How to learn Machine Learning (ML/AI Roadmap 2024)

Summary

In this machine learning course introduction, Kylie Ying outlines a roadmap for effectively learning the field. She emphasizes the importance of foundational math, including probability, statistics, calculus, and linear algebra, as well as programming skills, particularly in Python. The course covers core machine learning concepts, types of learning, and数据处理. It also touches on various models like neural networks and CNNs, and concludes with the necessity of practice, research, and community engagement for expertise.

Takeaways

• 📚 Start with a strong foundation in basic math, including probability and statistics, calculus, and linear algebra, to understand the theory behind machine learning.
• 🧠 Probability and statistics are essential for making predictions and understanding the most probable outcomes in machine learning.
• 🔍 Learn about concepts like conditional probability, Bayes' Rule, and statistical distributions to model the unpredictable world effectively.
• 📈 Calculus, particularly optimization problems and gradient descent, is vital for training models in machine learning.
• 📉 Derivatives play a crucial role in adjusting parameters to achieve desired outcomes in machine learning models.
• 🔡 Linear algebra is key for handling large-scale data computations and operations in machine learning.
• 🤖 Programming skills, especially in Python, are necessary for implementing machine learning models.
• 🐍 Python is favored for machine learning due to its popularity, extensive documentation, and rich library ecosystem.
• 🔧 Understand programming concepts such as variables, functions, classes, and how to utilize libraries like pandas, numpy, and machine learning frameworks.
• 🌟 Explore core machine learning concepts including types of machine learning (supervised, unsupervised, reinforcement), tasks (classification, regression), and models (KNN, logistic regression, SVM, etc.).
• 🔧 Data is crucial in machine learning; understand data types, the importance of training, validation, and testing datasets, and data manipulation techniques like cleaning, scaling, and feature engineering.
• 👨‍🏫 Practice and research are key to deepening expertise in machine learning; engage with projects, datasets, and communities like Kaggle, and read research papers to stay updated.

Q & A

What is the primary focus of Kylie Ying's machine learning course?

The primary focus of Kylie Ying's machine learning course is to teach students how to learn machine learning effectively by providing a roadmap that covers concepts from the fundamentals to becoming an expert in the field.

Why is foundational math important in machine learning?

Foundational math is important in machine learning because it provides the necessary understanding of probability, statistics, calculus, and linear algebra, which are essential for modeling and predicting outcomes in the unpredictable world.

What are the two main areas of mathematics that Kylie Ying emphasizes as foundational for machine learning?

The two main areas of mathematics emphasized are probability and statistics, and calculus. These areas are crucial for understanding predictions, optimization problems, and the behavior of machine learning models.

How does the Brilliant platform help in learning foundational math for machine learning?

The Brilliant platform helps by offering thousands of interactive lessons that allow learners to gain an intuition in different areas of math, data analysis, programming, and AI, focusing on hands-on problem-solving and critical thinking skills.

What programming language does Kylie Ying recommend for beginners in machine learning?

Kylie Ying recommends Python for beginners in machine learning due to its popularity, extensive documentation, supportive resources, and great libraries for processing data and working with models.

Why is Python considered a good starting point for learning machine learning?

Python is considered a good starting point because it is widely used in the machine learning community, has a vast ecosystem of libraries, and is beginner-friendly, making it easier to prototype and experiment with machine learning models.

What are the core concepts Kylie Ying mentions in the machine learning roadmap?

The core concepts mentioned include understanding what machine learning is, types of machine learning, tasks such as classification and regression, the importance of data, data manipulation techniques, various machine learning models, neural networks, and training and evaluating models.

What is the significance of understanding data types and data manipulation in machine learning?

Understanding data types and manipulation is significant because it impacts the quality and relevance of the data fed into machine learning models. Good data practices ensure that the models are trained effectively and produce accurate predictions.

What are some of the machine learning models Kylie Ying lists in the script?

Some of the machine learning models listed include K-nearest neighbors, logistic regression, support vector machines (SVM), linear regression, neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), GRUs, LSTMs, and principal component analysis (PCA).

How does Kylie Ying suggest one becomes an expert in machine learning?

