#Learning from Data machine_learning

[jobrxiv]

Postdoc / PhD position: Causal inference from single cell data Heidelberg University PhD and Postdoc opportunity to develop statistical + machine learning methods to identify causal relationships from single cell data See the full job description on jobRxiv: https://jobrxiv.org/job/heidelberg-university-27778-postdoc-phd-position-causal-inference-from-single-cell-data/?feed_id=68774 #machine_learning #single_cell_biology #statistics #ScienceJobs #hiring #research

[explainprogrammerhumor]

Skipping Some Steps

The joke is "newbie" or beginner programmers tend to overestimate their abilities. The steps in the picture represent the usual order in which programmers learn things, with the newbie programmer trying to skip all the basics to jump into something advanced. Let's break down the steps:

A "hello world" program is just about the simplest thing you can code that actually does something: it has the computer spit out the words "hello world" onto the screen. (You can use any words you want but "hello world" is traditional.) If you see those words, you know your code is working. If you don't, it isn't. The fact that it usually only take a couple lines of code makes "hello world" a great piece of starter code for new programmers, as well as experienced programmers learning a new piece of technology or starting a new project.

OOP is Object-Oriented Programming. Many programming languages let you bundle data and code into objects to help you keep related things organized. For example, a "User" object might contain data like a username, password, and e-mail address, and code that lets you log in and change your password. The learning curve for OOP goes from pretty flat to really steep. It's kind of like using electricity: you can't get far in life without knowing how to change the batteries in a flashlight or knowing that you shouldn't stick a fork in a wall socket, but everything beyond that, like knowing how to connect wires and measure voltage, can feel pretty advanced.

Understanding data structures is understanding the different ways programming languages tell the computer to handle and organize data. For example, it makes sense that when you sign up for a Facebook account, Facebook writes your name in a computer somewhere. But how does Facebook handle lists of names, like your account's "friends"? How does it know which names are your friends and which names are other people's friends?

An algorithm is a list of instructions to take in some data and spit out some other data. For example, subtracting someone's age from the current year to get the year they were born is an algorithm: regardless of how old someone is, if you follow those steps you'll always get the year they were born. When you hear "algorithm" you probably think of some fancy equation to forecast the weather or help Google search the web, but they can also be simple.

Different programmers might learn OOP, data, and algorithms in different orders. Each of them goes from being pretty straightforward to super complicated. You don't need to know everything about one before going to another. But you definitely need to know a good chunk about all of them before going to the last one:

'AI' and 'ML' refer to artificial intelligence and machine learning. They're different but have a lot of overlap. They also have kind-of "fuzzy" definitions. I'd say AI is the ability of a machine to make a decision without having instructions telling it exactly how to make the decision. ML is the ability of a machine to recognize patterns in data without having instructions telling it exactly how to recognize the patterns. Machine learning can be used to increase a computer's artificial intelligence.

A good number of people start learning code because they have an idea for a video game, an AI application, or something else shiny and trendy. It's tempting to skip the basics and go straight into the "interesting" stuff, but it very quickly becomes obvious that won't work.

[ssiddique]

Machine learning Ebooks and frameworks for Beginners and experts

Machine learning Ebooks and frameworks for Beginners and experts #machinelearning #python #machine_learning #pythonframework #ai #languageprocessing #language_processing #analytics

Machine learning is the part of Data Science dedicated to solving forecasting problems or finding structures in data. Often, when people talk about Data Science or Artificial Intelligence, they mean Machine Learning. In this article, I have collected books, courses and resources that will help beginners and experts to learn machine learning from scratch. Free E-Books for Machine…

[innrpin]

Machine Learning (ML) is a rapidly evolving field. Every field of work is drawn, it simply encapsulates a whole complex body of working. Naturally we all get intimidated or flustered by its canvas of complexity. But that complex body is made of smaller building blocks. We just need to know the blocks.

