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: https://medium.com/towards-artificial...
​
The GitHub Repository with all the resources: https://github.com/louisfb01/start-ma...​

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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]