Self-Studying Machine Learning? Remind yourself of these 6 things

all right back to you Terry hi it's me where's the jingle jingle all right welcome back to another episode with Daniel Berg self-taught machine learning engineer from today's episode we're going to go through six tips to help you if you're self learning machine learning now I eat all the questions asking how do I know machine learning how do I learn math and machine learning how do I get motivation to study every day and to be honest it's good the answer is going to be different for for every single person because everyone's learning journey is unique however I did write an article the other day I posted on medium the link will be below and I've printed it out now because you know that's what you do when you're a professional newsreader you get papers and continually shuffle them the title of the article was thinking of self learning thinking of self studying machine learning remind yourself of these six things so in this video we're going to go through those six things it won't be the same that article because I've just written there the titles down I'm just going to be ripping them off number one is to get some pipe and foundations now if you if you're just a beginner and you want to get into machine learning and you're thinking of learning it yourself you probably definitely want to start with with learning some code and learning how to run it and now why - you could use a bunch of different other programming languages Java rc2 - all code up machine learning however Python is one of the lowest barriers to entry and what do I mean by that it's that when you read a line of Python code it's very similar to how you would say the same thing in real life now working your loan - when I first started I started learning it on tree house now of course the resources I list there's a bunch of them online and it doesn't really matter where you learn it but my brother's starting to learn it now and I recommended him to use data camp because of their focus on teaching Python for data science and machine learning do you necessarily have to use it no of course not but if you do want to get into machine learning you do have to start learning how to code so you've started learning to code you're about three four months in you've got a bit of - foundations what do you do next well number two and this is beautifully written article by the way I'm kidding number two is to start making things when you're not ready and what do I mean by that well the best learning I've had is working on projects that I didn't necessarily know what the outcome was and so doing online courses is great however there's a structure to them so someone's gone ahead and worked through work through the problems giving you a bit of a scaffolding of how to how to get through and you can go to forums and ask how to how to get through this step and you'll probably find an answer that's well and good to be able to do that sort of research but what happens when you come across something that finding the answer is is a little bit more difficult or in fact there is no answer now the reason I recommend working on things when you're not ready is because that's gonna X even though it might not feel like it when when you begin it's gonna exponentially increase your learning rate because not only will you will you start to build upon those foundations you'll start to learn ah okay I can look at things and think of different ways of solving them even if I don't necessarily know what the outcomes going to be that's a very valuable mindset to have start building things when you're about 70% ready because in reality you'll never be a hundred percent ready number three there's a lot of clutter out there so reduce it what do I mean by that well if you google how to learn machine learning on different machine learning courses you'll probably be overwhelmed by the amount of learning resources out there and they're all great which is which is actually amazing and an amazing time to be getting into to learning machine learning however having too many things to choose from can hold you back from actually choosing one thing it's like when you go to the ice cream store and they have 36 different flavors in your life what I can't choose because there's there's so many different flavors alright so one of the best things I ever did was creating my own custom pathway now that's my you might have seen it before it's my a I master's degree and I wrote an article about it I linked some of the results that I was doing and it's ever-expanding now if you've got some foundations in Python some three of the best resources that I use day to day or that have helped me most day to day as machine learning engineer have been their hands on machine learning book the fast AI machine learning course and the applied data science with Python course on Coursera they're three of the things that relate most most to my job day-to-day as a machine learning engineer and now of course that again is going to differ depending on what you want to work on what your job requires etc but there are just three things you might want to bookmark for after you've got some Python foundations effort in having that was that when I when I thought about trying another course or something like that I was like no I'm gonna stick to my curriculum follow that that'll give me a good set of foundations then I can start building it number four research is necessary but pointless if you can't apply it there's a lot coming out in the machine learning world and deep learning well a I will and data science world and what what I mean by that way you look on medium you look on archive you look everywhere you go there's headlines of new ways to do things and now when you're first beginning that can be incredibly overwhelming as well because at the same time you're trying to get - foundations and all you've got you've got your foundations need trying to try to work on your project and trying to learn a bit more in-depth machine learning stuff and you're getting all this new research state of the art there state of your back how do you keep up well the fact is you can't right now I work on this stuff every day and I I struggle to to keep up so when you're first beginning it began or it ignore all the new stuff unless it's directly relevant to the project you're working on so remember when you're first starting out get the foundations have a set curriculum that you said you're going to follow through and then if you if you've got a project that you're working on maybe then you might want to look at new ways of doing things but in the beginning focus on the foundation number five is a little bit every day now when I