Find Your Machine Learning Tribe

Get Started And Avoid Getting The Wrong Advice

Machine learning is a fascinating and powerful field of study filled with algorithms and data.

The thing is, there are so many different types of people interested in machine learning, and each has different needs. It is important to understand what it is you want from machine learning and to tailor your self-study to those needs.

If you don’t, you could very easily go down the rabbit hole and get lost, lose interest and not get what you’re looking for.

Find Your Tribe!

In this post you will discover the 10 main groups of people interested in machine learning. I call them machine learning tribes.

You will discover the general needs and the types of resources that each tribe will find most valuable.

Importantly, you can review the 10 tribes and discover where you fit, find comfort that there are other people like you and get an idea of what your next steps could be.

Where do you fit? Leave a comment and let me know.

Find Your Machine Learning Tribe

Find Your Machine Learning Tribe
Photo, some rights reserved.

Avoid Tribe Mismatch, It’s Very Common

You’re interested in machine learning. You ask around and a specific course or book is recommended.

A few hours in, you get frustrated but you’re not sure why.

Has this happened to you?

It’s because the resource you picked up is great, it just might not be great for your specific circumstance.

This is a common problem and I call it the problem of mismatch.

Classic Example of the Developer and the Textbook

A classic example is a developer interested in working through a one-off problem. They are recommended a machine learning textbook.

They purchase it, start reading and never make it past the first chapter.

A textbook is perfect for a student in a graduate machine learning class with 4 years of math class in their recent past.

It is next to useless for a developer, 10 years into their career looking at machine learning as a tool to deliver a result.

This is why it is critical to know about the different groups interested in machine learning and to which group you belong. So that you can find other people like you and start using the resources that will actually help you get the solution you’re after.

10 Machine Learning Tribes

In this section we lay out 10 different groups of people interested in machine learning.

I’ve given each name, highlighted their main goals and interests and listed resources that people in that group can use in their next steps. I’ve also grouped the groups by general theme such as business, academic, engineering and data.

There may be some overlap in the groups. It is also possible that you belong to one or more of them. That’s fine (and I’d love to hear about it in the comments).

Also, we are constraining our interest here to machine learning, not all of data science, which is broader.

Let’s dive in.

Tribes Overview

Here’s a quick snapshot of the 10 tribes:

  • Business Tribes
    • 1) Business Person with a General Interest
    • 2) Manager Interested in Delivering a Project
  • Academic Tribes
    • 3) Machine Learning Student in a Undergraduate or Graduate Class
    • 4) Machine Learning Researcher Interested in Impacting the Field
    • 5) General Researcher Interested in Modeling Their Problem
  • Engineering Tribes
    • 6) Programmer Interested in Implementing Algorithms
    • 7) Developer Interested in Delivering One-Off Predictions
    • 8) Engineer Interested In Developing Smarter Software And Services
  • Data Tribes
    • 9) Data Scientist interested in Getting Better Answers to Business Questions
    • 10) Data Analyst interested in Better Explaining Data
Machine Learning Tribes

Mind Map of Machine Learning Tribes

Business Tribes

Generally, these are people interested in harnessing machine learning effectively in their organization, but not necessarily interested in the nuts and bolts of algorithms or tools.

Business people might use terms like business intelligence or predictive analytics, both of which are more general fields that may make use of machine learning techniques.

1) Business Person with a General Interest

This may be anyone from an executive to a consultant who has heard about machine learning and is looking to make use of it strategically, perhaps in upcoming projects or initiatives.

This is not really my area, but some resources that might be useful for more strategic thinking include:

I would also recommend the books in the next section for “Managers“.

Gartner Magic Quadrant for Advanced Analytics Platforms

Gartner Magic Quadrant for Advanced Analytics Platforms Taken from Gartner, all rights reserved.

2) Manager Interested in Delivering a Project

This is a project manager or similar leadership position on a project in which machine learning is being used. It may be a niche feature of the project or core to the project.

Resources that would be useful would be high-level perspectives on the field that relate various classes of problem and algorithm without going into too much detail.