The science of working that goes behind the scene for teaching computer machine how to learn from data and make decisions or predictions. This process of machine learning strategically sits at the cross-section of science of statistics and computer science.

Much of the art in data science lies in learning the fundamental concepts in Machine Learning…

Data is transforming everything we do. We are so deeply immersed in an complex environment that can best be understood by taking data-driven decisions. Much like any other field, it is the fundamental concepts that counts before we can appreciate the applications.

Once we start counting the mathematics of conceptual understanding we are off to a great start…we then just need to keep pressing the accelerator and build on the momentum that we gain from a head start.

#machine_learning,#ML,#AI,#statistics,#computing,#data,#decisions,#data_science,#concepts,#50_Key_Concepts_ML,#50ML_Concepts

[aboutmachinelearning-blog]

How to Become a Machine Learning Expert

Machine learning is an artificial intelligence application that provides the system with the ability to learn automatically and learn from the experience without being given a lineup program.  Machine learning mainly deals with the development of computer programs that can access information and use it to learn by themselves.  Machine learning, in this case, will help you gain skills that you will need to use in the present time and also in the future.  You need to know that machine learning is an area where learning will never stop.  Whenever you start machine learning you need to know that you will be required to go for the next path for you to become an expert in machine learning. Giving up is not the option because you will encounter challenges on the way ,therefore you need some methods of coping up reach the finish line. This article gives critical tips to consider if you want to become a machine learning expert, check it out. 

Always understand the basics of machine learning before considering to enroll in one.  Spend some time studying the general knowledge about the area of science and machine learning.  For you to become an expert in machine learning you need to get it's concept deeply in that you can teach someone else and learn through you.

Learning statistics is another factor that should be considered.  What you are supposed to know here is understand some concepts and know when they should be applied.  Understand the concept of machine learning process so as to prevent wastage of time.  Creating a list of reference to help you understand each topic will be ideal for you.

Creating unsupervised learning models should be another component that should be keenly looked at.  In this stage, you will be able to understand yourself by using the skills you have gained. After realizing that you can learn by yourself then,  you are now in  a position to teach someone without straining. This models will be super useful especially when it comes to doing machine learning freelance jobs. 

Another thing is to understand the data technologies.  Many of the learning machines in the market today are outdated.  But the reason as to why they are useful today is because you can have to see a large amount of data.  You should also understand that any machine learning expert should know how to deal with big data systems, despite their area of specialization within the industry.

Understanding how to undertake a complete data project is another aspect that should be looked at.  Showcasing what you have been doing will be ideal for you since other people will get to agree with your idea. The internet is asking useful because you can showcase your learning experience and people can get to learn from there. To learn more info about this topic, view here: https://en.wikipedia.org/wiki/Machine_learning.

[motherofchinchillas-blog]

I like the concept of Algebra because I’ve always been fairly good at it and figuring it out. It studies the structure of material things, their quantity, and the relation between them. “By substituting concrete numbers with symbols, it generalizes arithmetic,” according to [3]. It was perfected by Muhammad ibn , a Persian who published his work in Al-Jabr (820 AD). It’s essential because it brings together the facts of physical existence, and the theoretical idea of something. It relates to computer science in many ways, Boolean logic uses algebra for evaluating code paths, error correcting codes, processor optimization, and more.

PageRank is a good example of algebra in computer science, “PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.”[5] It uses an algorithm to assign numerical rankings to websites,” which is found through the use of hyperlinks. Pages that are linked to other pages with high ranks receives a relatively high rank. In 9 Algorithms That Changed the Future, John MacCormick details an example of this, “There are two pages that link to my home page; these pages have no incoming links themselves, so they get scores of 1. My home page gets the total score of all its incoming links, which adds up to 2. Alice Water’s home page has 100 incoming links that each have a score of 1, so she gets a score of 100. Ernie’s recipe has only 1 incoming link, but it is from a page with a score of 2, so by adding up all the numbers (in this case there is only one number to add), Ernie gets a score of 2. Bert’s recipe also has only 1 incoming link, valued at 100, so Bert’s final score is 100. And because 100 is greater than 2, Bert’s page gets ranked above Ernie’s.” [1, p. 28] The below illustration helps to further this explanation.

