Machine Learning Course curriculum | Machine Learning - Roadmap
Summary
TLDRSiddharthan introduces his hands-on Machine Learning course with Python on YouTube, posting three videos weekly: two on course content and one on a ML project. The course requires only an interest in ML, with modules covering ML basics, Python basics, and various ML projects. Upcoming modules include Python libraries, data collection, mathematical basics, ML models, and algorithms. A deep learning course will follow. The channel is designed for beginners and experienced learners alike.
Takeaways
- 😀 Siddharthan introduces a hands-on machine learning course with Python on his YouTube channel.
- 📅 The channel posts three videos a week: two on the course content on Mondays and Wednesdays, and one detailed machine learning project video on Fridays.
- 📚 The course is aimed at beginners as well as those familiar with basic concepts, with modules covering the basics of programming in Python.
- 🎓 The curriculum includes modules on machine learning basics, Python basics for machine learning, and various machine learning projects.
- 🛠️ Google Colaboratory is highlighted as an environment to run Python codes without needing to install any IDEs.
- 📈 Six machine learning projects have been completed, including face recognition, rock vs. mind prediction, and diabetes prediction, with more to be posted every Friday.
- 📚 The third module to be posted covers Python libraries essential for machine learning, such as NumPy, pandas, matplotlib, and scikit-learn.
- 🔢 The course will also cover mathematical basics important for machine learning, including linear algebra, calculus, statistics, and probability.
- 🤖 Machine learning models and algorithms will be discussed in detail, including classification, regression, clustering, and association models.
- 🏆 The course concludes with an introduction to deep learning, which will be covered in a standalone course after the completion of the machine learning course.
- 🔗 Siddharthan encourages viewers to subscribe, share the channel with friends, and stay tuned for regular video uploads.
Q & A
What is the main topic of Siddharthan's YouTube channel videos?
-The main topic is a hands-on machine learning course with Python.
How often does Siddharthan post videos on his YouTube channel?
-He posts three videos a week, specifically on Monday, Wednesday, and Friday evenings.
What is the content of the videos posted on Monday and Wednesday?
-The videos on Monday and Wednesday cover the course order being discussed.
What type of video is posted on Friday evening?
-A detailed video on a machine learning project is posted on Friday evenings.
What is the prerequisite for the machine learning course offered by Siddharthan?
-The only prerequisite is having an interest to learn machine learning, and basic programming is covered in the course as well.
How many modules have been completed on Siddharthan's channel before the script was written?
-Two modules were completed: Machine Learning Basics and Python Basics for Machine Learning.
What is the focus of the first module in the course curriculum?
-The first module focuses on the basic concepts and terms in machine learning.
What is the content of the second module on Siddharthan's channel?
-The second module covers Python basics necessary for machine learning, including Google Collaboratory, Python data types, and other programming fundamentals.
How many machine learning projects have been completed and mentioned in the script?
-Six machine learning projects have been completed and mentioned.
What are the topics covered in the upcoming third module of the course?
-The third module will cover Python libraries tutorials for machine learning, including NumPy, pandas, matplotlib, and scikit-learn.
What mathematical basics will be discussed in the course?
-Linear algebra, calculus, statistics, and probability will be discussed as mathematical basics for machine learning.
What types of machine learning models will be covered in the course?
-The course will cover classification models, regression models, clustering models, and association models in machine learning.
What is the plan for after completing the machine learning course?
-After completing the machine learning course, Siddharthan plans to create a standalone deep learning course.
How long is the estimated time to complete the machine learning course?
-The estimated time to complete the course is three months or less.
What is the schedule for the video uploads on Siddharthan's channel?
-Videos are uploaded at 5:30 p.m. on Monday, Wednesday, and Friday.
Outlines
📚 Introduction to Machine Learning Course
Siddharthan introduces his YouTube channel and the hands-on machine learning course with Python. He provides a link to the channel and mentions the schedule for posting videos, which includes two educational videos on Mondays and Wednesdays and a detailed machine learning project video on Fridays. The course is aimed at beginners and those familiar with basic concepts, with prerequisites being an interest in machine learning and possibly basic programming skills in Python. The curriculum includes completed modules on machine learning basics and Python basics, with ongoing projects and upcoming modules on Python libraries for machine learning.
