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Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parentartificial intelligence (AI).

The reality is that Machine Learning as a concept is as old as computing itself. As early as 1950, Alan Turing was asking the question, “Can computers think?” In 1969, Arthur Samuel helped define machine learning specifically by stating, “[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”

While the concept has been around for more than half a century, we have finally reached a point in technological advancement where hardware and software can actually help developers match their aspirations with tangible reality. This development has led to not only the rise of machine learning and AI advancements, but, more importantly, also advancements inexpensive enough for anyone to use.

Why Did We Create This Guide?

While the topic of machine learning and AI has been exhaustively covered in the technology space, a singular comprehensive guide has not been created in the marketing space on the topic, including how it affects marketers and their work. This space is so thick with technology-based terminology, but not every marketer has the chops to venture into the space with confidence. With many products coming to market, iPullRank believes that preparing marketers to tackle the landscape armed with a solid foundation is important.

Who Is This Guide For?

Since machine learning touches an ever-increasing number of industries, we’ll also touch on several different ways that machine learning is impacting people in many professions. Most data scientists use R and Python for machine learning, but have you met a marketer these days that only lives and breathes data science? We created this guide for the marketers among us whom we know and love by giving them simpler tools that don’t require coding for machine learning.

We’ll briefly touch on other spaces to round out marketers’ understanding of the complex topic we’re tackling. We’ll also look at how machine learning can help marketers through examination of use cases, plus we’ll dive into martech, a field that’s increasingly including machine-learning concepts.

How Will This Guide Help?

Since we want to create the most comprehensive resource on machine learning for marketers, this guide will be far more than explanatory. We’ll be looking at relevant use cases related to machine learning and delving into practical use of machine learning so that you can begin to use the technologies we’ll discuss after you read this guide.

We’ll also discuss essential jargon that you’ll need to know about machine learning and how machine learning guides SEO specifically, plus we’ll delve into the topic of chatbots (just what do they do, anyway?).

Finally, we’ll help marketers actually use machine learning, but focus on the common problems beginners face. Along the way, you’ll get a look at specific tools and platforms you can use more effectively.

Machine learning is a vital tool for marketers to add to their knowledge base and future-proof their skill sets. As the technology behind it continues to develop, machine learning won’t be something you read about in tech articles; it’ll be essential to organizations of all sizes.

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    1

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    The Basics of Machine Learning

    Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parentartificial intelligence (AI).

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    2

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    Supervised vs Unsupervised Learning and Other Essential Jargon

    By 2020, the digital universe will be 40,000 exabytes, or 40tn gigabytes, in comprehensive size. In contrast, the human brain can hold only 1 million gigabytes of memory. Too much data exists for humans to parse, analyze, and understand. Here is where machine learning is finding its value: The raw amount and constant growth of data creates a need for methods to make sense of that data overload in ways that can impact an array of professions and lifestyles.

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    3

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    What Marketers can Accomplish with Machine Learning

    Now that we know the language of machine learning, we’re ready to look at specifically what marketers can do using machine learning. The ad tech space is full of companies promising the next silver bullet for marketers. Armed with your new knowledge of machine learning and related concepts, we can begin to look past the veil toward what makes these tools, process, and marketing services tick.

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    Successful Machine Learning use Cases

    Now that we’ve pushed through both generalities of machine learning, its basic concepts, and how they apply to areas of marketing, it’s time to dive into the specifics of how companies are using these processes.

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    How Machine Learning Guides SEO

    Our Vector Report served as a delicious appetizer for the world of machine learning and how it applies to SEO and search. As we’ve noted, the core of modern search builds upon models based on machine learning. As the field has expanded, both search engines and search practitioners have worked to incorporate more robust machine learning processes into their methodologies and technologies.

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    Chatbots: The Machine Learning you are Already Interacting with

    Chatbots are probably the most common format of reinforced learning that people are interacting with daily. Since chatbots can pass the Turing test, some people probably don’t know that they are interacting with chatbots in some cases.

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    How to Set Up a Chatbot

    Here, we’ll dive into the nuts and bolts of how you can set up a chatbot yourself. We briefly touched on the overall structure and components of a chatbot, as well as some of the third-party tools that exist to create chatbots, but we want to dive deeper into the concept and give you a strong idea of exactly how you can put down this guide and get to building your first chatbot.

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    How Marketers Can Get Started With Machine Learning

    Now that we’ve taken the time to outline exactly how you can use and build chatbots, let’s take a look at specific third-party options that let you get started with machine learning regardless of your technical prowess.

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    Most Effective Machine Learning Models

    Previously, we’ve already talked about the macro levels of machine learning (ML) which included the supervised and the nonsupervised module. Additionally, we also discussed the best tools which marketers use for ML and the best way of using such tools. In this article, we’ll be talking about the models and the algorithms which stand as inner logic for all those that we’ve previously discussed.

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    How to Deploy Models Online

    Marketers and businesses who want to use machine learning (ML) beyond the tools previously discussed may require better customization to deploy their specific online models. In addition, marketers as well as business organizations may be able to come up with ways on how their developers can fully use their models which, in this case, may call for other helpful tools.

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    How Data Scientists Take Modeling to the Next Level

    Data scientists are exceptional people who bring data analysis to a new level. These analytical experts have a high level of technical skills and can solve the most complicated problems with a high level of inquisitiveness.

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    Common Problems With Machine Learning

    Machine learning (ML) can provide a great deal of advantages for any marketer as long as marketers use the technology efficiently. Knowing the possible issues and problems companies face can help you avoid the same mistakes and better use ML.

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    chapter 13
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    Machine Learning Quick Start

    A certain notion exists that machine learning is only for the experts and is not for people with less knowledge of programming. The people you’ll be dealing with are machines, and machines can’t make decisions on their own. Your job is to make these machines think. At first, you would think that this task is impossible, but through this guide, you’ll understand how machine learning (ML) can be much easier than it sounds.