Kylie Ying suggests that becoming an expert in machine learning involves a combination of practice, research, and learning from experts. This includes working on projects, using resources like the UCI machine learning repository and Kaggle, reading papers, and implementing the findings to gain deep expertise in a chosen area.

Outlines

00:00

📚 Introduction to Machine Learning Course

Kylie Ying introduces her machine learning course, focusing on teaching effective learning strategies for machine learning. She outlines a roadmap covering fundamentals to expert-level knowledge. The first step is establishing a strong foundation in basic math, emphasizing probability, statistics, and their relevance to prediction and modeling in machine learning. Kylie recommends the Brilliant platform for interactive math lessons to build intuition and critical thinking, offering a discount for the first 30 days through her link.

05:01

🔢 The Importance of Foundational Math in Machine Learning

The paragraph delves into the necessity of a solid math foundation for machine learning, particularly calculus and linear algebra. Calculus, with its focus on optimization and gradient descent, is crucial for model tuning. Linear algebra accelerates model computation through vector and matrix operations. Kylie explains the importance of understanding these mathematical concepts to grasp the theory behind machine learning algorithms, even though practical implementation relies on existing libraries.

10:05

💻 Developing Programming Skills for Machine Learning

Kylie highlights the importance of programming skills, especially Python, for coding machine learning models. She covers basic programming concepts such as variables, functions, and classes, and the necessity of understanding object-oriented programming due to its prevalence in Python libraries. Kylie mentions libraries like pandas, numpy, and matplotlib for data manipulation and visualization, as well as scikit-learn, TensorFlow, and PyTorch for machine learning tasks.

15:06

🌟 Core Concepts of Machine Learning

This section introduces the core concepts of machine learning, starting with understanding the types of machine learning: supervised, unsupervised, and reinforcement learning. It also touches on tasks such as classification and regression. The importance of data quality is emphasized, with a预告 of topics like data types, data manipulation, and feature engineering to be covered in future videos.

20:08

🖼️ Exploring Neural Networks and Model Types

Kylie discusses various machine learning models, starting with simple ones like K-nearest neighbors and logistic regression, to more complex models like neural networks. She provides a brief overview of different neural network architectures, including convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequential data. She also mentions GRUs and LSTMs, which are advanced types of RNNs.

25:08

📈 Training and Evaluating Machine Learning Models

The paragraph focuses on the iterative process of training and evaluating machine learning models. It discusses the importance of using metrics to evaluate model performance and the challenge of overfitting. Kylie stresses the need to understand why overfitting occurs and how to mitigate it to ensure models perform well in real-world scenarios.

🚀 Advancing in Machine Learning Through Practice and Research

The final section of the roadmap emphasizes the importance of practice and research for advancing in machine learning. Kylie suggests starting with projects found online, using resources like the UCI Machine Learning Repository and Kaggle for datasets and community interaction. She also recommends reading research papers and attempting to reproduce results to deepen understanding and expertise in the field.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that allows computers to learn from data and make decisions or predictions without being explicitly programmed. In the video, it is the central theme, with the course aiming to teach viewers how to learn machine learning effectively. The script discusses various aspects of machine learning, such as its foundational math, programming skills, core concepts, and models.

💡Foundational Math

Foundational math refers to the basic mathematical concepts necessary to understand and apply machine learning algorithms. The video emphasizes the importance of probability and statistics, calculus, and linear algebra as the mathematical bedrock for machine learning. For instance, probability is crucial for making predictions, while calculus, particularly optimization problems and gradient descent, helps in training models.

💡Programming Skills

Programming skills are essential for implementing machine learning models. The video highlights Python as a key language for machine learning due to its popularity, extensive documentation, and rich set of libraries. Basic programming concepts such as variables, functions, classes, and the use of libraries like pandas, numpy, and machine learning frameworks like scikit-learn, TensorFlow, and PyTorch are mentioned as necessary skills.

💡Probability and Statistics

Probability and statistics are mathematical fields that deal with the understanding of data and the likelihood of events. In the context of the video, they are foundational to machine learning as they provide the framework for predicting outcomes and understanding the behavior of data, which is exemplified by discussing concepts like conditional probability and statistical distributions.

💡Calculus

Calculus is a branch of mathematics that deals with rates of change and accumulation. The video script mentions calculus as important for machine learning because it helps in solving optimization problems, particularly through the use of derivatives and gradient descent, which are fundamental to training neural networks.