Check out the following books:

Amazon Image

Academic Tribes

Generally, these are people interested in machine learning from an academic perspective. They may be students (undergraduate or postgraduate) or otherwise associated with a university.

Additional examples are post-docs, research associates and lectures of various sorts.

Academic tribes may spend a lot of time researching a specific machine learning algorithm in research papers. You can learn more about researching algorithms in the post “How to Research a Machine Learning Algorithm“.

3) Machine Learning Student in a Undergraduate or Graduate Class

A machine learning student is very likely taking a class and interested in hyper-specific questions related to techniques and algorithms.

A student has the structure to focus and the time to dive deeper into the material. They are best served with a textbook. Some of the best machine learning textbooks are as follows:

4) Machine Learning Researcher Interested in Impacting the Field

A machine learning researcher is interested in a deep understanding of one aspect of machine learning to the point of making a minor addition to extend the field.

A researcher is interested in research papers, journals and the organizations and networking that go along with them.

Textbooks don’t cut it, they’re secondary sources and out of date.

Some high-profile machine learning journals and proceedings are:

Checkout the great answers to the Quora question “What are the best conferences and journals about machine learning?“.

Here’s a handy list of the top 50 ranked journals in Artificial Intelligence.

5) General Researcher Interested in Modeling Their Problem

A general researcher may be interested in machine learning, but as a tool. They are most likely interested in building a descriptive or predictive model using their own data.

For example, a scientist from the field of client research, geology, or biology has their own dataset and is looking to create a model in order to make predictions and/or better understand the underlying problem.

They are often less interested in model accuracy and more interested in model explainability. Therefore, simpler well understood methods borrowed from statistics are preferred, such as linear regression and logistic regression.

Nevertheless, good systematic process is desired.

I would recommend the resources under the “Engineering Tribes“, specifically,”Developer Interested in Delivering One-Off Predictions“. Also take a look at the “Data Scientist” group under “Data Tribes“.

Engineering Tribes

Generally, a group of developers that are used to delivering solutions to problems with software and want to incorporate machine learning.

A good general post I recommend for engineers looking to get into machine learning is “Machine Learning for Programmers“.

Engineer Tribes can get a lot of help and support in machine learning communities like those on Q&A sites. For more information, checkout the post “Machine Learning Communities“.

6) Programmer Interested in Implementing Algorithms

An excellent way for a programmer to develop skills in machine learning is to use their existing programming skills and implement machine learning algorithms from scratch.

I talk a lot about this approach and give good tips and resources in my blog post “Understand Machine Learning Algorithms By Implementing Them From Scratch“.

Three books I recommend for this approach taken from that blog post are:

Amazon Image

7) Developer Interested in Delivering One-Off Predictions

A developer is not necessarily a great programmer, and programming is not required to develop and deliver an accurate and reliable predictive model.

A one-off predictive model may be required in a business environment for the set of predictions it can provide. It is also a powerful model for self-stud, for working through practice datasets and even machine learning competitions.

You can learn a lot by working systematically through a process on a problem and delivering a standalone model.

Checkout my systematic process for working a machine learning problem end-to-end in the post “Process for working through Machine Learning Problems“.

8) Engineer Interested In Developing Smarter Software And Services

An engineer interested in adding machine learning to their software project requires some knowledge of the algorithms, some knowledge of how to work problems end-to-end and knowledge for how to get the algorithm running reliably in an operational environment.

This group of people grow from the previous two groups described and might best be described as a machine learning engineer. They look to use fast algorithms that deliver reliable and accurate results, balancing these concerns.

This group also makes heavy use of machine learning libraries and infrastructure.

Some useful resources on jumpstarting machine learning libraries include:

Amazon Image

Also, checkout the post “Building a Production Machine Learning Infrastructure“.

Data Tribes

Generally, this are groups of people that primarily are in data roles but may need to make use of machine learning.

9) Data Scientist interested in Getting Better Answers to Business Questions

The learning does not stop when you’re a data scientist.

You must stay on top of the latest data flows, techniques and algorithms. This includes the machine learning techniques that you need to describe data and create predictive models.

The data scientist can take what they need from the more applied resources listed under “Engineering Tribes“, as well as the more theoretical resources listed under “Academic Tribes“.

