Dublin, Feb. 04, 2020 (GLOBE NEWSWIRE) -- The "Reinforcement Learning: An Introduction to the Technology" report has been added to ResearchAndMarkets.com's offering.

These days, machine learning (ML), which is a subset of computer science, is one of the most rapidly growing fields in the technology world. It is considered to be a core field for implementing artificial intelligence (AI) and data science.

The adoption of data-intensive machine learning methods like reinforcement learning is playing a major role in decision-making across various industries such as healthcare, education, manufacturing, policing, financial modeling and marketing. The growing demand for more complex machine working is driving the demand for learning-based methods in the ML field. Reinforcement learning also presents a unique opportunity to address the dynamic behavior of systems.

This study was conducted in order to understand the current state of reinforcement learning and track its adoption along various verticals, and it seeks to put forth ways to fully exploit the benefits of this technology. This study will serve as a guide and benchmark for technology vendors, manufacturers of the hardware that supports AI, as well as the end-users who will finally use this technology. Decisionmakers will find the information useful in developing business strategies and in identifying areas for research and development.

The report includes:

Key Topics Covered

Chapter 1 Reinforcement Learning

Chapter 2 Bibliography

List of TablesTable 1: Reinforcement Learning vs. Supervised Learning vs. Unsupervised LearningTable 2: Global Machine Learning Market, by Region, Through 2024

List of FiguresFigure 1: Reinforcement Learning ProcessFigure 2: Reinforcement Learning WorkflowFigure 3: Artificial Intelligence vs. Machine Learning vs. Reinforcement LearningFigure 4: Machine Learning ApplicationsFigure 5: Types of Machine LearningFigure 6: Reinforcement Learning Market DynamicsFigure 7: Global Machine Learning Market, by Region, 2018-2024

For more information about this report visit https://www.researchandmarkets.com/r/g0ad2f

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Reinforcement Learning (RL) Market Report & Framework, 2020: An Introduction to the Technology - Yahoo Finance

One of the biggest challenges for developers of natural language systems is accounting for the many and varied ways people express themselves. There is a reason many technology companies would rather we all spoke in simple terms, it makes humans easier to understand and narrows down the chances of machines getting it wrong.

But its hardly the engaging conversational experience that people expect of AI.

Language has evolved over many centuries. As various nations colonised and traded with other nations so our language whatever your native tongue is changed. And thanks to radio, TV, and the internet its continuing to expand every day.

Among the hundreds of new words added to the Merriam Webster dictionary in 2019 was Vacay: a shortening of vacation; Haircut: a new sense was added meaning a reduction in the value of an asset; and Dad joke: a corny pun normally told by fathers.

In a conversation, we as humans would probably be able to deduce what someone meant, even if wed never heard a word or expression before. Machines? Not so much. Or at least, not if they are reliant solely on machine learning for their natural language understanding.

While adding domain specialism such as a product name or industry terminology to an application overcomes a machine recognising some specific words, understanding all of the general everyday phrases people use in between those words is where the real challenge lies.

Most commercial natural language development tools today dont offer the intelligent, humanlike, experience that customers expect in automated conversations. One of the reasons is because they rely on pattern matching words using machine learning.

Although humans - at a basic level - pattern match words too, our brains add a much higher level of reasoning to allow us to do a better job of interpreting what the person meant by considering the words used, their order, synonyms and more, plus understanding when words such as book is being used as a verb or a noun. One might say we add our own more flexible form of linguistic modelling.

As humans, we can zoom in on the vocabulary that is relevant to the current discussion. So, when someone asks a question using a phrasing weve not heard before, we can extrapolate from what we do know, to understand what is meant. Even if weve never heard a particular word before, we can guess with a high degree of accuracy what it means.

But when it comes to machines, most statisticians will tell you that accuracy isnt a great metric. Its too easily skewed by the data its based on. Instead of accuracy, they use precision and recall. In simple terms precision is about quality. It marks the number of times you were actually correct with your prediction. Recall is about quantity, the number of times you predicted correctly out of all of the possibilities.

The vast majority of conversational AI development tools available today rely purely on machine learning. However, machine learning isnt great at precision, not without massive amounts of data on which to build its model. The end result is that the developer has to code in each and every way someone might ask a question. Not a task for the faint hearted when you consider there are at least 22 ways to say yes in the English language.

