Why Get Into Machine Learning?

Discover Your Personal Why And
Finally Get Unstuck

In this post, we will explore why you are interested in machine learning.

We will look at some questions that can help you get to the root of what draws you to the field.

We will finish with a map showing the 4 main “whys” so that you identify where you fit and what resources to target.

Question Your Why

Why are you interested in machine learning? Have you deeply considered this question?

It is useful to know your why, because you can use it as a filter to best choose the projects and tasks that you enjoy to work on. If you cannot come up with a clear why, that can be useful too as it can motivate you to try a bunch of different things and find out what you like or want to do.

You may be drawn to machine learning for lots of reasons. Perhaps you are responding to media and news articles about big data and data science. Perhaps you have seen a glimpse of machine learning in a tool or from a friend and you think it’s cool. There may be many reasons, but learning machine learning is hard work. To have the confidence and persistence to get through studying the hard and frustrating parts, you will want to have a strong why to fall back on.

I’m going to pose some rhetorical questions, and I want you to think about (even write down) your answers and see which one gels with you the most. One question is not better than another – keep an open mind.

What do you want machine learning to do for you?

Solve a Problem

Do you have a problem that you think machine learning can solve?

Maybe it’s an open business problem or a problem at work. Maybe it’s an opportunity you can see in the market. Nevertheless, you are thinking about machine learning as a tool for you to learn and apply to a problem.

In this case you may be interested to learn tools that provide implementations of algorithms you could use quickly. You will also very likely be interested in the creative ways to use these tools, such as case studies on problems like the problem you want to solve.

Technical Achievement

Is learning machine learning a mark of achievement?

Maybe machine learning is a popular technical field and you get great pride from learning new and difficult technologies and tools. Maybe you see machine learning as your next big challenge and opportunity for growth and a chance to demonstrate your abilities to learn and master technical materials.

If this sounds like you, you may be interested in books of algorithms where you can fast track getting an understanding of a method and how to use it without having to get down into the latest research. You will also very likely be interested in completing courses, entering competitions and implementing algorithms yourself.

What do you want to do with machine learning?

Extend the Field

Do you already have some experience with machine learning and want to extend what is possible?

Maybe you have been around the block with machine learning and read a book or completed a course. You have found a question or a method that you just can’t put down and not only do you want to go deep on that method, but you want to push the boundaries of what that method can do and has been shown to be capable of.

If this rings a bell, you may be interested in deep subject matter on the subject such as research papers and monographs. You may also be very interested in hearing expert opinions on the subject and exactly where the edges of the frontier are.

Do What Was Impossible Before

You have some experience with machine learning and you have some domain expertise and you want to do things in your domain that are not possible without machine learning. These are not necessarily problems like those mentioned above in the “Solved Problem” section, but rather the extension of a domain using experience of and capabilities provided by machine learning.

You will be interested in methodologies from data mining to automatic discovery of patterns. You will also very likely be interested in case studies of discoveries and extensions made by machine learning methods in similar domains.

Machine Learning Map

This is all a gross simplification of the field, but we could classify the motivation to learn machine learning by the type of work we want to do. We can classify the type of work we want to do into solving a problem in machine learning or in another domain. You can classify the types of tasks as tasks of a practitioner and tasks of a researcher.

I have tried to capture this summary in a table, see below.

The table has two rows by domain: the domain of machine learning and the other domain (such as analytical chemistry, petroleum mining or transport analysis.). The table has two columns by role: practitioner and researcher. Each box has the type of task for that domain-role intersection which is either solve a problem or extend the field. And each cell in the table lists the types of resources that may be of interest to a person interested in that task.

Why get into machine learning

Map showing the 4 why’s for getting into machine learning.

Each cell can be considered a why that is motivating you to learn more about machine learning and the list of resources are things that can help in that pursuit.

This is just one way to slice the pie, but I’ve been meditating on it for a few weeks now. I worked hard on the groupings and I’m very interested to hear what you think of it, please leave a comment. I’d love to get some pro’s to start poking holes in it so we can see the strengths and limitations of this model (all models are wrong, it’s just a matter of degree).

Please leave a comment and let me know where your why fits in and what you identify with.

I have to say thanks to my wife for helping me think through this and map it all out on a whiteboard.

41 Responses to Why Get Into Machine Learning?

  1. Shantnu December 3, 2013 at 8:40 am #

    For me, it’s just curiosity/technical achievement. It may change to “Solve a problem” once I understand the domain. It’s one of those things- you can’t really know its benefits till you have more than a surface understanding.

    Some people may be hesitant to invest a lot of time learning something that may not be useful. Which makes it a chicken and egg problem- you can’t know how useful it is till you learn it, but you don’t want to learn it unless you know it will actually help you.

    Which is why a good, easy to get into guide may be useful.

    PS: Your subscription form says we get a free pdf- is that working?