💡Linear Algebra

Linear algebra involves the study of vectors, matrices, and linear transformations. The video explains that linear algebra is critical for machine learning as it allows for efficient computation, especially when dealing with large datasets. It enables the manipulation and transformation of data in a way that is fundamental to the operation of machine learning models.

💡Neural Networks

Neural networks, also known as artificial neural networks, are a set of algorithms designed to recognize patterns. They are inspired by the human brain and are a core topic in the video. The script discusses different types of neural networks, such as convolutional neural networks (CNNs) for visual data and recurrent neural networks (RNNs) for sequential data, and mentions concepts like perceptrons and backpropagation.

💡Data

Data is the raw material that machine learning models learn from. The video script underlines the importance of having good quality data, understanding data types, and the process of data manipulation, including cleaning, feature scaling, and feature engineering. The phrase 'junk in equals junk out' is used to emphasize the critical role of data quality in the success of machine learning models.

💡Model Training and Evaluation

Model training is the process of teaching a machine learning model to make predictions or decisions based on data, while evaluation is the process of assessing the model's performance. The video script discusses the iterative nature of training and evaluating models, the importance of using metrics to evaluate them, and the challenge of overfitting, which occurs when a model performs well on training data but poorly on new, unseen data.

💡Practice and Research

Practice and research are the final steps in the machine learning roadmap presented in the video. They involve applying the knowledge gained through the course to real-world projects and staying up-to-date with the latest research in the field. The script encourages viewers to engage with online resources, such as the UCI Machine Learning Repository and Kaggle, and to read and implement research papers to deepen their expertise.

Highlights

Introduction to a machine learning course aimed at teaching effective learning strategies in the field.

Emphasis on the importance of a strong foundation in basic math for developing machine learning skills.

Explanation of the relevance of probability and statistics in predicting outcomes in machine learning.

Introduction of Brilliant.org as a platform for interactive learning in math, data analysis, and AI.

The necessity of understanding calculus for dealing with optimization problems in machine learning.

Importance of derivatives in performing gradient descent to train models.

Fundamental role of linear algebra in machine learning for efficient data manipulation.

Overview of programming skills, particularly Python, essential for implementing machine learning models.

Discussion on the significance of understanding object-oriented programming concepts in Python.

Introduction of key Python libraries such as pandas, numpy, and matplotlib for data processing and visualization.

Core concepts of machine learning including types of learning and tasks like classification and regression.

The concept of 'junk in equals junk out' highlighting the importance of quality data in machine learning.

Explanation of data manipulation techniques including data cleaning, feature scaling, and feature engineering.

Overview of prevalent machine learning models such as K-nearest neighbors, logistic regression, and neural networks.

In-depth look at neural networks, including convolutional neural networks (CNNs) for visual data.

Introduction to recurrent neural networks (RNNs) for handling sequential data and advanced models like GRUs and LSTMs.

Discussion on training and evaluating machine learning models, including avoiding overfitting.

Recommendation for practice and research through projects, online resources, and reading academic papers.

Encouragement to engage with the machine learning community on platforms like Kaggle for exposure and expertise.

Conclusion summarizing the machine learning roadmap from foundational math and programming to core concepts and practice.

Transcripts

hi everyone I'm Kylie ying and welcome

to my machine learning course many of

you out there are wondering well how do

I learn machine learning effectively and

so that is exactly what my course

strives to teach in today's video I'm

going to go over a machine learning road

map to help you learn machine learning

effectively and these will cover

Concepts from like the fundamentals all

the way through how do I become an

expert in this field so with that let's

get

started so the first thing for our

machine learning road map is that we

want to really lay down a good

foundation so the first thing that we're

going to go over are our

foundations now there's two areas that

we really need a strong foundation in

order to develop our machine learning

skills and the first area is actually

going to be basic math so I'm going to

call this foundational math

under foundational math we have a few

different areas of mathematics the first

one is actually probability and

statistics now why probability and

statistics how are these even relevant

to machine

learning machine learning is all about

trying to figure out a prediction it's

about trying to come up with the most

probable outcome and in order to do that

well we kind of have to have a really

good foundation in probability we have

to understand things such as conditional

probability you know if these things are

true then how does that impact this

other thing um we need a good

understanding of something called base

Rule and we need a good understanding of

statistical distribution such as the

normal distribution or um the binomial

distribution and these things will help

you gain an intuition for how do we

model you know the unpredictable world

around us and ultimately that's going to

translate into how does machine learning

help us predict this unpredictable world

around us even though I'm planning to

teach a little bit about probity and

statistics as well as the other machine

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statistics great that's one area but