Some development tools rely on linguistic modelling, which is great at precision, because it understands sentence constructs and the common ways a particular type of question is phrased, but often doesnt stack up to machine learnings recall ability. This is because linguistic modelling is based on binary rules. They either match or they dont, which means inputs with minor deviations such as word ordering or spelling mistakes will be missed.

Machine learning on the other hand provides a probability on how much the input matches with the training data for a particular intent class and is therefore less sensitive to minor variations. Used alone, neither system is conducive to delivering a highly engaging conversation.

However, by taking a hybrid approach to conversational AI development, enterprises can benefit from the best of both worlds. Rules increase the precision of understanding, while machine learning delivers greater recall by recovering the data missed by the rules.

Not only does this significantly speed up the development process, it also allows for the application to deal with examples it has never seen before. In addition, it reduces the number of customers sent to a safety net such as a live chat agent, merely because theyve phrased their question slightly differently.

By enabling the conversational AI development platform to decide where each model is used, the performance of the conversational system can be optimised even further. Making it easier for the developer to build robust applications by automatically mixing and matching the underlying technology to achieve the best results, while allowing technology to more easily understand humans no matter what words we choose to use.

Andy Peart, CMSO, Artificial Solutions

Link:
How to handle the unexpected in conversational AI - ITProPortal

Dear Reader,

As of the beginning of January 2020, its my great pleasure to join The Machine Learning Times as editor in chief! Ive taken over the main editorial duties from Eric Siegel, who founded the ML Times (also the founder of the Predictive Analytics World conference series). As youve likely noticed, weve renamed to The Machine Learning Times what until recently was The Predictive Analytics Times. In addition to a new, shiny name, this rebranding corresponds with new efforts to expand and intensify our breadth of coverage. As editor in chief, Im taking the lead in this growth initiative. Were growing the MLTimes both quantitatively and qualitatively more articles, more writers, and more topics. One particular area of focus will be to increase our coverage of deep learning.

And speaking of deep learning, please consider joining me at this summers Deep Learning World 2020 May 31 June 4 in Las Vegas the co-located sister conference of Predictive Analytics World and part of Machine Learning Week. For the third year, I am chairing and moderating a broad ranging lineup of the latest industry use cases and applications in deep learning. This year, DLW features a new track on large scale deep learning deployment. You can view the full agenda here. In the coming months, the MLTimes will be featuring interviews with the speakers giving you sneak peeks into the upcoming conference presentations.

In addition to supporting the community in these two roles with the MLTimes and Deep Learning World, I am a fellow analytics practitioner yes, I practice what I preach! To learn more about my work leading and executing on advanced data science projects for high tech firms and major research universities in Silicon Valley, click here.

And finally, Attention All Writers: Whether youve published with us in the past or are considering publishing for the very first time, wed love to see original content submissions from you. Published articles gain strong exposure on our site, as well as within the monthly MLTimes email send. If you currently publish elsewhere, such as on a personal blog, consider publishing items as an article with us first, and then in your own blog two weeks thereafter (per our editorial guidelines). Doing so would provide you the opportunity to gain our readers eyes in addition to those you already reach.

Im excited to lead the MLTimes into a strong year. Weve already got a good start with greater amounts of exciting original content lined up for this and coming months. Please feel free to reach out to me with any feedback on our published content or if you are interested in submitting articles for consideration. For general inquiries, see the information on our editorial page and the contact information there. And to reach out to me directly, connect with me on LinkedIn.

Thanks for reading!

Best Regards,

Luba GloukhovaEditor in Chief, The Machine Learning TimesFounding Chair, Deep Learning World

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The ML Times Is Growing A Letter from the New Editor in Chief - Machine Learning Times - machine learning & data science news - The Predictive...

GadgetsNow & Agencies | Feb 1, 2020, 05:28 PM IST

1 / 16Made in India' phone, artificial intelligence, machine learning and all that Budget 2020 has for the technology sector

The Union Budget 2020-21 talks about several policies to leverage technology to boost economic growth in India going ahead. Finance minister Nirmala Sitharaman proposed steps to allow private players to build data centre parks along with several measures to help startups. Heres everything about tech that FM announced in Budget 2020.