Nevertheless, some data science-centric machine learning resources that include this mix are:

10) Data Analyst interested in Better Explaining Data

Data analysts are primarily interested in explaining data in the context of business interests. Sometimes machine learning algorithms are useful for providing more powerful models. Mostly descriptive models, but also sometimes predictive.

Like the group “General Researcher“, this group likely has a good foundation in statistics and statistical inference. Also, given that they are most likely interested in a descriptive model, classical methods like linear and logistic regression may be sufficient. Explainability over accuracy in the resulting model.

Many of the same resources above would be useful, although perhaps with more of a statistical inference stance.

Action Step

In this post you discovered 10 different groups of people interested in machine learning.

Here’s that handy mind map to summarize the 10 tribes:

Machine Learning Tribes

Mind Map of Machine Learning Tribes

Your action step is to review the list and figure out where you belong.

Which group is your tribe? Leave a comment below and let me know.

128 Responses to Find Your Machine Learning Tribe

  1. Najmuddin September 4, 2015 at 11:54 pm #

    Great post! Keep up the good work.

  2. Susan Sun September 5, 2015 at 4:24 am #

    I’m in group 10. 🙂

    • Jason Brownlee September 5, 2015 at 8:11 am #

      Thanks Susan, I wonder if a text like “The Elements of Statistical Learning” would be a good place to start for you?

  3. Justin September 5, 2015 at 7:49 am #

    This is excellent! Very well thought out and communicated. Thank you for all the resources as well!

    • Jason Brownlee September 5, 2015 at 8:09 am #

      Thanks Justin.

    • Amit September 7, 2015 at 7:54 am #

      I am in Group-8 – machine learning engineer. That would be a great job title.

  4. hexcola September 5, 2015 at 2:10 pm #

    Thank you, Jason, this is really awesome!

    I’m a programmer, I think group 8 fit me well, and for my business, group 9 will help a lot, any advice?

    btw, can I translate your blog to Chinese and post to my blog? It definitely will help a lot of people, and I will keep your original link for sure.

    • Jason Brownlee September 5, 2015 at 5:18 pm #

      Very nice hexcola.

      Rather than translate, why not start your own blog and share your machine learning journey!?

  5. Viral Rathod September 5, 2015 at 4:41 pm #

    Great way to start my ml learning. Really useful to identify yourselves as one of the mentioned tribes. I’m 8! Thanks Jason.

  6. Srinivasan September 7, 2015 at 3:42 am #

    I belong to Tribe-2. But would want to build deep knowledge in Machine learning in next couple of months by implementing/using some of the algorithms. So on the journey of trying few algorithms and learning Python and R now. Your post is very useful. Thanks a lot.

  7. Beatrice September 7, 2015 at 9:03 pm #

    General Researcher Interested in Modeling Their Problem

    • Jason Brownlee September 8, 2015 at 5:51 am #

      Thanks Beatrice!

      • Chris October 14, 2017 at 3:30 pm #

        Hi Jason,

        Im a guy in South Africa and would like to get into machine learning. I do not have an IT background. Is there space in the area of learning for someon with no IT background to develop a career in the field? And what areas can one get into if one gets into business as a ml consultant?

  8. Amedeo September 8, 2015 at 11:05 pm #

    Excellent Jason! We are a start up on advanced analytics and all of us follow your blog and waiting with lot of interest your mail. Thanks Jason!

  9. Osayame David Gaius-Obaseki September 16, 2015 at 3:44 am #

    Hi Jason,

    I’m in Group 8, but often find myself in Group 3. I am currently entering grad school, and looking forward to taking some courses. More importantly, for my tribe (8), learning ML algorithms and reading papers is a means to an end (building cool things). I’ve found that keeping the end in mind, and being given room to tinker (break things quickly) prevents frustration or boredom.