that's not it so the second area that

you'll really need to gain a foundation

in is

calculus and the reason why we need

calculus is because um a lot of machine

learning is also about okay given our

data how do

get the best model and whenever you're

looking for the best something that

becomes an optimization problem so you

know a lot of calculus actually helps us

deal with these optimization

problems such as one area of calculus is

called gradient

descent and that will help us eventually

tune neural Nets and be able to train

our models so that's gradient

descent and the foundation of gradient

descent is actually derivatives and that

is the most calculus thing ever so under

here we have

derivatives um and how do derivatives

help us well when we actually go and

perform gradient descent what we're

doing is we have some number and we want

to adjust this number a little bit like

we have some parameter that affects our

outcome and we want to adjust this

parameter so that we we get closer to

our desired outcome and how do we adjust

it which way do we step a little bit to

the left a little bit to the right we

can actually use derivatives to help us

figure out which way we want to go so

derivatives really powerful tool all

over you know neural networks machine

learning um in order to understand the

mathematics behind like how these things

work you will need to understand

derivatives and now finally

the last piece of math that's really

critical to machine learning is linear

algebra a lot of machine learning is

just basic algebraic computation so for

example um a lot of the models that you

will deal with are usually just adding

numbers multiplying numbers and doing

some simple operation on on those

well you know if we have a huge model

and we have a ton of data which that's a

foundation of machine learning it's a

ton of data and we want to use that data

to try to make some model better and

better if we were to just go through

each like each operation like addition

multiplication if we were to go through

these one by one that would take forever

so we actually want to parallelize these

operations and basically what that means

is instead of calculating these one at a

time we're just going to calculate a

bunch at the same time and maybe at the

very end we can sum all of these things

together rather than having to go

through each one and then at the end sum

all of those together so that just saves

us a lot of time and the way that we do

that is through linear algebra so linear

algebra helps us basically solve for

things a lot faster and so in linear

algebra the things that you will have to

understand our vectors and

matrices and how you know how to

multiply these things together um how to

take the inverse how to solve basic uh

systems of equations stuff like that

also relevant from linear algebra will

be igen vectors and Ian

values and now the these will eventually

help us take a lot of data and figure

out what are the most important

components of this data so what I mean

by that is let's say I have a million

different factors that go into some

model I have a million different things

that I want to consider when I build my

model well can I pick out maybe certain

things that are the most important as

far as math goes having probability and

statistics calculus and linear algebra

that should suff suffice in terms of

having a good like foundation for

machine learning foundational math will

provide you with the basis in order to

learn the mathematics behind a lot of

the concepts in machine learning so you

know you're never actually going to do

the math by hand when you're trying to

like build something using machine

learning because that's just not

feasible there's already code out there

they're called libraries like somebody

else has already optimized these things

and you're just calling their functions

but you should have a good foundation of

like okay how does this work behind the

scenes so that you know what you're

doing now in order to actually use those

libraries and to put machine learning

into practice well that's where the

second foundational area of machine

learning comes in so that second area is

going to be programming skills um you

need programming skills basically in

order to code pretty much anything

including your machine learning models

so that's why it's important hopefully

that's pretty clear that if you want to

code a model you will need programming

in order to do it I would say the

language that's probably the most

important is

python I would say for beginners that

python is a really great starting point

um to improve your programming skills

and the reason why I say that especially

in the context of machine learning is

that python is popular you're going to

find a lot of documentation online for

it there's a lot of resources out there

to support you in your journey To Learn

Python also python has really great

libraries so they have good packages of

code that will help you process data or

um or work with models and python is

really great for non-production code so

when you're trying to just prototype

something when you're playing around

with data Python's really great because

it's pretty Bare Bones and it's really

um

understandable so yeah Learn Python and

if you don't know python I have a bunch

of courses on that as well so under

programming skills the things that are

important are basic concepts and these

concepts are things such as

[Applause]