    • Deepak August 15, 2017 at 11:52 pm #

      I would like to be Machine Learning expert over the period. Would like to do research in the field of agricultural and social work

  2. jasonb December 3, 2013 at 10:10 am #

    Really good point Shantnu.

    I am putting together a high-level 101 course and I am spending a lot of time on the benefits of machine learning (the why) rather than the what and how like many other introduction texts and courses. I think knowing the benefits can help to clarify the why for getting into the field.

    You’re point is great because it is not until you internalize those benefits (they become real to you) that you can really grasp how it can help.

    I have sent you an email with the link to the resource guide. I just added the guide yesterday and all new sign-ups will get the link immediately. I’m keen to hear what you think of it.

  3. Jason January 10, 2014 at 11:22 pm #

    For me its always been about the dream of true AI and understanding how our brain really works. I think machine learning offers the best path to start to get to that goal so that is why I am interested in it. I think some of the purely statistical techniques of ML are interesting but I really am more interested in building models that try to simulate the brain and topics like deep learning really fascinate me.

  4. Ketan January 20, 2014 at 2:18 am #

    Hi Jason,
    I’ve watched Andrew Ng’s Coursera course, Udacity’s AI course and Caltech’s online ML course. Also, I’ve read Toby’s book and a a book called ‘Algorithms for the intelligent web’. I have brushed up my statistics, probability and other relevant areas in the past few months as well.
    My primary goal is life has been to invent human-like intelligence with emotion, or to help develop it in case someone else does it. But now I have relatively humbler goals, like extending ML & AI in some manner. I’m working on a new method of mine, which has its roots in cognitive sciences and I’m testing it on octave, as I right this.
    I’d really love if you, or anyone reading this, could point me to resources where I could get to know the state-of-the-art of AI/ML, what are the technological and conceptual limitations and other information, so that I can clearly know where we’re heading.
    Moreover, are there any benchmark datasets, that one can use to test one’s ideas?
    To be precise, I’m working on a classifier right now. Do let me know of any way I can benchmark my classifier against the state-of-the-art.

    • Kenzo February 18, 2014 at 2:54 pm #

      You might want to check out MIRI (Machine Intelligence Research Institute) if you do not know it already.

      • jasonb February 19, 2014 at 8:46 am #

        Thanks Kenzo. I’ve been following them for years, back when hey were the singularity institute.

  5. Tom L November 11, 2014 at 2:41 am #

    I work in education in the area of Research, Evaluation and Assessment. I’d like to use machine learning to help us with early identification (academic, behavioral, mental health, etc.) of student needs, as well as to better monitor when students are ready to progress through learning (e.g. differentiation, intervention, etc.). We have lots of test scores and use statistics to both evaluate and predict student outcomes, but this is typically done across wide ranges of time (e.g. annual state tests). I want to improve how we use day to day data to meet the individualized needs of kids more proactively. I took an AI/Neural Networks course in undergrad and thought I would explore this area to see what it might have to offer.

    • Jason Brownlee November 11, 2014 at 7:53 am #

      Thanks for sharing Tom, contact me if you need any help or have any questions.

  6. Akshay January 22, 2015 at 7:01 pm #

    Hi Jason,

    Actually i am not getting the link for downloading the pdf “Why machine learning is important for you and world ” . Is that link disabled ?

    Can you please help me to get that ?

    Br,
    Akshay

    • Jason Brownlee January 23, 2015 at 5:25 am #

      I’m sorry to hear that, please email me at Jason@MachineLearningMastery.com and tell me exactly what is happening.

      I just clicked the link in the post, completed the form and downloaded the PDF without incident.

      Jason.

  7. Ganesh July 27, 2015 at 11:48 pm #

    Hey Jason! I am a CS undergrad in India, and I recently stumbled across Machine Learning. Since I have a course on AI this semester, I am thinking of making a project using Machine Learning. I am thinking something on the lines of Optical Character recognition of an Indian Language( Haven’t decided which one, yet.) I would really love to know what should I learn to solve this task, and successfully complete this project.

  8. Matt October 28, 2015 at 2:00 am #

    I’ve been working as a data analyst for years now and I think the skills and knowledge of data analysts are generally pretty average in light of what is now possible. Usually it amounts to querying databases through something like SAS, and running results through Excel for a bit of analysis and presentation. The advanced stuff never gets a look in usually because the companies are unwilling to shell out for Enterprise Miner. I’ve been learning Machine Learning for a year or so now and am convinced that it’s a field that data analysts will need more and more knowledge of in the future, especially if you want to be at the top of your game. Wish I’d had some of this ML knowledge and the tools available throughout my career. They could have solved some big complex problems pretty easily.

  9. J George February 14, 2016 at 9:05 am #

    I am interested in developing my knowledge base, and as a HS teacher, to introduce some of the basic concepts to my students.

  10. Di Wu March 20, 2016 at 3:02 am #

    i am a junior programmer , i want to learn ML and use it in investment in stocking market. I have use java for years, but have no idea in ML, so I want to know where is the suitable start place for me , could you please give me some advice or introduce some guidance books.

