---
title: 'Learn Machine Learning Like a GENIUS and Not Waste Time'
source: 'https://youtube.com/watch?v=qNxrPri1V0I'
video_id: 'qNxrPri1V0I'
date: 2026-06-16
duration_sec: 0
---

# Learn Machine Learning Like a GENIUS and Not Waste Time

> Source: [Learn Machine Learning Like a GENIUS and Not Waste Time](https://youtube.com/watch?v=qNxrPri1V0I)

## Summary

This video provides a practical, no-nonsense roadmap for learning machine learning efficiently, based on the speaker's eight years of experience and teaching hundreds of students. It emphasizes learning how to learn, focusing on the 20% of effort that yields 80% of results, and avoiding common pitfalls like tutorial hell. The roadmap covers Python, pandas, essential math, core ML algorithms, and the importance of real projects and collaboration.

### Key Points

- **Personal Failure as a Learning Tool** [0:16] — The speaker failed initially when trying to learn ML by just applying for jobs, but those failures taught them what works.
- **Transferable Skills from Learning ML** [0:47] — Even if you don't become a data scientist, you'll gain valuable skills in programming, data analysis, statistics, and communication.
- **Learn How to Learn** [1:12] — Learning how to learn is crucial because technology evolves fast; focus on adaptability and problem-solving over memorization.
- **Pareto Principle (80/20)** [3:00] — 80% of results come from 20% of effort; constantly ask if your current activity is the most useful use of your time.
- **Python and Pandas Foundation** [3:30] — Start with Python and Jupyter Notebooks; learn fundamentals, then master pandas for data manipulation.
- **First Data Analysis Project** [5:25] — Work on a real data analysis project before ML; find a dataset, clean it, explore correlations, and present findings.
- **Essential Math for ML** [6:34] — Essential math: statistics & probability (most important), linear algebra (tools), calculus (derivatives for optimization).
- **Core ML Algorithms First** [8:56] — Master simple algorithms (linear regression, logistic regression, decision trees) before deep learning; they are more interpretable and form the basis for complex ones.
- **Recommended Learning Resource** [9:45] — Use the book 'An Introduction to Statistical Learning' and its free video series; implement algorithms from scratch and with scikit-learn.
- **Avoid Tutorial Hell** [11:15] — Avoid tutorial hell by building real projects; collaborate with others, share work, and get feedback.
- **Advanced Topics: Learn by Need** [14:00] — Only after mastering fundamentals should you explore advanced topics like deep learning, and only if your project requires them.