  10. Emily December 3, 2015 at 12:21 am #

    Jason, this is a fantastic concept! I’m preparing a presentation on instructional design as part of interviewing for IBM Watson Health (keep your fingers crossed for me). To discuss my design process (quite similar to UI design process) I need a context and machine learning seems a good fit. But of course it’s a hugely complex topic and I’m not a programmer, so it’s like pulling a rabbit out of a hat to put together a *very* simple, but accurate story. Your mind map is perfect. I’ll be sure to include a reference to your website in my discussion. Congrats on teaching a maths-avoidant, right-brained baby-boomer mom the basics! Cheers from Boston, MA.

  11. Reet January 3, 2016 at 7:42 pm #

    I belongs to groups 6, 7, and 8.

  12. Pankaj January 16, 2016 at 5:27 am #

    I guess group 9 and 10 suit me the best

  13. Brent February 4, 2016 at 3:27 pm #

    I’m a bit of the 3 major groups. I’d pick 1, 5, and 10.

  14. john February 12, 2016 at 11:55 pm #

    I’ve been surfing online more than three hours nowadays,
    but I by no means discovered any attention-grabbing article like yours.
    It is pretty value enough for me. In my view, if all site owners and bloggers made good content as you probably did, the web
    will likely be much more useful than ever before.

  15. canismajoris April 13, 2016 at 3:19 am #

    I belong to 8…try hard to move into 4 & 5…hope someone accepts me as their student!!

    • canismajoris April 13, 2016 at 3:20 am #


  16. César Gomes July 19, 2016 at 4:02 am #

    Congratulations for explanation about theme! I liked it so much reading that did not even notice the time pass…

  17. David Snyder July 20, 2016 at 12:31 am #


    Really nice article.

    I’m definitely in one of the Engineering tribes. As I’m just getting started, I’m probably place myself in Group 6. But I hope to continue growing my knowledge and gain deeper and deeper understanding in machine learning.

  18. Jim August 13, 2016 at 12:24 am #

    I’m in 8 – Engineer Interested In Developing Smarter Software And Services –

    Maybe a quick visualisation of feedback received would be good Jason, just a pie chart as feedback comes in, see who everyone identifies with the most!

  19. Omri September 29, 2016 at 4:56 pm #

    I’m in group 9. I am already familiar with many ML approaches but want to have a deeper understanding in order to support my product better. The assumptions (both statistical and practical) of each model plays a crucial part of the decision on which model to invest.

    Are there any additional resources besides the three books that people like me would fine relevant?

    I hope I gave enough information.

    Thanks for the site! It’s excellent!

  20. Young October 12, 2016 at 12:41 am #

    I’m in group 3

  21. Abhishek October 23, 2016 at 9:55 pm #

    I somehow don’t belong to any of the tribe. Because, I am trying to make a career change. I do have an engineering background and with it comes the mathematics background. Also, I have some experience in python (mostly through online courses). Can you help me understand where I belong and how i can proceed?
    p.s. I am doing Machine Learning course offered by University of Washington on Coursera.

    Thanks, looking forward to learning from you.

    • Jason Brownlee October 24, 2016 at 7:06 am #

      Hi Abhishek,

      Think about why you want to learn machine learning and what you want to be able to do with the skill.

      Focus on where you want to be and then associate with that tribe.

      • abhishek gawde October 24, 2016 at 8:02 pm #

        Thanks that would help! Just love this site!

  22. Eric October 28, 2016 at 9:54 pm #

    Any book or textbook you recommend for someone that is curious about machine learning. But is just exploring or testing the waters right now?

  23. Yashraj December 2, 2016 at 9:37 pm #

    I am in group 8. Engineer Interested In Developing Smarter Software And Services

  24. Christopher December 15, 2016 at 7:19 am #

    Nice, I am in groups 3 and 8! I am currently an Undergraduate Student, and I desire to develop production machine learning infrastructures, as well as go a bit more in-depth through various textbooks.

  25. Scarlett December 20, 2016 at 6:38 am #

    Graduate Student just starting out with Machine Learning! Learning more and more about this field and preparing myself to dive into it. Wish me luck!

    • Jason Brownlee December 20, 2016 at 7:24 am #

      Good luck Scarlett!

      Ask lots of questions.

    • Benson February 25, 2017 at 6:52 pm #

      Hi Scarlet,

      How far have you gone into ML?

      I think I am in the same position as you and looking for someone to develop with.

