Learn Machine Learning Like a GENIUS and Not Waste Time

[00:00] so you want to learn machine learning

[00:01] and you somehow ended up here well I've

[00:03] got good news and bad news the bad news

[00:06] I'm not here to sell you some become an

[00:08] ml engineer in 3 months fantasy could it

[00:10] happen sure people also win the lottery

[00:13] the good news I've been exactly where

[00:16] you are about 8 years ago I thought I

[00:18] knew enough Basics to just apply for

[00:20] jobs and learn the rest while working

[00:22] spoiler alert I failed miserably but

[00:25] those failures taught me exactly what

[00:26] works and what doesn't and that's what

[00:28] I'm sharing today I've since taught all

[00:30] of this to hundreds of students in

[00:31] several countries so I think I have a

[00:33] good idea on what works and what doesn't

[00:35] hopefully I can save you months of

[00:37] frustration by showing you the smart way

[00:39] to learn machine learning learning data

[00:41] science was one of the best decisions I

[00:43] ever made and most of you can do it and

[00:45] you will learn some cool stuff on the

[00:47] way that even if you don't become a data

[00:49] scientist or machine learning engineer

[00:50] you will have learned programming how to

[00:52] build apps how to analyze and visualize

[00:55] data you will have strong statistics and

[00:57] research skills and be able to

[00:58] communicate data clearly

[01:00] many amazing job options will be open to

[01:02] you even if you don't become a data

[01:03] scientist but you will have to work hard

[01:06] but what should you work hard on and how

[01:08] do we even start that's what I'm here to

[01:10] tell you learning how to learn before we

[01:13] even touch machine learning let's talk

[01:15] about something crucial learning how to

[01:17] learn why because machine learning and

[01:20] AI like most things in Tech are

[01:22] constantly evolving and what matters

[01:23] isn't just what you know but how quickly

[01:25] you can adapt and learn new things a

[01:27] little secret I actually suck at at

[01:30] programming and algorithms but I am

[01:31] really good at learning new stuff here's

[01:33] why this matters specifically for

[01:34] machine learning technology changes fast

[01:37] new platforms and Frameworks drop

[01:38] constantly and new papers come out daily

[01:41] what's hot today might be obsolete

[01:42] tomorrow problem solving is everything

[01:45] machine learning isn't about memorizing

[01:46] algorithms it's about understanding data

[01:48] and patterns about breaking down complex

[01:50] problems and finding Creative Solutions

[01:52] confidence AKA don't get overwhelmed or

[01:55] scared of big problems people who know

[01:57] how to learn and problem solve don't get

[01:58] paralyzed when faced with a new big

[02:00] problem they develop a strategy for how

[02:02] to look at a new problem and break it

[02:04] down into manageable problems they have

[02:05] faced before they know how to look up

[02:07] Solutions and find tools necessary to

[02:09] solve new problems they adapt more

[02:11] quickly when Tech changes efficiency if

[02:14] you know how to learn you won't waste

[02:16] time on unnecessary things time is money

[02:19] learn what you actually need to get

[02:21] where you want to be there's no one size

[02:23] fits all solution for learning something

[02:25] it depends on your style of learning but

[02:26] also on your goals not everyone needs to

[02:29] learn everything so how do you learn how

[02:31] to learn this one you kind of have to

[02:33] figure out for yourself because what

[02:35] works for one person doesn't necessarily

[02:37] work for the next some people learn well

[02:39] with graphs and diagrams others with

[02:41] text others maybe with voice notes some

[02:43] people need to understand the theory

[02:44] before applying it others need to jump

[02:46] right in and use an algorithm before

[02:48] asking what it actually does in this

[02:51] video I will try to show you what worked

[02:52] for me while giving you resources that I

[02:54] believe will get you there as quickly as

[02:55] possible I will just mention a principle

[02:57] that has helped me a lot throughout my

[02:59] career the the Paro principle sometimes

[03:01] called 8020 principle it says that 80%

[03:04] of the results come from 20% of the

[03:06] effort constantly ask yourself why am I

[03:09] doing this is this actually getting me

[03:11] where I want to be or can I do something

[03:13] more useful with my time well the answer

[03:15] to this question isn't always the same

[03:17] for everyone I will try to now give you

[03:19] the 20% of the work that would have

[03:20] gotten me 80% of the way to becoming a

[03:23] data scientist adapt as needed but where

[03:25] do I start now let's build your machine

[03:28] Learning Foundation the right way

[03:30] here's your road map python while the

[03:33] next skill is at least as important I

[03:34] would start with learning python the

[03:36] main reason is that you will get a

[03:37] feeling of achievement fairly quickly

[03:39] and python is super simple why python

[03:42] python is the main language of data