2 / 16New scheme to make mobile phones, electronics in India

The government will introduce a new scheme to encourage domestic manufacturing of mobile phones, electronic equipment and semiconductor packaging in order to make India a part of the global manufacturing chain and boost employment opportunities.

3 / 16New policy to enable private sector to build Data Centre parks throughout the country

4 / 16Fibre to the Home (FTTH) connections through Bharatnet to link 100,000 Gram Panchayats by 2020

5 / 16Finance minister Nirmala Sitharaman has proposed Rs 6,000 crore for Bharatnet programme in 2020-21

6 / 16For startups, a digital platform to be promoted to facilitate seamless application and capture of IPRs

7 / 16Knowledge Translation Clusters to be set up across different technology sectors including new and emerging areas to help startups

8 / 16This budget has announced steps to help startups build manufacturing facilities

For designing, fabrication and validation of proof of concept, and further scaling up Technology Clusters, harbouring test beds and small scale manufacturing facilities to be established.

9 / 16Two new national level Science Schemes to be initiated to create a comprehensive database to map Indias genetic landscape

10 / 16Early life funding proposed, including a seed fund to support ideation and development of early stage startups

11 / 16Budget 2020 proposes Rs 8,000 crore over five years for National Mission on Quantum Technologies and Applications

12 / 16NABARD to map and geo-tag agri-warehouses, cold storages, reefer van facilities, etc.

13 / 16Targeting diseases with an appropriately designed preventive regime using Machine Learning and AI

14 / 16Up to 1-year internship to fresh engineers to be provided by Urban Local Bodies.

15 / 16150 higher educational institutions to start apprenticeship embedded degree/diploma courses by March 2021

16 / 16Financing on Negotiable Warehousing Receipts (e-NWR) to be integrated with e-NAM.

The Budget 2020 proposes financing on negotiable warehousing receipts (e-NWR) to be integrated with e-NAM. This is likely to help facilitate seamless trading of agricultural products across the country and help realize the full potential of the national agriculture market (eNAM).

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Made in India' phone, artificial intelligence, machine learning and all that Budget 2020 has for the technology sector - Gadgets Now

Introduction To Machine Learning:

Undoubtedly, Machine Learning is the most in-demand technology in todays market. Its applications range from self-driving cars to predicting deadly diseases such as ALS. The high demand for Machine Learning skills is the motivation behind this blog. In this blog on Introduction To Machine Learning, you will understand all the basic concepts of Machine Learning and a Practical Implementation of Machine Learning by using the R language.

To get in-depth knowledge on Data Science, you can enroll for liveData Science Certification Trainingby Edureka with 24/7 support and lifetime access.

The following topics are covered in this Introduction To Machine Learning blog:

Ever since the technical revolution, weve been generating an immeasurable amount of data. As per research, we generate around 2.5 quintillion bytes of data every single day! It is estimated that by 2020, 1.7MB of data will be created every second for every person on earth.

With the availability of so much data, it is finally possible to build predictive models that can study and analyze complex data to find useful insights and deliver more accurate results.

Top Tier companies such as Netflix and Amazon build such Machine Learning models by using tons of data in order to identify profitable opportunities and avoid unwanted risks.

Heres a list of reasons why Machine Learning is so important:

Importance Of Machine Learning Introduction To Machine Learning Edureka

To give you a better understanding of how important Machine Learning is, lets list down a couple of Machine Learning Applications:

These were a few examples of how Machine Learning is implemented in Top Tier companies. Heres a blog on the Top 10 Applications of Machine Learning, do give it a read to learn more.

Now that you know why Machine Learning is so important, lets look at what exactly Machine Learning is.

The term Machine Learning was first coined by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms of technological advancements.

If you browse through the net about what is Machine Learning, youll get at least 100 different definitions. However, the very first formal definition was given by Tom M. Mitchell:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

In simple terms, Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed to do so. In the sense, it is the practice of getting Machines to solve problems by gaining the ability to think.

But wait, can a machine think or make decisions? Well, if you feed a machine a good amount of data, it will learn how to interpret, process and analyze this data by using Machine Learning Algorithms, in order to solve real-world problems.

Before moving any further, lets discuss some of the most commonly used terminologies in Machine Learning.

Algorithm: A Machine Learning algorithm is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it. It is the logic behind a Machine Learning model. An example of a Machine Learning algorithm is the Linear Regression algorithm.