## Transcript

so you want to learn machine learning
and you somehow ended up here well I've
got good news and bad news the bad news
I'm not here to sell you some become an
ml engineer in 3 months fantasy could it
happen sure people also win the lottery
the good news I've been exactly where
you are about 8 years ago I thought I
knew enough Basics to just apply for
jobs and learn the rest while working
spoiler alert I failed miserably but
those failures taught me exactly what
works and what doesn't and that's what
I'm sharing today I've since taught all
of this to hundreds of students in
several countries so I think I have a
good idea on what works and what doesn't
hopefully I can save you months of
frustration by showing you the smart way
to learn machine learning learning data
science was one of the best decisions I
ever made and most of you can do it and
you will learn some cool stuff on the
way that even if you don't become a data
scientist or machine learning engineer
you will have learned programming how to
build apps how to analyze and visualize
data you will have strong statistics and
research skills and be able to
communicate data clearly
many amazing job options will be open to
you even if you don't become a data
scientist but you will have to work hard
but what should you work hard on and how
do we even start that's what I'm here to
tell you learning how to learn before we
even touch machine learning let's talk
about something crucial learning how to
learn why because machine learning and
AI like most things in Tech are
constantly evolving and what matters
isn't just what you know but how quickly
you can adapt and learn new things a
little secret I actually suck at at
programming and algorithms but I am
really good at learning new stuff here's
why this matters specifically for
machine learning technology changes fast
new platforms and Frameworks drop
constantly and new papers come out daily
what's hot today might be obsolete
tomorrow problem solving is everything
machine learning isn't about memorizing
algorithms it's about understanding data
and patterns about breaking down complex
problems and finding Creative Solutions
confidence AKA don't get overwhelmed or
scared of big problems people who know
how to learn and problem solve don't get
paralyzed when faced with a new big
problem they develop a strategy for how
to look at a new problem and break it
down into manageable problems they have
faced before they know how to look up
Solutions and find tools necessary to
solve new problems they adapt more
quickly when Tech changes efficiency if
you know how to learn you won't waste
time on unnecessary things time is money
learn what you actually need to get
where you want to be there's no one size
fits all solution for learning something
it depends on your style of learning but
also on your goals not everyone needs to
learn everything so how do you learn how
to learn this one you kind of have to
figure out for yourself because what
works for one person doesn't necessarily
work for the next some people learn well
with graphs and diagrams others with
text others maybe with voice notes some
people need to understand the theory
before applying it others need to jump
right in and use an algorithm before
asking what it actually does in this
video I will try to show you what worked
for me while giving you resources that I
believe will get you there as quickly as
possible I will just mention a principle
that has helped me a lot throughout my
career the the Paro principle sometimes
called 8020 principle it says that 80%
of the results come from 20% of the
effort constantly ask yourself why am I
doing this is this actually getting me
where I want to be or can I do something
more useful with my time well the answer
to this question isn't always the same
for everyone I will try to now give you
the 20% of the work that would have
gotten me 80% of the way to becoming a
data scientist adapt as needed but where
do I start now let's build your machine
Learning Foundation the right way
here's your road map python while the
next skill is at least as important I
would start with learning python the
main reason is that you will get a
feeling of achievement fairly quickly
and python is super simple why python
python is the main language of data
analytics data science and machine
learning while also being a full-fledged
programming language allowing you to
write scripts build apps and websites
and much more python will allow you to
actually start writing real code within
days without having to learn super
complicated computer science Concepts
like pointers memory allocation and
garbage collection also with python you
will be able to get a job as a
programmer or data analyst or web
developer even if you don't learn all
the hard machine learning stuff I
suggest you first install jupyter
notebooks as they make learning much
easier and jupyter notebooks are also a
core tool for data analysts and data
scientists all over the world then learn
about these Core Concepts programming
fundamentals basic syntax indentation
rules comments and so on variables math
if else Loops printing data types like
strings ins floats booleans lists
dictionaries functions classes and
objects modules packages and importing
do a pandas tutorial pandas is Python's
primary data manipulation Library built
for handling tabular data through data
frame objects imagine it as Excel
spreadsheets on steroids it will be your
main tool for data analysis cleaning and
transformation with powerful functions
for merging reshaping and analyzing data
the library strength lies in combining
the power of numpy arrays with
spreadsheet like functionality and SQL
database like joints it also comes with
built-in plotting functionality built on
top of Python's powerful met plot lib
libraries pandas is a true data analysis
Powerhouse if you truly Master pandas
you will excel at most data analysis
positions in the world also because
exploratory data analysis and data
preparation are about 60 to 80% of a
data scientist job it will also lay the
foundation for that 8020 principle
remember your First Data analysis
project so before you get into any
machine learning I would take the time
here to work on an actual project to
deepen your python pandas and data
analysis knowledge as I mentioned in my
previous videos real projects beat
tutorials at developing a good data
scientist find some data you want to
analyze maybe from one of your old jobs
or school maybe you can export some data
from your favorite health tracker or ask
some friends if they have some data they
want analyzed or maybe download public
data from the government the World Bank
or a nonprofit any topic you're
interested in it could be economics
Sports politics video games the board
games this last one was a passion of one
of my former students work on importing
the data into pandas clean up the data
make the units uniform decide what to do
about missing data and outliers plot the
different variables look at correlations
between variables and come up with some
hypothesis about the data and test them
by making more plots turn your results
into a slideshow with nice graphs that
tell a story that you can present to
friends and family Pro tip Jupiter
notebooks with data and plots can be
turned directly into a slideshow this
will also be the first project for your
port portfolio which you can show when
applying for jobs as a data analyst
essential math for machine learning this
might be the part that most of you fear
the most but I think it is the most
important part for anyone wanting to
learn machine learning you should take
this seriously you don't need to be a
math genius or know about all of math to
become good at machine learning but you
need to really understand the Core
Concepts from the areas I'm about to
mention for more details on math for
machine learning check out my video on
the topic basic statistics and
probability this for me is the most
important Branch as a data analyst and
data scientist now there are many online
resources for statistics but I highly
suggest taking the Con Academy
statistics and probability course this
course is completely free and is the one
I took when I prepared for my first job
as a data scientist the full course is
probably around 50 hours of content so
if you have prior math knowledge you
probably won't spend more than 100 hours
on this but it might take you longer
that's around 2 3 weeks of full-time
self-study more if much of this is
completely new to you but please take
the time to do this it will make
everything that follows so much easier