[03:44] analytics data science and machine

[03:45] learning while also being a full-fledged

[03:47] programming language allowing you to

[03:49] write scripts build apps and websites

[03:51] and much more python will allow you to

[03:53] actually start writing real code within

[03:55] days without having to learn super

[03:57] complicated computer science Concepts

[03:59] like pointers memory allocation and

[04:00] garbage collection also with python you

[04:03] will be able to get a job as a

[04:04] programmer or data analyst or web

[04:06] developer even if you don't learn all

[04:08] the hard machine learning stuff I

[04:10] suggest you first install jupyter

[04:12] notebooks as they make learning much

[04:13] easier and jupyter notebooks are also a

[04:15] core tool for data analysts and data

[04:17] scientists all over the world then learn

[04:19] about these Core Concepts programming

[04:21] fundamentals basic syntax indentation

[04:24] rules comments and so on variables math

[04:26] if else Loops printing data types like

[04:29] strings ins floats booleans lists

[04:31] dictionaries functions classes and

[04:33] objects modules packages and importing

[04:36] do a pandas tutorial pandas is Python's

[04:38] primary data manipulation Library built

[04:40] for handling tabular data through data

[04:42] frame objects imagine it as Excel

[04:44] spreadsheets on steroids it will be your

[04:46] main tool for data analysis cleaning and

[04:48] transformation with powerful functions

[04:50] for merging reshaping and analyzing data

[04:53] the library strength lies in combining

[04:56] the power of numpy arrays with

[04:57] spreadsheet like functionality and SQL

[05:00] database like joints it also comes with

[05:03] built-in plotting functionality built on

[05:04] top of Python's powerful met plot lib

[05:06] libraries pandas is a true data analysis

[05:09] Powerhouse if you truly Master pandas

[05:11] you will excel at most data analysis

[05:13] positions in the world also because

[05:15] exploratory data analysis and data

[05:17] preparation are about 60 to 80% of a

[05:19] data scientist job it will also lay the

[05:21] foundation for that 8020 principle

[05:24] remember your First Data analysis

[05:26] project so before you get into any

[05:28] machine learning I would take the time

[05:30] here to work on an actual project to

[05:31] deepen your python pandas and data

[05:33] analysis knowledge as I mentioned in my

[05:35] previous videos real projects beat

[05:37] tutorials at developing a good data

[05:39] scientist find some data you want to

[05:41] analyze maybe from one of your old jobs

[05:43] or school maybe you can export some data

[05:45] from your favorite health tracker or ask

[05:47] some friends if they have some data they

[05:49] want analyzed or maybe download public

[05:51] data from the government the World Bank

[05:53] or a nonprofit any topic you're

[05:55] interested in it could be economics

[05:58] Sports politics video games the board

[05:59] games this last one was a passion of one

[06:02] of my former students work on importing

[06:04] the data into pandas clean up the data

[06:07] make the units uniform decide what to do

[06:09] about missing data and outliers plot the

[06:11] different variables look at correlations

[06:13] between variables and come up with some

[06:15] hypothesis about the data and test them

[06:17] by making more plots turn your results

[06:19] into a slideshow with nice graphs that

[06:21] tell a story that you can present to

[06:22] friends and family Pro tip Jupiter

[06:24] notebooks with data and plots can be

[06:26] turned directly into a slideshow this

[06:28] will also be the first project for your

[06:29] port portfolio which you can show when

[06:30] applying for jobs as a data analyst

[06:33] essential math for machine learning this

[06:36] might be the part that most of you fear

[06:37] the most but I think it is the most

[06:38] important part for anyone wanting to

[06:40] learn machine learning you should take

[06:42] this seriously you don't need to be a

[06:44] math genius or know about all of math to

[06:46] become good at machine learning but you

[06:48] need to really understand the Core

[06:49] Concepts from the areas I'm about to

[06:51] mention for more details on math for

[06:53] machine learning check out my video on

[06:54] the topic basic statistics and

[06:57] probability this for me is the most

[06:59] important Branch as a data analyst and

[07:01] data scientist now there are many online

[07:03] resources for statistics but I highly

[07:05] suggest taking the Con Academy

[07:07] statistics and probability course this

[07:09] course is completely free and is the one

[07:11] I took when I prepared for my first job

[07:12] as a data scientist the full course is

[07:14] probably around 50 hours of content so

[07:16] if you have prior math knowledge you

[07:18] probably won't spend more than 100 hours

[07:19] on this but it might take you longer

[07:21] that's around 2 3 weeks of full-time

[07:23] self-study more if much of this is

[07:25] completely new to you but please take

[07:27] the time to do this it will make

[07:29] everything that follows so much easier