🔍 Curriculum Overview and Upcoming Modules
This paragraph delves into the specifics of the machine learning course curriculum. It outlines the modules that have been completed, including basics of machine learning and Python fundamentals, as well as the machine learning project videos that have been released. The upcoming modules are detailed, starting with Python libraries essential for machine learning, followed by data collection and processing techniques. The course continues with mathematical basics necessary for understanding machine learning algorithms, moving on to machine learning models, their selection, optimization, and evaluation. The subsequent modules cover various machine learning algorithms, including classification and regression models, as well as unsupervised learning with clustering and association models. Siddharthan also mentions the plan for a deep learning course post the completion of the machine learning course, indicating a comprehensive learning journey that will take approximately three months to complete.
Mindmap
Keywords
💡Machine Learning
💡Curriculum
💡Python
💡Course Order
💡Machine Learning Project
💡Prerequisite
💡Google Colaboratory
💡Data Preprocessing
💡Classification Models
💡Regression Models
💡Unsupervised Learning
💡Association Models
Highlights
Introduction of a hands-on machine learning course with Python on YouTube.
Link to the YouTube channel provided in the document file and description.
Posting schedule of three videos per week, covering course content and machine learning projects.
The course is designed for beginners and those familiar with basic concepts.
Curiosity and interest in machine learning are the only prerequisites for the course.
Modules on basic programming in Python for those new to programming.
Completion of two modules: Machine Learning Basics and Python Basics for Machine Learning.
Introduction to Google Collaboratory for running Python codes without installing an IDE.
Six machine learning projects completed, with topics ranging from face recognition to loan status prediction.
Upcoming module on Python libraries essential for machine learning, including NumPy, Pandas, Matplotlib, and Scikit-Learn.
Discussion on data collection and processing, including specialized techniques for data collection through APIs.
Module on mathematical basics for machine learning, covering linear algebra, calculus, statistics, and probability.
Exploration of machine learning models, selection, optimization, and evaluation.
In-depth discussion of classification models, including logistic regression, support vector machines, decision trees, and more.
Regression models module, covering linear regression, lasso regression, and logistic regression for regression problems.
Unsupervised learning module focusing on clustering models like k-means and hierarchical clustering.
Association models module, introducing a priori and eclat algorithms.
Plans for a standalone deep learning course after completing the machine learning course.
Estimated time to complete the machine learning course is three months or less.
Invitation to subscribe and share the channel for a beneficial learning experience.
Transcripts
hello everyone this is siddharthan
welcome to my youtube channel in this
video i would like to explain you about
the curriculum for my hands-on machine
learning course with python
that i am publishing in youtube okay so
i have given the link for my youtube
channel in this
document file and you can get the link
for this
particular file in the description of
this video okay so
first of all let me tell you about the
schedule in which i post the videos in
my channel
so in a week i will be posting three
videos so
two videos will be on the course order
that we are going to discuss
so those two videos will be published on
monday and wednesday evening
okay and there will be one detailed
video on a machine learning project and
that particular video will be posted on
friday evening okay so this is because i
want this channel to be useful
to people who are just beginning to
learn machine learning so those people
can watch these two videos
and there are also can be people who are
already familiar with the basic concepts
and those people can watch this project
video so that's the reason behind this
okay
so and what is the prerequisite for this
course so i would say that the only
prerequisite
that i would say is that you should have
some interest to learn machine learning
okay so that curiosity is enough to
learn machine learning
and if you are new to programming that's
completely fine too because i also have
modules covering the basics of
programming in python okay
so now let me explain you the course
curriculum so already
we have completed two modules in my
channel so you can check out the
playlist in my channel
so this is the curriculum so the first
module is about the machine learning
basic basics okay so in this module i
will
explain you all the basic concepts and
uh very basic terms you need to know
in machine learning so there are totally
five videos in this particular module so
first we have discussed about the
difference between
artificial intelligence machine learning
and deep learning then we have uh
discussed about the different types of
machine learning
and what is mean by supervised learning
and its types then
unsupervised learning and its type and
what is done by deep learning okay so
you can
check out this module and after this
model we have completed the
second module which is python basics for
machine learning okay
so in this module the first video is
google collaboratory basic so google
collaboratory is an environment in which
you can run python codes okay
so you don't need to install any python
ids so you can access google
collaboratory
through your google chrome itself so you
can check out that video if you are new
to python
okay so then we have discussed about
python basics python
data types and other operators default
statements loops and functions so these
two model modules
are completed okay we have also
completed about
six machine learning projects so you can
see those
project topics here so first video we
have seen as face recognition system
then we have this rock versus mind
prediction diabetes prediction house
space prediction
fake news prediction and this particular
loan status prediction video will be
published this week so it will be
published on friday okay so i will be
uploading more project videos
every friday okay so we have completed
the
first two modules which are machine