      I am free to share and discuss if you are still in the ML pathway.



  26. Paul December 27, 2016 at 4:08 am #

    Thank you for the wonderful post. You always provide context in an area that is so vast that it is easy to get lost, become disillusioned and quit. Keep it up.
    I bought your bundle on ML and the ebooks are great.

    • Jason Brownlee December 27, 2016 at 5:25 am #

      Thanks for your kind words and your support Paul, I really appreciate it!

  27. Jai December 27, 2016 at 11:36 pm #

    Hey man! Which category would i fit into if I want to apply machine learning to stuff like poker and board games? Or maybe trying to use it in the stock market?
    Which book/course/path would you recommend to such a person?

  28. steve pan December 30, 2016 at 2:41 pm #

    I’m in group 10

  29. Shailesh January 6, 2017 at 7:13 pm #

    Thanks for all the info in this post! Helps me focus my efforts to build expertise a lot. I think I’m mostly an 8 with some 6 & 1 thrown in there as well but have been a little off track by struggling through the mathematical theory. Wondering if you know of any good online communities for #8??

    • Jason Brownlee January 7, 2017 at 8:26 am #

      Hi Shailesh, nice!

      Some ideas for communities for building smart software:

      To some extent this site.
      The IOT guys are very interested in using time series effectively in their apps – good guys to talk to.
      To a lower degree, some of the AI game devs communities are good to talk to.

      It is lonely. When I was building predictive models for operational use, the best guys to talk to were subject matter experts.

  30. Malak Pirtskhalava January 7, 2017 at 9:00 pm #

    I belongs to groups 5 and 7

  31. Lidi January 18, 2017 at 12:48 am #

    Thanks for your nice job.
    I’m a statistician and looking for a data analyst position. So I think Groups 9 and 10 are a better fit for me.

  32. Dark January 21, 2017 at 10:13 pm #

    It was really an awesome post for people who is striving to learn machine learning. But in my case, i am a last year student in instrumentation engineering and i want to do a good project in machine learning using R. So is it possible to do a good project in 2 months which will really help my cv to showcase my skills.
    And Thanks a lot for what you are writing, because it really helped to know it in deeper way.

  33. Precious January 22, 2017 at 1:22 pm #

    I belong to the 3rd category. It has been very challenging its definitely worth it.

    • Jason Brownlee January 23, 2017 at 8:35 am #

      Thanks Precious, it’s great to have you here!

  34. Richard February 18, 2017 at 2:01 pm #

    I feel like I’m all over the place! I’m a developer-turned-manager responsible for everything related to databases at my workplace. I still like to program though. So I think I’m a 1 since I think we could use machine learning to do some predictions based on our data but I’m definitely a 6 and 7 as well. Have a lot to learn but that will be part of the challenge!

    • Jason Brownlee February 19, 2017 at 8:43 am #

      Hang in there Richard!

    • Jeff B April 1, 2017 at 5:37 am #

      Hi Richard,

      I very much relate to your experience. Feels like drinking out of a firehose. I would say I’m a 1, 6, 7 as well.
      Thank you Jason for your informative and extremely helpful emails.

      • Jason Brownlee April 1, 2017 at 5:59 am #

        Take it slow, pick one area, one tool, one problem type. Slowly develop a portfolio to build confidence.

        There’s no rush, applied machine learning is a long-term pursuit.

  35. Benson February 25, 2017 at 7:05 pm #

    Hello Jason,

    A very informative post. Many thanks.

    I think I am a 5, 6 , 7. I am only beginning to understand the ML general overview. I have successfully installed Python and Anaconda set of tools, and tested Ok using a long code I copied and pasted. Results look great as expected.

    Now I think it’s time to learn syntax I suppose ? The word CONFUSION is a great friend sometimes as you try to learn syntax

  36. William Dekou March 4, 2017 at 8:19 am #

    8) Engineer Interested In Developing Smarter Software And Services

  37. Geet March 6, 2017 at 4:28 pm #

    It is very useful jason. I am in the group 9.

  38. Ramesh S March 8, 2017 at 8:23 pm #

    I fall into Engineering Tribe and I am in the group 8.