Model: A model is the main component of Machine Learning. A model is trained by using a Machine Learning Algorithm. An algorithm maps all the decisions that a model is supposed to take based on the given input, in order to get the correct output.

Predictor Variable: It is a feature(s) of the data that can be used to predict the output.

Response Variable: It is the feature or the output variable that needs to be predicted by using the predictor variable(s).

Training Data: The Machine Learning model is built using the training data. The training data helps the model to identify key trends and patterns essential to predict the output.

Testing Data: After the model is trained, it must be tested to evaluate how accurately it can predict an outcome. This is done by the testing data set.

What Is Machine Learning? Introduction To Machine Learning Edureka

To sum it up, take a look at the above figure. A Machine Learning process begins by feeding the machine lots of data, by using this data the machine is trained to detect hidden insights and trends. These insights are then used to build a Machine Learning Model by using an algorithm in order to solve a problem.

The next topic in this Introduction to Machine Learning blog is the Machine Learning Process.

The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement. To understand the Machine Learning process lets assume that you have been given a problem that needs to be solved by using Machine Learning.

Machine Learning Process Introduction To Machine Learning Edureka

The problem is to predict the occurrence of rain in your local area by using Machine Learning.

The below steps are followed in a Machine Learning process:

Step 1: Define the objective of the Problem Statement

At this step, we must understand what exactly needs to be predicted. In our case, the objective is to predict the possibility of rain by studying weather conditions. At this stage, it is also essential to take mental notes on what kind of data can be used to solve this problem or the type of approach you must follow to get to the solution.

Step 2: Data Gathering

At this stage, you must be asking questions such as,

Once you know the types of data that is required, you must understand how you can derive this data. Data collection can be done manually or by web scraping. However, if youre a beginner and youre just looking to learn Machine Learning you dont have to worry about getting the data. There are 1000s of data resources on the web, you can just download the data set and get going.

Coming back to the problem at hand, the data needed for weather forecasting includes measures such as humidity level, temperature, pressure, locality, whether or not you live in a hill station, etc. Such data must be collected and stored for analysis.

Step 3: Data Preparation

The data you collected is almost never in the right format. You will encounter a lot of inconsistencies in the data set such as missing values, redundant variables, duplicate values, etc. Removing such inconsistencies is very essential because they might lead to wrongful computations and predictions. Therefore, at this stage, you scan the data set for any inconsistencies and you fix them then and there.

Step 4: Exploratory Data Analysis

Grab your detective glasses because this stage is all about diving deep into data and finding all the hidden data mysteries. EDA or Exploratory Data Analysis is the brainstorming stage of Machine Learning. Data Exploration involves understanding the patterns and trends in the data. At this stage, all the useful insights are drawn and correlations between the variables are understood.

For example, in the case of predicting rainfall, we know that there is a strong possibility of rain if the temperature has fallen low. Such correlations must be understood and mapped at this stage.

Step 5: Building a Machine Learning Model

All the insights and patterns derived during Data Exploration are used to build the Machine Learning Model. This stage always begins by splitting the data set into two parts, training data, and testing data. The training data will be used to build and analyze the model. The logic of the model is based on the Machine Learning Algorithm that is being implemented.

In the case of predicting rainfall, since the output will be in the form of True (if it will rain tomorrow) or False (no rain tomorrow), we can use a Classification Algorithm such as Logistic Regression.

Choosing the right algorithm depends on the type of problem youre trying to solve, the data set and the level of complexity of the problem. In the upcoming sections, we will discuss the different types of problems that can be solved by using Machine Learning.

Step 6: Model Evaluation & Optimization

After building a model by using the training data set, it is finally time to put the model to a test. The testing data set is used to check the efficiency of the model and how accurately it can predict the outcome. Once the accuracy is calculated, any further improvements in the model can be implemented at this stage. Methods like parameter tuning and cross-validation can be used to improve the performance of the model.

Step 7: Predictions

Once the model is evaluated and improved, it is finally used to make predictions. The final output can be a Categorical variable (eg. True or False) or it can be a Continuous Quantity (eg. the predicted value of a stock).

In our case, for predicting the occurrence of rainfall, the output will be a categorical variable.

So that was the entire Machine Learning process. Now its time to learn about the different ways in which Machines can learn.

