How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML

This is a complete guide to start and improve your knowledge of machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques! The complete guide: The GitHub Repository with all the resources: https://medium.com/towards-artificial... https://github.com/louisfb01/start-ma... Follow me for more AI content: Instagram: https://www.instagram.com/whats_ai/ LinkedIn: https://www.linkedin.com/in/whats-ai/ Twitter: https://twitter.com/Whats_AI Facebook: https://www.facebook.com/whats.artifi... Join Our Discord channel, Learn AI Together: https://discord.gg/learnaitogether Chapters: 0:00 - Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 2:33 - How to start 3:45 - YouTube Courses 4:21 - Books and Articles 5:00 - Maths behind ML 6:00 - Programming & Online Courses 7:11 - Practice, practice, and practice... 8:03 - Join Communities 8:45 - Use Cheat sheets 9:04 - How to stay up-to-date 9:51 - Conclusion Video Transcript 00:00 i get asked these two questions multiple 00:02 times a day 00:03 how can i start in machine learning and 00:06 how can i follow the news in ai 00:08 the first one takes multiple forms such 00:10 as how can i start for free 00:12 how can i start if i don't have a 00:14 developer background or how can i start 00:16 without any math 00:18 etc so i decided to do this video to 00:20 answer them 00:21 once and for all of course since i will 00:24 share many resources 00:25 i also wrote a complete guide on how to 00:27 start in machine learning in 2021 00:30 from no background at all and for free 00:32 as well as a github 00:33 repository with all the useful links it 00:36 is linked in the description below 00:38 because of these pertinent questions 00:40 i've researched a lot of resources 00:42 online and i saved the best ones on a 00:44 notepad over the past 00:45 two years to quickly answer the next 00:47 upcoming questions 00:49 today i will share this notepad with 00:50 everyone and list 00:52 many great resources and give you some 00:54 of my personal tips on how to learn and 00:56 improve your machine learning skills 00:58 and how do i stay up to date with all 01:00 the news in the field 01:02 oh and please let me know in the 01:03 comments if you know any other great 01:05 resources that i could add to this guide 01:07 to make this learning process 01:09 easier and better for everyone since 01:11 this video is a bit special 01:13 here are some important time stamps you 01:15 can skip to if you'd like more 01:16 information about a specific subject 01:19 otherwise i will go through this guide 01:20 and show you how you can develop 01:22 great machine learning skills without 01:24 wasting money following your own needs 01:26 one last thing if you are here to know 01:28 how to stay up to date with the news in 01:30 the field 01:31 and learn about the new techniques you 01:33 should consider subscribing to my 01:35 channel 01:35 since i share exactly this type of 01:37 content every week 01:39 this guide is intended for anyone having 01:41 zero or a small background in 01:43 programming 01:44 mathematics or machine learning this is 01:46 why i will list 01:47 resources for all these subjects but 01:49 feel free to go at your pace and learn 01:51 what you want to learn 01:52 also there is no specific order to 01:54 follow here i mainly listed them the way 01:57 i will do it 01:58 but feel free to start with whatever you 02:00 feel you like the most 02:02 final little note before sharing these 02:04 tips with you 02:05 it is super important do what you want 02:07 to do 02:08 if you don't like reading books skip the 02:10 section if you don't want to follow an 02:12 online course 02:13 you can skip this one as well same thing 02:15 for videos there is not a single way to 02:17 become a machine learning expert and 02:19 with motivation 02:20 you can absolutely achieve it by 02:22 creating your own path 02:23 oh and don't be afraid or ashamed to 02:25 replay videos or learn the same concepts 02:28 from multiple sources 02:29 repetition is the key to success in 02:32 learning something new 02:33 now let's dive right into it this 02:36 section is mainly for complete beginners 02:38 in my opinion the best way to start 02:40 learning anything 02:41 is with short videos on youtube and this 02:44 field is no exception 02:46 there are thousands of amazing videos 02:48 and playlists that teach 02:49 important concepts of machine learning 02:51 for free on this platform 02:53 and you should take advantage of them 02:55 here i recommend two playlists to watch 02:57 they will give