I’ve always found the concept of AI and machine learning interesting, and I like learning more about the topic. It’s fascinating to know and learn more about the fact that these computers and machines already have a specific set of data, however based on its purpose and interactions with an environment, it will learn more about it and/or learn what actions to take in response to that stimulus. Computers already have specific algorithms in them that allow them to know/absorb facts about said stimulus, but also have the ability to learn from them as well. For example, like the video we saw in class about the AI learning how to play the Super Mario game. At first it started out with learning the controls and the layout of the map, it was all more trial-and-error, learning where to go and where not to, (I guess that’s everyone when we play games). After learning more about different and specific stimuli (their behavior, anatomy, surroundings, threats, etc.), they find patterns and utilize this information to play the game, or carry out actions based on what their programming tells them or allows them to do.

Alan Turing was (and still is) an important figure to mathematics and computer science. In his lifetime, the computer science area of his life depended more on the concepts of mathematics rather than the other way around. Over his lifetime he made a name for himself; Turing was involved in the fields of mathematics, cryptanalysis, logic, computer science, mathematical and theoretical biology. As for the 2 more relevant topics here; he published many papers, designed inventions, and many more, his most important inventions being the Turing Machine, the Turing test, and becoming the widely accepted father of artificial intelligence.

Turing also had a lot of work outside of computer science. He has a lot of recognition for helping to crack codes sent by the Germans in WW2, publishing some of his greatest work, “The Chemical Basis of Morphogenesis” (relating to mathematical biology in 1952) and pattern formations in nature, for example, Fibonacci phyllotaxis or Fibonacci numbers in plants. 

[1] MacCormick, J. (2012). Nine algorithms that changed the future. Princeton: Princeton University Press.

[2] En.wikipedia.org. (2017). Machine learning. [online] Available at: https://en.wikipedia.org/wiki/Machine_learning [Accessed 14 Nov. 2017].

[3] Novak, D. and Di Renzo, A. (2017). Twelve Mathmatical Concepts. [ebook] p.7. Available at: http://file:///C:/Users/Katie%20Young/AppData/Local/Packages/Microsoft.MicrosoftEdge_8wekyb3d8bbwe/TempState/Downloads/Twelve_Concepts%20(1).pdf [Accessed 14 Nov. 2017].

[4] En.wikipedia.org. (2017). Alan Turing. [online] Available at: https://en.wikipedia.org/wiki/Alan_Turing [Accessed 14 Nov. 2017].

[5] En.wikipedia.org. (2017). PageRank. [online] Available at: https://en.wikipedia.org/wiki/PageRank [Accessed 14 Nov. 2017].

[wonbindatascience]

Machine Learning vs Statistical Modelling

Machine Learning

is an algorithm that can learn from data without relying on rules-based programming.

Statistical Modelling

is formalization of relationships between variables in the form of mathematical equations.

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: 

statistics draws population inferences from a sample, 

while machine learning finds generalizable predictive patterns.

https://www.analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling/ https://en.wikipedia.org/wiki/Machine_learning#Relation_to_statistics

[surabhycognizinfotech-blog]

#Machine_Learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.

[explainprogrammerhumor]

Using Deep Learning to Solve One of Humanity's Oldest Mysteries

Most programming languages have a way of organizing data called an ordered list or an array. This lets you take pieces of data and group them together in some kind of order, which makes it easier to do stuff to each item in that group.

For example, instead of telling the computer,

"Hey, reset the passwords for Alice, Bob, Carol, Dan, Ellen, and Frank. Now sign out Alice, Bob, Carol, Dan, Ellen, and Frank. Now send an e-mail to users Alice, Bob, Carol, Dan, Ellen, and Frank."