when I get frustrated most of the time it's because I'm trying to control things I can't control what do I mean by that well when you're studying what's something that you can't control you can't control how fast you learn or how fast you be able to solve a project sure these are things you can work towards improving but if you're first beginning out learning learning machine learning there's a lot to take in so something you can't necessarily control is how quickly you you grasp the concepts how quickly you can start putting them to play what you can control is how much time you spend on practicing those concepts every day so that's something everyone is has the ability to control and what does that look like I'll say instead of getting up and saying well today I'm gonna nail that new concept that I've been struggling with I'm gonna learn how to build a neural network in pi torch well that's that's a great goal to have but are you necessarily gonna be out of completed in a day maybe if it was a really good day but the opposite of that and the other side the thing that you definitely can't control is getting up and going I'm going to spend three hours working on that that project right that's all you have to do you work for three hours in that project and you've you've completed the goal set the system up so you always win and if you miss a day that's fine all right life happens these things happen when you hear people saying yeah I do this every day sure that's a great but not again not everyone's the same and sometimes things will come up so you miss a day that's fine there's always tomorrow you can get back into it focus on the things you can control not the things you can't control [Music] Perai five down number six this is this is actually really important these last two I've decided I'm throwing a bonus for number seven for the video version but number six is don't beat yourself up for not knowing something at the end of the day who does this help no one especially not you if you're beating yourself up going wow I wish I knew this you go online I'm guilty of this I read articles and I see what people are doing I see people's amazing amazing projects right and the stuff that's coming out of this world is just just incredible I'm going wow why can't I do that well what what you often miss with with with everyone's journey as you see you see that final little little little project right at the end right right at the end so it's got all this work and all you really see is that that last little bit so when you go to the movies right you're watching ninety minutes of of a pristine movie and it's it's gets all the great reviews however when that movie first started out it was probably like hundred hours of footage that's been cut back to being that final little polished product so that's that's important when it's when you're learning something when you're learning something new is - don't beat yourself up don't compare yourself to to other people how far they are ahead sure it's great to use them as inspiration for where you can get to but feeling negative about where you're not and where they are is not going to help your learning journey and I mean people think and Gary you probably want to you know probably put the graphic over here for this one people think learning is a straight line it's more like all over the place right the first year of learning something new is is yeah you suck at it and then the second year year you're better than you were the first year but now you know how much you don't know so you think you suck even more but you're actually actually getting better than the third year well I can't tell you that yet because I'm not there but stay tuned and and this is a bonus and probably really important to I'm just as important as all the others and it's number seven and on the original on the original version of this actually no I did a live stream the other day asking in Q&A and Matias left a comment which was very valuable which is something that I probably should have included in me the original article was what's the rush I it's it's not going to help again this is time back into what you can't control what you can't control and not beating yourself up for things you don't know whenever I feel down whenever I I don't have the motivation to study it's because I'm trying to be too far ahead of where where I'm actually at and now machine learning is in the compass of of programming mathematics statistics probability communication a whole bunch of different fields right and each of them you could spend years learning so combining them and inspecting to be an incredibly good machine learning engineer after a short period of time is kind of kind of working against yourself and that's what I have to remind myself is to have patience to learn these things takes time but it's worth it right there the seven tips that I have for people who are just getting into cell studying machine learning maybe you're just starting out maybe maybe you're getting into it I've been studying self studying machine learning for about two years and now when I say self studying I've spent I definitely have spent a lot of time in this room however everything that I've learned and everything that I've worked on has been because of the incredible work that other people have done and put online and whatnot so self-study is not really self-study it's because I'm standing on the shoulders of giants right and that's a I believe that's from Isaac Newton the the guy who kind of invented capitalists at the age of 23 man why did I invent something by age of 23 and that's at 2.6 right don't beat yourself up the things that that you don't know yet but with any learning journey it's going to be unique will these things apply to you maybe they will maybe they won't if you have something that you would add leave a comment below try and help someone else out otherwise all the best learning machine learning and as always keep learning and back to you Gary PS before you go if you're looking for any of the links that I've said throughout the show they'll be in the description below and if you have any more questions feel free to leave a comment or email me at daniel at mr deburr comm and I'll get back to you as quick as we can

Channel: Daniel Bourke

Views: 90,936

Keywords: machine learning study tips, machine learning, self-studying machine learning, machine learning engineer, how to learn machine learning, daniel bourke, leanring machine learning online, data scientist, tips for studying machine learning online, best machine learning online resources, best online resources for studying machine learning

Id: MD3R9yatou0

Length: 13min 52sec (832 seconds)

Published: Sat Mar 02 2019