you a great first 02:59 introduction to the terms you need to 03:00 know to get started in the field 03:02 the first one is my own playlist where i 03:04 explain the most used terms in the field 03:07 which can be very helpful if you are 03:08 just starting and then 03:10 i'd suggest to dive a little deeper into 03:12 the foundations of machine learning and 03:14 deep learning 03:15 and learn more about neural networks 03:18 understanding neural networks and back 03:20 propagation 03:20 is the most important thing when 03:22 starting and gives you an enormous 03:24 advantage 03:25 when you dive into more advanced 03:27 lectures and courses 03:28 for this i will recommend the great 03:30 playlist made by 03:32 three blue one brown now that you have a 03:34 good basis of what a machine learning 03:36 algorithm 03:37 is how it works and how it learns using 03:40 backpropagation you are ready to dive 03:42 even deeper 03:43 with more complete and advanced courses 03:46 this step is a little longer since you 03:48 will be watching 03:48 many hours of free amazing courses on 03:51 youtube 03:51 and learn a lot from them please do not 03:54 watch these courses while doing 03:56 something else 03:57 they are great resources that need 03:58 concentration taking notes and asking 04:01 questions 04:02 through online communities as i will 04:04 talk later in the video 04:05 here i share three courses that i 04:07 followed and loved personally 04:09 which are the ones from mit stanford and 04:12 andrew angie 04:13 again i just want to remind you that all 04:15 the links are in the repository 04:17 linked in the description below so you 04:19 don't have to note them down 04:20 right away as it has been proven 04:22 multiple times 04:23 humans learn better by repeating and 04:26 learning in different ways 04:27 such as hearing writing reading watching 04:30 and etc 04:32 this is why it's as important to read as 04:34 to watch videos for a better 04:36 understanding 04:37 you will cover many angles and have a 04:39 more complete view of what you are 04:41 trying to learn 04:42 this section is a list of short articles 04:44 and books that are completely free and 04:46 optional 04:47 if you are into reading i will suggest 04:49 starting with these five short articles 04:51 and then 04:52 jump into the more advanced books books 04:54 are a great way to learn at your rhythm 04:56 be sure to understand everything before 04:59 going into practice mode 05:01 the final theoretical subject to cover 05:03 here is the mathematics behind machine 05:05 learning 05:06 which are extremely important you can 05:08 always just apply machine 05:10 learning algorithms and tweak it until 05:12 it works but you will never understand 05:14 it correctly and improve it following 05:15 this path 05:16 if you have zero background in math 05:18 don't worry just like 05:20 most things in life you can learn math 05:22 unfortunately for us 05:24 there's an awesome website called can 05:26 academy 05:27 where you can learn many mathematics 05:29 concepts all for free 05:30 here are some great beginner and 05:32 advanced resources to get into the 05:34 machine learning map 05:36 i also listed some great videos and free 05:38 books you can check as well 05:40 more is better i will also suggest 05:42 starting with these three 05:43 very important concepts in machine 05:45 learning linear algebra 05:47 probability and multivariable calculus 05:50 which are the three courses i suggested 05:53 of course 05:53 if some subjects covered here are 05:55 already a bit too advanced for you 05:57 you can always look to complement these 05:59 with other courses 06:01 or books this section is for beginners 06:03 in coding 06:04 if you have no background at all in 06:06 python or any other programming language 06:08 this will get you starting and give you 06:10 an awesome basis for machine learning 06:12 programming 06:13 if you are already pretty familiar with 06:15 python you can skip to the next step 06:17 here i list the best online courses to 06:20 learn the programming side of machine 06:21 learning using python 06:23 and have a great background but you can 06:25 always decide to learn with another 06:27 language 06:28 for sure now that you have a good 06:30 understanding of the theory behind 06:32 machine learning and a coding background 06:34 you are ready to start your way into 06:36 machine learning courses 06:37 of course these are all optional here 06:40 the first one is free 06:41 and the other ones are paying since they 06:44 will teach you 06:44 many things and some even give you 06:46 