variables um you got to know how

variables work in order to create a

program uh same with

functions and classes becomes a little

bit more advanced stuff but I mean

python is an objectoriented programming

language which means that it's based on

objects and these objects are built

using

classes now uh while you yourself

probably won't have to deal with classes

a lot of these libraries use classes so

you have to at least be able to

understand how those

work and then finally

libraries and and um basically how to

use these

libraries now the libraries in Python

that are relevant for machine learning

there's a few of them there's a library

called pandas and this one will let you

basically import some data and be able

to look at that in a table and do

vectorized operations so instead of

multiplying um a single item by a number

for every single Row in your in your

table you can just multiply that entire

column by some number um and also

numpy and numai is really built for uh

really large multi-dimensional arrays

and matrices and being able to do um

math operations on these data types

after that um you have a few libraries

that actually like give you machine

learning models that are readily

available to use and those include s kit

learn tensor

flow and P

torch and then finally there's a really

great plotting library in Python um

called Matt plot

lib and especially under this there's

something called P plot that you can use

to plot in Python these are really the

foundations that you should have to

learn machine learning the first is

foundational math so you can understand

the theory of machine learning and then

the second is programming skills so you

can actually implement the models

yourself now that we have a solid

foundation for Math and programming we

can move on to actually learning The

Core Concepts of machine learning so the

next category on this road map are those

Core Concepts the first area that's

really important is what is machine

learning

so it's understanding what is available

to you out there and just like what are

the different techniques that you can

use what are the different broad

categories of things that you're trying

to do with your

models so that includes the types of

machine learning um and the three types

are

supervised

unsupervised and reinforcement

[Music]