  11. Blank May 31, 2016 at 10:22 am #

    I am a junior programmer , I want to learn ML and new algorithms

  12. Dr. Neha Mittal July 14, 2016 at 11:20 pm #

    Hi Jason,

    I am beginner in Machine Learning. I am learning ML step by step suggested by you through your mails as well as blogs. This field is fascinating me because of its applications. Although I don’t have any exact problem in my mind, still I want to know more about ML.

    May be while going through the learning process of ML something would click in my mind.

    But I think I fall into very first category where I am thinking about machine learning as a tool for me to learn and apply to a problem in near future. Also, I have heard about this field from lots of friends of mine working in corporate as they are applying certain algorithms to get business insights.

    So I am interested to learn tools that provide implementations of algorithms that I could use quickly. Also I am very interested in the creative ways on how to use these tools, such as case studies on some business problems.

    I am from Mathematical background and little bit of Statistical background comparatively.

    Please suggest me the best way to learn machine learning and become master some day.

    Thanks

  13. Jim Kitzmiller August 11, 2016 at 7:05 am #

    Hi Jason,

    I want to leverage my talents and experience, have fun, do ethical things, and make a good living.

    Jim

  14. Mark September 17, 2016 at 1:13 pm #

    Hi,

    Last semester we did a PIC32 project, which is a pair of gloves that can recognize the gestures of hands and translate it into voice. We did it by simply mapping the data we collect to the gestures within a fixed threshold range. However, it suffers with a high error rate. It occurs to me that it would be better to use machine learning on the data we collect using the gloves. It will save lots of hard coding work and be more scalable.

    I would like to put machine learning into practice and learn through it. I hope it can become one of my competence and gives me a job in the future.

    • Jason Brownlee September 18, 2016 at 7:55 am #

      Wow, that sounds like a great project Mark. I hope you can report back and share how it went?

  15. Paul October 20, 2016 at 5:42 am #

    Hey! Loving these articles. It really shows that you’ve been meditating on those groupings. It’s bleeding edge stuff that only someone with a deep understanding of the field AND a clear mind could create.

    Paul.

  16. Christopher December 15, 2016 at 6:22 am #

    Hey Jason,

    The primary reason I want to learn Machine Learning is to be able to better transition an organization into first utilizing ML on a practical level whether it be with applying advanced formulas to sets of data to make sense of key areas of growth, or to more generally be aware of Machine Learnings role in the next major leap in several different fields.

    It sounds quite general, but I am really young and I can’t say a company in a particular industry because there are a lot of industries that interest me. I hope I answered accurately 🙂

    • Jason Brownlee December 15, 2016 at 8:29 am #

      Fantastic Christopher, I hope that I can help in some small way.

  17. Amrita December 17, 2016 at 4:53 am #

    Hey Jason,

    I am a final year CS undergrad, AI and NN were my subjects this semester. I am thinking of making a project on extracting all the text from a jpeg image using ML. What can I think further nd how to implement dis? Suggestions plzz 🙂

    Amrita

  18. William Dekou March 4, 2017 at 5:37 am #

    I join this adventure for making intelligent software product in my career

  19. Tim April 8, 2017 at 3:58 am #

    Hey Jason
    This is an awesome forum. Wish I would have found it sooner. I am a business analyst that has started using pandas and python for data analysis where excel falls short. I would consider myself a data scientist. I am wondering what the career prospects are for data scientists with advanced programming skills and ML skills but lack a PhD?

  20. shepherd April 27, 2017 at 10:27 pm #

    one of the reason I want to learn Machine Learning is that,i want to an expert in data mining and data science. Am really interested in big data and data analyst as well. I am also a computer science student

    • Jason Brownlee April 28, 2017 at 7:40 am #

      Thanks shepherd, it’s great to have you here as part of the community.

  21. shepherd April 29, 2017 at 1:20 am #

    you are my pillar of my strength DR Jason. I need your help, can i get step by step and procedures of data mining(career) like what u did in machine learning.

  22. Ani June 30, 2017 at 6:57 am #

    Hey Jason,
    You are doing a great job providing all this useful information about Machine Learning.I don’t have any specific problem to solve right now but maybe it’ll will be related to solving issues related to agriculture or even share markets in future.For me,it’s more about curiosity and mathematics that is involved and also I see it as a technical achievement.I started with ML but after few weeks I couldn’t continue and left it for months but thanks to your blog I am starting it again.

  23. Utkarsh Agarwal July 11, 2017 at 5:01 pm #

    Hi Jason

    This model is very accurate for a beginner.
    I myself is a beginner and looking at this model has helped me to set proper goals.
    I can now focus on specific type of learning rather than going through everything that ML has to offer.

    Thanks for this awesome model

    Cheers!!

  24. Samir July 27, 2017 at 1:30 am #

    I’m new to the domain of Machine Learning. My specialisation is in technology risk. I want to learn ML / AI to understand how ML / AI can further the technology risk practices.

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