certifications you can use in your 06:48 resume 06:49 if you don't want to follow any courses 06:51 you can jump to the next section and 06:53 start to practice on your own 06:55 it will be a little more difficult at 06:56 first but with great googling skills 06:59 and motivation you will be able to do 07:01 this for sure 07:02 otherwise if you prefer to have clear 07:05 steps to follow 07:06 these courses are the best ones to do 07:08 starting from the basics to more 07:09 advanced 07:10 from top to bottom practice practice 07:14 and practice the most important thing in 07:16 programming 07:17 is practice and this applies to machine 07:19 learning too 07:20 it can be hard to find a personal 07:22 project to practice on 07:23 but fortunately for us kaggle exists 07:26 this website is full of free courses 07:28 tutorials and competitions you can join 07:30 competitions for free 07:32 and just download their data read about 07:34 their problem 07:35 and start coding and testing right away 07:38 you can even earn money from winning 07:40 competitions and it is a great thing to 07:42 have 07:42 on your resume as well this may be the 07:45 best way to get experience 07:46 while learning a lot and even earn money 07:49 you can also create teams for kaggle 07:51 competition and learn with people 07:54 i definitely suggest you to join a 07:55 community to find a team and learn with 07:58 others it is always better than learning 08:00 alone 08:01 the following section is devoted to this 08:04 indeed 08:04 most of the time the best way to learn 08:06 is to learn with someone else 08:08 join online communities and find 08:10 partners to learn with 08:12 this is the reason why i created a 08:14 discord server a year ago 08:16 with the goal of getting many ai 08:18 enthusiasts and learn together 08:20 ask questions find kaggle team mates 08:23 share your projects and much more 08:25 it is called learn ai together we are 08:27 already more than 8 000 people 08:29 in not even a year i will be glad to see 08:32 you there 08:33 and please reach out to me if you do you 08:35 can also follow reddit communities where 08:37 you can ask questions 08:39 share your projects follow news in the 08:40 field and more 08:42 here i list the most popular ones that i 08:44 follow on a daily basis 08:46 finally i will recommend you to save 08:48 cheat sheets on your computer 08:49 tablet or even print them they are a 08:52 very good way to compress information 08:54 and have it all at hand here i list the 08:56 best cheat sheets i could find 08:58 and as i just said even if you are 09:00 advanced you should definitely have them 09:02 printed somewhere 09:03 near your desk now another important 09:06 thing in this field is to stay up to 09:08 date with the new upcoming papers and 09:10 new applications that are released 09:12 every single day a great way is to join 09:15 linkedin and facebook groups that are 09:16 sharing these new applications 09:18 you can also follow medium publications 09:20 and youtube channels that are 09:22 summarizing these new papers 09:24 of course you are already at a pretty 09:26 good place for this since i personally 09:28 do share news related to ai every week 09:31 so you should definitely subscribe and 09:33 turn on the notifications to not miss 09:35 any future videos newsletters are also a 09:38 great way to have all the news condensed 09:41 at one place 09:42 every day or week here i list a few of 09:44 the best ones i know that i personally 09:46 use in my day to day life but you can 09:48 surely search for more 09:50 in your fields of interest note that 09:52 this is a non-existent list of resources 09:55 you can definitely use more or less 09:57 resources and learn at your rhythm 09:59 you must follow your instinct to find 10:01 the best way you can learn 10:02 don't ever feel guilty about replaying a 10:04 video or reading an article twice to 10:06 understand a concept 10:08 we've all been through this and it is 10:10 perfectly normal 10:11 the most important thing is that you 10:13 understand the concept 10:14 and not that you go through the list as 10:16 quickly as possible let me know in the 10:18 comments if you know any other great 10:20 resources that i could add to this guide 10:22 to make this learning process 10:23 easier and better for everyone please 10:26 leave a like if you went this far in the 10:27 video 10:28 and since there are over 80 percent of 10:30 you guys that are not subscribed 10:32 yet please consider subscribing to the 10:34 channel to not miss any further news 10:36 thank you for watching 10:46 [Music]