oops and then on top of the types of

machine learning there's also different

tasks so what are you trying to achieve

in each of these types and um I won't

talk about reinforcement learning yet

but in supervised and sometimes

unsupervised these tasks boil down to

something called classification

and you also have something called

regression again I won't go over these

but these are important Concepts to know

and I'll go over them in a separate

video the next core concept is data now

junk in equals junk out which is why

it's really critical to know what is

going into your model and to make sure

that you have a good data set going in

it's important to gain an intuition for

understand understanding what is good

data and where is my data coming from

and to ask yourself these things before

just assuming that the data that you

have in front of you is good there have

been you know multi-billion dollar

companies established just to solve the

problem of how do I get good data for

machine

learning so first under data you have to

understand the types of data

and this might include something known

as

qualitative

data as well as

quantitative data so those are different

and you have to handle each of those

differently again I'll go over this in a

different

video next it's understanding this

concept of

training validation

and

testing

data why do you have three different

types of data sets why is one not enough

finally the last thing that's really

important to understanding this concept

of data is manipulating your

data so some form of manipulation of

your data

set and that might include data cleaning

so making sure that your data set is

good or getting rid of certain things

that are not not good about your data

set there's something known as feature

scaling so maybe you want to you know

the scale of um something in your data

set is not quite where it wants to be

like if I have a whole data set where

many things are judged on a scale of 0

to 100 and then something else is judged

on a scale of 0 to 5 well I might want

to multiply everything in that's 0 to

five by 20 so that this is also on a

scale of 0 to 100

and then finally there's something

called feature

engineering where that means that in

your entire data set you're choosing

what pieces of data do I actually use or

can I use the data that I have to build

you know some

intermediary piece of data some new

piece of data that I can actually feed

into my bottle so that my model performs

better um so for example if I have stock

prices of something like a feature that

I can engineer is instead of the price

having the return from day-to-day so

that's the difference between today and

yesterday and so

on so that kind of summarizes uh this

concept of data that's really critical

to machine learning next after data we

can start looking at models because you

know we have good stuff coming in and

now the next part is okay well what does

it go into so those are the

models in this road map I'm just going

to list a bunch of models that are

prevalent in machine learning and these

are things that would be good to learn

in order to have a good foundation in

machine learning so there's things such

as K

nearest

neighbors that's an algorithm um there's

another model called logistic regression

or other otherwise known as log

regression and again I'm not going to go

over each of these here but in a later

video I will so stay tuned subscribe you

know and again don't worry about

understanding these here and now these

will come that's why this is just a road

map um there's something called

svm or

support Vector machine

that's another

model there's something called linear

regression which you might be wondering

hey I already know what that is but I

didn't think that was machine learning

well surprise some people consider it a

machine learning

model and then there's a big one that

everybody talks about neural

networks otherwise known as neural Nets

or artificial neural networks artificial

neural nets all of those things things

but neural networks and the

individual like neuron that makes up

this neural network that's something

called a

perceptron and there's two more

algorithms that um I'll add to this list

as well that might be K

means and finally something called

PCA or principle

component

analysis so this list of models will

provide you with a really great

foundation and toolkit for basic machine

learning and now we can get a little bit

more in depth into neural

networks because there's actually many

different types of neural networks and

there's a few that are pretty popular so

the first one is known as a

CNN and that C stands for

convolutional

convolutional so convolutional neural

networks and what this is doing it's

actually taking an

image um and trying to run a neural

network on that image so you can work

with visual

data and then there's this concept of an

RNN and this R stands for

recurrent and RNN are really great for

sequential data so they can handle

sequences um when your data has some

sort of ordering that is relevant so

sequential data and you can get a little

bit more complex with this RNN stuff um

you can go into grus and lstms and then

from there it opens a whole new world

and eventually you get to things such as

uh chat GPT the final part of this

machine learning road map app is

training and evaluating a

model now the reason why I put them

together is because the training and the

evaluating kind of go hand inand we have

to evaluate a model to see how it does

and then if it doesn't do so well we

want to train it again so that you know

maybe we can see if it improves and at

the very end we have a final evaluation

but this is a this is a recursive

process it's we train something we see

how it does okay it doesn't do exactly

what we want it to do so we retrain it

and we try to see okay does it do a

little bit better hopefully yes and we

keep going like

that so um there are things here that

are important such as metrics like how

do you evaluate a model what are

important things to look at and then

also

things such as

overfitting what does that mean it means

okay I've trained this model the

evaluation looks really good but when I

actually go put it out in the real world

it doesn't work as well so what is going

on there and being able to understand

okay what is leading to this overfitting

how can I combat that overfitting those

are really critical pieces

to to just building a good model that

kind of wraps up this Core Concepts

piece of our road map once you

understand these Core Concepts in

machine learning from there it's really

just a lot of practice and reading

research because you already have the

basic building blocks um in order to go

and learn more and now at this point

it's all about okay which direction do

you want to go in so I would call this

next step as just

practice and research

so for practice a good place to start

would just be projects that you find

online try to get an understanding of

how do other people do this and you can

use

YouTube as a resource hopefully my

channel um you can use other like blog

posts I mean the internet is full of

people who are really good teachers so

YouTube slash other things on the

internet there is one data set that's

really great for introductory machine

learning and just trying to play around

with code and data and building simple

models so that is the

UCI

machine learning

repository great resource to you know

try dabble

yourself um and then finally there's a

lot of data on

kaggle and kagle also makes it really

easy to learn from other people and to

see what other people are doing so

that's a really great place to interact

with a community of people all

passionate about machine learning from

there it's all about papers so a lot of

cutting edge research people will be

writing papers about those people will

be publishing their work and literally

describing oh this is what we do in

order to achieve these results and I had

recommend papers and honestly trying to

implement those papers for yourself and

to see if you can reproduce those

results and this is really the area

where you know I can't tell you how to

become an expert you just have to find

something that you really like and go

deep into it and this is where honestly

in this section the practice and

research section this is about just

scouring the internet and learning from

other people who are experts in machine

learning

in this machine learning road map we

started out with foundations of things

that you have to learn in order to get

started on machine learning and those

include math and programming and then

after that we talked about Core Concepts

in machine learning and I named a few

different models that are pretty

fundamental to a lot of more complex

machine learning and finally at the very

end we talked about how do you practice

how do you gain more exposure and

expertise and all of that is about you

know practice and reading and trying to

learn from experts on the Internet or

you know if you know them in real life

like that's great too but the internet

is a great resource this road map makes

for a great syllabus for machine

learning course so stay tuned I'm going

to be teaching some of these Concepts

and don't forget to subscribe