learning basics and python basics and we
have also completed six projects in our
machine learning project video
playlist okay so after that the third
module which will be
posted from the next week is this which
is python libraries tutorial for machine
learning so in this module we will be
discussing in detail
about uh various libraries that are
important for learning machine learning
okay
so we will be discussing in detail so
these videos will be completely hands on
okay so we will be doing this in python
suppose we will be discussing
in detail about numpy library then we
will discuss about various functions in
pandas library then matplotlib and c
bond library
and finally the important machine
learning library which is a scale on
library okay
so these are the three modules and after
completing that
we will be uh learning about data
collection and
data processing so in this module i will
be explaining you
on where to collect data and how to
collect data and there is a specialized
technique
for collecting data through api so in
that video i'll
explain you how to import data set
through calculate okay then we will be
discussing
how to handle missing values and other
data pre-process
data pre-processing techniques okay so
after that uh
there is another pre-processing
technique called as data standardization
so these are the topics
in our fourth module okay so after that
we will be discussing
the mathematic basics for machine
learning so first we will be discussing
in detail about linear algebra
calculus statistics and probability okay
so once we are clear with
this mathematical basics of machine
learning so we will
learn about machine learning model so in
this model so we will
learn the basics of what is mean by a
machine learning model and
where it is used and how to select a
suitable model for a particular problem
statement
and how to optimize our model and how to
evaluate our model so
these concepts and these topics will be
covered in the sixth module
okay so in the next module it's we are
going to
discuss about all the machine learning
algorithms from now on okay so this is
the most requested topic in my channel
okay so in this particular module
we will discuss about all the
classification models or is the
important
classification models in machine
learning so first we will discuss about
logistic regression so in the first
video we will be discussing about
the theory and the mathematic basics
behind the logistic regression then we
will
build a logistic regression model from
scratch using python okay
so after completing the logistic
regression model so we will be working
on support vector machine model
then decision tree classification random
forest classification and naive bias
classification and also
k nearest neighbors so these are the
models we will be discussing under
classification model
so you can see here i have also
mentioned logistic regression because
logistic regression can also be used for
classification okay
so these are the models that we will be
covering in this classification models
okay so in the next module we will be
discussing about regression models in
machine learning showing that we will be
discussing about
linear regression basics and uh building
it from scratch through python and after
that lasso regression
logistic regression for regression
problems so the previous
logistic regression we are we will be
discussing for classification and in
this
module we will be discussing it for
regression problems after that we will
be discussing about support vector
machine particularly for regression
and decision tree regression and random
forest regression okay so it is
these two modules are for super edge
learning then we have two modules for
unsupervised learning in which we will
discuss about
the clustering models in machine
learning okay so in this we will discuss
about
k-means clustering uh both theory and uh
ions on video and then we will be
discussing about hierarchical customer
okay so after the clustering module is
over in the
10th module we will discuss about the
association models in machine learning
okay
so there are two main association models
one is a priori
and the next one is eclat so these are
the first 10 modules
in our machine learning course so once
we are done with all these modules so
you can watch this machine learning
projects
so as i have told you earlier but i have
i have started posting the videos for
this particular
projects module because it would be
useful for a lot of you
of people who are already familiar with
machine learning okay so this is
our machine learning course curriculum
so once we complete this machine
learning course
so we will be uh learning about a deep
learning okay so deep learning deserves
a complete
standalone course so once we complete
this machine learning course i will be
making
a deep learning course okay so but it's
so it's a topic for some other day
okay so when i complete this uh
course one would require you know three
months or even less than that to
complete this course
so i hope this could be a very good
learning journey for you
okay so from now on it would take almost
three months
also for me to complete the course okay
so i will be posting videos
every week okay so i have already posted
about 18 videos so you can check the
videos in my channel
so i have given the link for my channel
just i will just go to
this link and show you
okay
so if you are already in my channel you
can get to
the playlist page
so this is my channel so here you can go
to this playlist
section so here you will see this
created playlist five separate
uh modules which we have discussed
before and in this section you can see
this and sound machine learning course
with python
so these are the models we have
completed module one which is machine
learning basics module 2 is python
basics okay so this is
the machine learning projects that i
have told you so the remaining
eight modules will be posted regularly
okay so
keep track of this video so you can
expect my video to be published on
5 30 p.m in the evening on wednesday
sorry monday wednesday and friday okay
so you can also check my
machine learning project playlist so
there are totally
six videos so far and this video this
loan status prediction
video will be uploaded so stay tuned
okay
so i wish you a very good learning
journey in my
channel so please share this with your
friends
to whom you think this channel could be
really helpful okay so subscribe for
more videos thank you so much
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