A machine can learn to solve a problem by following any one of the following three approaches. These are the ways in which a machine can learn:

Supervised learning is a technique in which we teach or train the machine using data which is well labeled.

To understand Supervised Learning lets consider an analogy. As kids we all needed guidance to solve math problems. Our teachers helped us understand what addition is and how it is done. Similarly, you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing but the training data set.

Supervised Learning Introduction To Machine Learning Edureka

Consider the above figure. Here were feeding the machine images of Tom and Jerry and the goal is for the machine to identify and classify the images into two groups (Tom images and Jerry images). The training data set that is fed to the model is labeled, as in, were telling the machine, this is how Tom looks and this is Jerry. By doing so youre training the machine by using labeled data. In Supervised Learning, there is a well-defined training phase done with the help of labeled data.

Unsupervised learning involves training by using unlabeled data and allowing the model to act on that information without guidance.

Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that this image is Tom and this is Jerry, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.

Unsupervised Learning Introduction To Machine Learning Edureka

For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2. Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.

Reinforcement Learning is a part of Machine learning where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions.

Panic? Yes, of course, initially we all would. But as time passes by, you will learn how to live on the island. You will explore the environment, understand the climate condition, the type of food that grows there, the dangers of the island, etc. This is exactly how Reinforcement Learning works, it involves an Agent (you, stuck on the island) that is put in an unknown environment (island), where he must learn by observing and performing actions that result in rewards.

Reinforcement Learning is mainly used in advanced Machine Learning areas such as self-driving cars, AplhaGo, etc.

To better understand the difference between Supervised, Unsupervised and Reinforcement Learning, you can go through this short video.

So that sums up the types of Machine Learning. Now, lets look at the type of problems that are solved by using Machine Learning.

Type of Problems Solved Using Machine Learning Introduction To Machine Learning Edureka

Consider the above figure, there are three main types of problems that can be solved in Machine Learning:

Heres a table that sums up the difference between Regression, Classification, and Clustering.

Regression vs Classification vs Clustering Introduction To Machine Learning Edureka

Now to make things interesting, I will leave a couple of problem statements below and your homework is to guess what type of problem (Regression, Classification or Clustering) it is:

Dont forget to leave your answer in the comment section.

Now that you have a good idea about what Machine Learning is and the processes involved in it, lets execute a demo that will help you understand how Machine Learning really works.

A short disclaimer: Ill be using the R language to show how Machine Learning works. R is a Statistical programming language mainly used for Data Science and Machine Learning. To learn more about R, you can go through the following blogs:

Now, lets get started.

Problem Statement: To study the Seattle Weather Forecast Data set and build a Machine Learning model that can predict the possibility of rain.

Data Set Description: The data set was gathered by researching and observing the weather conditions at the Seattle-Tacoma International Airport. The dataset contains the following variables:

The target or the response variable, in this case, is RAIN. If you notice, this variable is categorical in nature, i.e. its value is of two categories, either True or False. Therefore, this is a classification problem and we will be using a classification algorithm called Logistic Regression.

Even though the name suggests that it is a Regression algorithm, it actually isnt. It belongs to the GLM (Generalised Linear Model) family and thus the name Logistic Regression.

Follow this, Comprehensive Guide To Logistic Regression In R blog to learn more about Logistic Regression.

Logic: To build a Logistic Regression model in order to predict whether or not it will rain on a particular day based on the weather conditions.

Now that you know the objective of this demo, lets get our brains working and start coding.

Step 1: Install and load libraries

R provides 1000s of packages to run Machine Learning algorithms and mathematical models. So the first step is to install and load all the relevant libraries.

Each of these libraries serves a specific purpose, you can read more about the libraries in the official R Documentation.

Step 2: Import the Data set

Lucky for me I found the data set online and so I dont have to manually collect it. In the below code snippet, Ive loaded the data set into a variable called data.df by using the read.csv() function provided by R. This function is to load a Comma Separated Version (CSV) file.

Step 3: Studying the Data Set

Lets take a look at a couple of observations in the data set. To do this we can use the head() function provided by R. This will list down the first 6 observations in the data set.

Now, lets look at the structure if the data set by using the str() function.

In the above code, you can see that the data type for the DATE and RAIN variable is not correctly formatted. The DATE variable must be of type Date and the RAIN variable must be a factor.

Step 4: Data Cleaning

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