning packages fall in these categories; here, we mentioned the most feasible and popular packages useful for beginners.

Start Gowing with Folio3 AI Today.

We are the Pioneers in the Cognitive Arena – Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@folio3.ai

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