  39. Kathiravan March 16, 2017 at 6:10 pm #

    Hi Jason, The way you explain things and the order is simply awesome. I fall into Group 6 “Programmer Interested in Implementing Algorithms”

  40. Shreyas Rajesh April 4, 2017 at 3:46 am #

    Hi Jason,

    I have really liked all your articles about machine learning. This one is one of the best though. Really makes my interests more clear. Although, I am still undecided which of these categories I belong to. I am still an undergraduate student but I am greatly interested in learning more in detail about machine learning and applying it in the future in the field of artificial intelligence and hopefully one day try to develop new AI softwares to help the world simplying their tasks. Can you tell me which category you would classify that interest as. Also, I would love it if you could guide me in how to go about learning and developing machine learning skills. I have so far only done some basic java programming but nothing to do with machine learning yet.

  41. fege April 14, 2017 at 4:32 am #

    Blog is very helpful and i guess i’m in 8 🙂

  42. Stephen April 16, 2017 at 9:39 am #

    I had a really hard time fitting myself into any one of these groups:
    I’m a professional software engineer, but my goals for machine learning as VERY far from my day job.
    I love statistics, numbers, and deriving information from data, but I don’t have a math degree and wouldn’t dare calling myself a Data Analyst.
    I want to build systems, but the application to build around doesn’t exist yet. The artificial intelligence aspect is the main in most of those designs.

    Here’s my goal: machine learning and data analysis for AIs for robots and chat bots.
    Also, learning “without the math” doesn’t appeal to me. It’s great if something works, but being incompetent without a tool or library feels like a bad idea to me.


    Sorry for the long comment

    • Jason Brownlee April 17, 2017 at 5:06 am #

      Perhaps ML Mastery is not the best place for you?

      • Praveen September 13, 2017 at 6:34 pm #

        any resources for building AI / ML powered robots Jason ? I feel 8) Engineer Interested In Developing Smarter Software And Services will fit for robotics. What do you say ?

        • Jason Brownlee September 15, 2017 at 12:02 pm #

          Sorry, I don’t have advice about robots. Perhaps start with small table top examples?

  43. Phanindra April 27, 2017 at 8:01 pm #

    Before reading this blog… I had done a bit of research and searched for a text book to get started into ML. First textbook I started with had too much of maths for me (me already being 5 years into my programming career). I was not very satisfied with the book or the approach it had taken. Then I stumbled across the book “Machine Learning in Action” (mentioned above). It proved quite good since it seems to be a book written for seasoned programmers rather than students. I am happy to continue on this path. But my question is this… Even though I have been programming for more than 10 years this is my first time interaction with Python…

    So i was wondering do I go back and learn Python completely before attempting ML?

    • Jason Brownlee April 28, 2017 at 7:39 am #

      No need, you can learn machine learning with your preferred programming language.

      I focus on Python and R on my blog because they are popular.

      Most of the production ML I have built runs in FORTRAN and Java.

  44. Vrushali May 18, 2017 at 8:23 pm #

    Very nice blog! Just curious why there is no girl in the photo? 🙂

    • Jason Brownlee May 19, 2017 at 8:17 am #


      No idea, it was a random creative commons photo. I do not mean to offend in any way at all.

  45. Saugat May 21, 2017 at 9:31 am #

    I am in group 3. Hoping to learn something interesting and creating something cool while doing my UG.

  46. Ramakrishna May 27, 2017 at 4:17 pm #

    I am in 3,4,8 groups

  47. Gagan June 12, 2017 at 12:37 am #

    Machine Learning Researcher Interested in Impacting the Field

  48. Alexvitk June 13, 2017 at 4:28 am #

    i dont know exactly my tribe, may be "Business Person with a General Interest") im a trader on NYSE, i want to automate my strategy with ML and i hope your interesting blog wiil help me) (i`m not developer of course)

  49. Vijayaraj June 22, 2017 at 1:53 pm #

    Appreciate this. Give clarity and focus for those who are about to start the “journey”.