[07:30] and save you much more than 100 hours of

[07:32] headaches later on ideally while you

[07:35] learn new Concepts here you go to your

[07:37] data set from the previous data analysis

[07:39] project phase and try to apply them

[07:40] there to deepen your intuition linear

[07:42] algebra

[07:44] fundamentals while also important linear

[07:46] algebra for machine learning is much

[07:47] more about learning some tools and rules

[07:49] this should be much quicker than

[07:50] learning probability and statistics

[07:52] Concepts the main thing you want to

[07:54] learn is how to operate with vectors and

[07:56] matrices and learn what the different

[07:57] operations mean this is more about

[07:59] mathem iCal tools and notations than

[08:00] Concepts I think learning this will take

[08:03] about a quarter to a third of the time

[08:05] it took you to learn the statistics

[08:06] Concepts so one or two weeks of studying

[08:09] should be enough for people with prior

[08:10] math

[08:11] knowledge I will also leave the link to

[08:13] the Khan Academy linear algebra course

[08:15] in the description calculus here again

[08:18] it's about learning some tools but also

[08:20] understanding what derivatives are

[08:21] conceptually and how they help in

[08:22] optimization problems you should really

[08:24] understand how functions and their

[08:26] derivatives work and know the basic

[08:27] rules of differentiation like the chain

[08:29] rule I would again calculate with one or

[08:31] two weeks if you have prior math

[08:33] knowledge I will also leave a con

[08:34] Academy Link in the description Pro tip

[08:38] you need working knowledge not a math

[08:40] PhD focus on intuition over proofs spend

[08:44] most your time on statistical concepts

[08:46] for linear algebra and calculus focus on

[08:48] learning the tools like Matrix

[08:49] operations and how to take the

[08:51] derivative of a function the core

[08:53] machine learning Concepts and algorithms

[08:56] now here's where many people mess up

[08:58] they jump straight to Deep learning but

[09:00] that's a mistake in my opinion you

[09:02] should spend most of your time on simple

[09:03] algorithms the reasons for that are

[09:05] manifold and discussed in my previous

[09:07] videos but basically many problems don't

[09:09] require complicated solution simple

[09:12] algorithms like linear regression are

[09:13] quicker to run they're more

[09:15] generalizable more interpretable and

[09:17] easier to learn from and communicate and

[09:19] more importantly these algorithms form

[09:21] the basis for the more complicated

[09:23] algorithms like neural networks so truly

[09:25] understanding them will help you

[09:27] understand the more complicated

[09:28] algorithms better too check out my video

[09:30] on machine learning algorithms but

[09:32] basically before getting into neural

[09:34] networks or even svm make sure you

[09:36] understand how linear regression and

[09:37] logistic regression work then look at

[09:39] decision trees and Ensemble algorithms

[09:42] like random forests and gradient

[09:43] boosting I learned most of these topics

[09:45] from the book an introduction to

[09:47] statistical learning the majority of you

[09:49] will prefer learning from videos so just

[09:51] watch the Youtube video series about

[09:53] this book by the authors themselves

[09:55] completely for free on YouTube I will

[09:58] leave a link in the description

[09:59] all the videos together are about 20

[10:01] hours but since you will want to pause

[10:03] and take notes and read up on certain

[10:05] Concepts I think this will be another

[10:07] 100 hours or so of study time so that

[10:09] should be another two or more weeks of

[10:11] full-time self-study all these numbers

[10:13] are estimates as everyone learns at

[10:15] different speeds do a scikit learn

[10:17] tutorial scit learn is the number one

[10:19] machine learning library in the world

[10:21] for basic machine learning algorithms

[10:23] you can do a basic sklearn tutorial in a

[10:25] day or two and the good thing is that

[10:27] the simple and consistent syntax makes

[10:29] it such that once you know how to use

[10:31] the library for one algorithm you know

[10:33] how to use it for any algorithm as long

[10:35] as you know what algorithm is meant for

[10:36] what which you now know because you just

[10:38] learned it pyit learn also comes with

[10:41] great documentation and toy data sets to

[10:43] play around with I suggest you start

[10:44] using psyit learn while you are learning

[10:46] about the algorithms in the statistical

[10:48] learning course the genius move while

[10:51] you learn about the theory behind new

[10:52] algorithms for example going through the

[10:54] statistical learning course and starting

[10:56] with linear regression Implement and use

[10:58] the algorithm in the following three

[10:59] three ways implemented from scratch

[11:01] using basic python implemented using

[11:04] scikit learn using a toy data

[11:07] set then use both your own

[11:09] implementation and the syit learn

[11:10] toolkit to try out the algorithm on a

[11:12] real data set that you have prepared