...you can tell the computer something simpler like this:

"Hey, put Alice, Bob, Carol, Dan, Ellen, and Frank in an array called 'Users'. Now reset the passwords for Users. Now sign out Users. Now send an e-mail to Users."

Programming languages all use different punctuation marks to indicate different things. In JavaScript, you use commas , and [square brackets] to tell the computer that something is an array.

All programming languages that have arrays also have some way of sorting them. For example, if you tell the computer to sort an array that contains numbers, it'll put them in order from smallest to largest or the other way around. If the array contains pieces of text, the computer will put them in alphabetical order.

In this meme, chicken and egg emoji have been put into a JavaScript array. (Emoji started going "mainstream" in about 2010, and are now so popular that some programming lanuages let you write code with them, just like letters and numbers.) This JavaScript array is then sorted.

The joke is this is a way to answer the age-old question, "What came first: the chicken or the egg?" But really, it's about as valid as putting the words 'chicken' and 'egg' in alphabetical order and saying that answers the question. In the "dictionary" that computers use, the chicken emoji comes before the egg emoji, which is why JavaScript puts the chicken first when it sorts the array.

Sorting is one of the first things you learn when picking up a programming language, and it has absolutely nothing to do with "deep learning," which usually refers to artificial intelligence or machine learning or something else fancy. The other joke is the tech industry tends to inflate the value of things by using really over-blown buzzwords to describe basic stuff.

[techgnext]

How machine learning technology is used in business?

Machine learning is a subset of artificial intelligence where computers use algorithms to learn from data. How machine learning technology used in business? #Machine_Learning #Machine_learning_in_business #Technology #Business https://wp.me/pcnMI5-19n

“Machine Learning” Does this term ring a bell? You must have heard or read this term somewhere. And why not, machine learning has become so popular in recent years that everyone knows this term. However they are unaware of the meaning of it. Don’t worry, in this article I will be telling you all the meaning of this technology. And how business’ use this machine learning technology. Let’s dig…

[kennethmontiveros]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

Artificial Intelligence (AI) for Marketing 101 published first on http://nickpontemktg.blogspot.com/

[josephkchoi]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

Artificial Intelligence (AI) for Marketing 101 published first on https://nickpontemrktg.wordpress.com/

[itsjessicaisreal]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

from Marketing http://webanalysis.blogspot.com/2019/10/artificial-intelligence-ai-for.html via http://www.rssmix.com/

[samanthasmeyers]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

from Marketing http://webanalysis.blogspot.com/2019/10/artificial-intelligence-ai-for.html via http://www.rssmix.com/

[jjonassevilla]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

from Marketing http://webanalysis.blogspot.com/2019/10/artificial-intelligence-ai-for.html via http://www.rssmix.com/

[annaxkeating]

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts. What is Artificial Intelligence?  There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from Oracle.com: "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect." So in a nutshell AI refers to machine, which can learn and become intelligent like humans. AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks. One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML). What is Machine Learning (ML)? Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia). Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source: https://en.wikipedia.org/wiki/Machine_learning) There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning. Supervised Learning In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Predict churn propensity of a customer. You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk". Unsupervised Learning In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source: https://en.wikipedia.org/wiki/Machine_learning) Example: Uncover customer segments. Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance. Reinforcement Learning In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward. Example: Provide recommended products to customers. Reinforcement leaning can be used to develop a online product recommendation engine. Other Terms that you should be aware of Structured Data Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc. Unstructured Data Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data. Marketing Uses of AI There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.

Customer Segmentation

Ad budget allocation across channels or by channel

Content creation

Chatbots - Which understands humans questions and then responds with appropriate response.

Churn Prediction/Customer Retention

Product recommendation engine

Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.

----------------------------------------------------------------------------------------------------------

Signup for my online courses. Here is a coupons for 50% off on the first month. Use coupon code 50OFFFIRSTMONTH at checkout.

from Digital http://webanalysis.blogspot.com/2019/10/artificial-intelligence-ai-for.html via http://www.rssmix.com/