  50. Fan Li June 23, 2017 at 8:27 pm #

    Academy tribe

  51. JTsao June 29, 2017 at 5:02 am #

    This is a great practical guide.
    I thought there might be also classification based on applications that can overlay on top of your guide.
    For example, chatbot for eCommerce is a single purpose development but it involves the currently evolving research in end-to-end neural network, that generally would be best worked by new or recent PhD’s interested in research. This is true with machine vision for self-driving cars that utilizes complex neural networks.

  52. Sonali July 4, 2017 at 2:35 am #

    Jason – I enjoy your blog-posts, and this one is awesome. Thanks for the matching resources (well thought out). I am in Tribe 2 and 8. I guess in today’s world, one has to straddle categories. Thanks and keep up the good work.

  53. Utkarsh Agarwal July 11, 2017 at 5:19 pm #

    Hi Jason

    I feel like I am in group 6 an well as group 10.
    Can these two groups go hand in hand?


  54. Matches Malonee July 14, 2017 at 6:30 am #

    Hi Jason.

    I read your post, and thought about the tribe that I’d be most comfortable in. Unfortunately, I haven’t been able to decide where I belong. I do wish to pursue ML, but I never thought that ML will be such a big umbrella. Honestly, I’m a bit overwhelmed.

    I need some time. I’m hoping that you can recommend some general, non-specific, ML books that I can get started with and then study about a topic that catches my eye, in detail. Are there any texts/resources that every ML student must read? If so, then I’d be grateful if you could tell me about them. At least this way, I’d get into the learning part, without allotting way too much time to just picking out resources.

    • Jason Brownlee July 14, 2017 at 8:36 am #

      Start small and figure it out along the way, like the rest of life.

  55. Purnima July 14, 2017 at 7:07 pm #

    I am a software tester, so as per my understanding i will not belong to any tribe. but i want to learn ML. so how do i start?

  56. rakesh kumar July 21, 2017 at 7:38 pm #

    I am in group 3

  57. Billy July 26, 2017 at 11:58 pm #

    Group 9. Many hours of frustration-meandering-wallowing. Your work is like a well-rested bright clear morning. Simple complete succession of English statements containing the ideas is the most difficult to find.

    If I understand the idea, I can figure out the implementation.

    Instead practitioners are tying to learn themselves with incomplete explanations and academicians are ardently explaining in absence of any context.

    Once self-identified, where does one find tribe-centric material, conversations, best practices?

    Hats off compadre!

    • Jason Brownlee July 27, 2017 at 8:09 am #

      Thanks Billy.

      Well, most of the material out there is for academics and researchers.

      There are some practitioner type material like this blog and some O’Reilly type books.

      My proposal is that once you know what you want/where you fit, that you can use that as a filter to help choose what material will get you closer to your goal.

      Does that help Billy?

  58. Rohan Talesara August 22, 2017 at 2:46 am #

    Amazing post!! This is probably what I was looking for to give my endeavors the correct direction…I belong to the second tribe, and it’s great to know that I’m not alone!

  59. Arnav Simer August 25, 2017 at 1:08 pm #

    I am from Engineer “Interested In Developing Smarter Software And Services”

  60. Shubham Kumar August 31, 2017 at 5:19 am #

    I was wondering how much Data Structures and Algorithms do I need to know if I mainly want to focus on the Machine Learning or Deep Learning aspect of personal software projects.
    Say, if I want to build a Django website that uses ML algorithms to function.
    I have gone through lots of Machine Learning content, but never came across any DSA

    • Jason Brownlee August 31, 2017 at 6:27 am #

      A deeper understanding of algorithms and data structures will help you better understand how algorithms work and how to use them more effectively, but it is not required when getting started.

      You do not need to be a mechanic to dive a car.

  61. Satya Dikshit September 18, 2017 at 11:24 pm #

    Hi Jason,

    I am a systems administrator trying to make a career shift to Machine Learning. But, I don’t fit into any of the tribes that you have listed. I have no prior experience in programming but i have started learning Python on my own. I think I want to belong to tribe 8 (Engineer Interested In Developing Smarter Software And Services) which requires lot of ground work.
    But like you have mentioned in your posts, I will take a leap and see where this goes.

    BTW, Your posts are awesome.

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