[11:13] yourself now there's a common Pitfall

[11:15] that many beginners get stuck in

[11:17] tutorial hell it's where you essentially

[11:20] just keep following tutorials without

[11:21] striking out on your own and actually

[11:23] building something most learning comes

[11:25] from the trial and error of building an

[11:26] application so if you always follow a

[11:28] tutorial you get Stu at a basic level

[11:31] how do you not get stuck there do just

[11:32] one or two tutorials per area Max and

[11:35] then work on a real project your first

[11:37] machine learning project here you can

[11:39] either continue with your data analysis

[11:41] project from before or find a new more

[11:43] interesting data set for a machine

[11:44] learning project but don't forget to

[11:46] still use pandas to do an exploratory

[11:48] data analysis to prepare your data for

[11:50] modeling and form hypothesis about your

[11:52] data more often than not the goal of a

[11:55] machine learning project will be to

[11:56] predict some variable from other

[11:57] variables research the industry of your

[11:59] project a bit look at the data and make

[12:02] some hypothesis about what might

[12:03] influence your target variable either

[12:05] based on intuition about the industry or

[12:07] from looking at correlations and Scatter

[12:10] Plots of different

[12:11] variables design new features based on

[12:13] your knowledge of the problem then start

[12:15] modeling but start with simple

[12:16] algorithms like linear regression

[12:18] logistic regression and decision trees

[12:21] then move on to more complex algorithms

[12:22] like svm random forests or gradient

[12:25] boosting note how as complexity

[12:26] increases accuracy usually increases but

[12:29] interpretability decreases your goal is

[12:31] usually to find a sweet spot also don't

[12:33] forget over fitting as you increase in

[12:35] model complexity keep validation and

[12:37] test sets aside before starting to model

[12:39] and compare your models using the test

[12:41] set at the very end many times the more

[12:44] complex algorithms don't look as good

[12:45] anymore once you use the final test set

[12:48] it might be a good idea to work with

[12:50] data sets that have been published on

[12:51] sites like kaggle to then compare your

[12:53] Solutions and accuracies to other

[12:54] people's Solutions and get an idea of

[12:57] how well your models are in comparison

[12:58] to others but don't get frustrated a lot

[13:01] of people on kaggle are professionals

[13:03] with years of experience if you get

[13:05] anywhere close in accuracy you should be

[13:07] happy don't know what to work on you can

[13:09] start with a tutorial but instead of

[13:11] following it directly after building the

[13:12] core features add some features change

[13:15] some features swap out the data set and

[13:17] try to break your code and then fix it

[13:19] this is one of the best ways to learn

[13:21] while not getting stuck in tutorial hell

[13:24] collaborate and share your projects with

[13:25] others learning ML and isolation is the

[13:28] slowest way to learn instead find coding

[13:30] buddies to work on a project with

[13:32] present your work to friends and family

[13:33] or post it publicly on GitHub or in

[13:35] machine learning communities have

[13:37] someone more advanced than you give you

[13:38] feedback this will speed up your

[13:40] learning 10 times don't know anyone to

[13:42] work on a project with participate in a

[13:44] hackathon or write to people with

[13:46] similar interests on kaggle GitHub

[13:48] Discord Reddit LinkedIn Etc the

[13:50] connections you form this way will not

[13:52] only help you learn better but boost

[13:53] your career in unexpected ways check out

[13:55] my most recent video to learn more about

[13:57] the importance of networking and data

[13:59] science Advanced topics only now should

[14:01] you look at more advanced topics deep

[14:03] learning architectures cnns for computer

[14:06] vision RNN for sequential data or

[14:08] Transformers for NLP Advanced

[14:10] optimization techniques model deployment

[14:12] strategies and the latest research

[14:14] papers remember learn these by need not

[14:17] by fomo you don't need to know

[14:19] everything just learn these techniques

[14:20] if they are important to your project

[14:22] here some dos and don'ts don't don't get

[14:25] stuck in tutorial hell don't try to

[14:27] memorize everything don't learn in

[14:29] isolation don't chase every new trend

[14:32] don't copypaste code without

[14:33] understanding don't try to learn every

[14:35] new fancy tool or research paper instead

[14:38] build real projects focus on

[14:40] understanding share your progress join

[14:42] communities Master fundamentals first

[14:46] Implement from scratch learn by doing if

[14:49] you found this video helpful share it

[14:50] with someone who you think might also

[14:52] like it and get started on one of the

[14:54] tutorials in the description or on this

[14:56] very Channel also consider liking the

[14:58] video and subscri subcribing to be

[14:59] notified about similar content in the

[15:01] future thanks for watching