and save you much more than 100 hours of
headaches later on ideally while you
learn new Concepts here you go to your
data set from the previous data analysis
project phase and try to apply them
there to deepen your intuition linear
algebra
fundamentals while also important linear
algebra for machine learning is much
more about learning some tools and rules
this should be much quicker than
learning probability and statistics
Concepts the main thing you want to
learn is how to operate with vectors and
matrices and learn what the different
operations mean this is more about
mathem iCal tools and notations than
Concepts I think learning this will take
about a quarter to a third of the time
it took you to learn the statistics
Concepts so one or two weeks of studying
should be enough for people with prior
math
knowledge I will also leave the link to
the Khan Academy linear algebra course
in the description calculus here again
it's about learning some tools but also
understanding what derivatives are
conceptually and how they help in
optimization problems you should really
understand how functions and their
derivatives work and know the basic
rules of differentiation like the chain
rule I would again calculate with one or
two weeks if you have prior math
knowledge I will also leave a con
Academy Link in the description Pro tip
you need working knowledge not a math
PhD focus on intuition over proofs spend
most your time on statistical concepts
for linear algebra and calculus focus on
learning the tools like Matrix
operations and how to take the
derivative of a function the core
machine learning Concepts and algorithms
now here's where many people mess up
they jump straight to Deep learning but
that's a mistake in my opinion you
should spend most of your time on simple
algorithms the reasons for that are
manifold and discussed in my previous
videos but basically many problems don't
require complicated solution simple
algorithms like linear regression are
quicker to run they're more
generalizable more interpretable and
easier to learn from and communicate and
more importantly these algorithms form
the basis for the more complicated
algorithms like neural networks so truly
understanding them will help you
understand the more complicated
algorithms better too check out my video
on machine learning algorithms but
basically before getting into neural
networks or even svm make sure you
understand how linear regression and
logistic regression work then look at
decision trees and Ensemble algorithms
like random forests and gradient
boosting I learned most of these topics
from the book an introduction to
statistical learning the majority of you
will prefer learning from videos so just
watch the Youtube video series about
this book by the authors themselves
completely for free on YouTube I will
leave a link in the description
all the videos together are about 20
hours but since you will want to pause
and take notes and read up on certain
Concepts I think this will be another
100 hours or so of study time so that
should be another two or more weeks of
full-time self-study all these numbers
are estimates as everyone learns at
different speeds do a scikit learn
tutorial scit learn is the number one
machine learning library in the world
for basic machine learning algorithms
you can do a basic sklearn tutorial in a
day or two and the good thing is that
the simple and consistent syntax makes
it such that once you know how to use
the library for one algorithm you know
how to use it for any algorithm as long
as you know what algorithm is meant for
what which you now know because you just
learned it pyit learn also comes with
great documentation and toy data sets to
play around with I suggest you start
using psyit learn while you are learning
about the algorithms in the statistical
learning course the genius move while
you learn about the theory behind new
algorithms for example going through the
statistical learning course and starting
with linear regression Implement and use
the algorithm in the following three
three ways implemented from scratch
using basic python implemented using
scikit learn using a toy data
set then use both your own
implementation and the syit learn
toolkit to try out the algorithm on a
real data set that you have prepared
yourself now there's a common Pitfall
that many beginners get stuck in
tutorial hell it's where you essentially
just keep following tutorials without
striking out on your own and actually
building something most learning comes
from the trial and error of building an
application so if you always follow a
tutorial you get Stu at a basic level
how do you not get stuck there do just
one or two tutorials per area Max and
then work on a real project your first
machine learning project here you can
either continue with your data analysis
project from before or find a new more
interesting data set for a machine
learning project but don't forget to
still use pandas to do an exploratory
data analysis to prepare your data for
modeling and form hypothesis about your
data more often than not the goal of a
machine learning project will be to
predict some variable from other
variables research the industry of your
project a bit look at the data and make
some hypothesis about what might
influence your target variable either
based on intuition about the industry or
from looking at correlations and Scatter
Plots of different
variables design new features based on
your knowledge of the problem then start
modeling but start with simple
algorithms like linear regression
logistic regression and decision trees
then move on to more complex algorithms
like svm random forests or gradient
boosting note how as complexity
increases accuracy usually increases but
interpretability decreases your goal is
usually to find a sweet spot also don't
forget over fitting as you increase in
model complexity keep validation and
test sets aside before starting to model
and compare your models using the test
set at the very end many times the more
complex algorithms don't look as good
anymore once you use the final test set
it might be a good idea to work with
data sets that have been published on
sites like kaggle to then compare your
Solutions and accuracies to other
people's Solutions and get an idea of
how well your models are in comparison
to others but don't get frustrated a lot
of people on kaggle are professionals
with years of experience if you get
anywhere close in accuracy you should be
happy don't know what to work on you can
start with a tutorial but instead of
following it directly after building the
core features add some features change
some features swap out the data set and
try to break your code and then fix it
this is one of the best ways to learn
while not getting stuck in tutorial hell
collaborate and share your projects with
others learning ML and isolation is the
slowest way to learn instead find coding
buddies to work on a project with
present your work to friends and family
or post it publicly on GitHub or in
machine learning communities have
someone more advanced than you give you
feedback this will speed up your
learning 10 times don't know anyone to
work on a project with participate in a
hackathon or write to people with
similar interests on kaggle GitHub
Discord Reddit LinkedIn Etc the
connections you form this way will not
only help you learn better but boost
your career in unexpected ways check out
my most recent video to learn more about
the importance of networking and data
science Advanced topics only now should
you look at more advanced topics deep
learning architectures cnns for computer
vision RNN for sequential data or
Transformers for NLP Advanced
optimization techniques model deployment
strategies and the latest research
papers remember learn these by need not
by fomo you don't need to know
everything just learn these techniques
if they are important to your project
here some dos and don'ts don't don't get
stuck in tutorial hell don't try to
memorize everything don't learn in
isolation don't chase every new trend
don't copypaste code without
understanding don't try to learn every
new fancy tool or research paper instead
build real projects focus on
understanding share your progress join
communities Master fundamentals first
Implement from scratch learn by doing if
you found this video helpful share it
with someone who you think might also
like it and get started on one of the
tutorials in the description or on this
very Channel also consider liking the
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future thanks for watching