Machine Learning Course curriculum | Machine Learning - Roadmap

Summary

Siddharthan 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

00:00

📚 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.

05:02

🔍 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.

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, the theme revolves around a hands-on course in machine learning with Python, indicating that the content will cover practical applications and techniques in this field.

💡Curriculum

A curriculum refers to the complete plan of study that a course offers, including the content, objectives, and structure. The video script discusses the detailed curriculum for the machine learning course, outlining the modules and topics that will be covered, such as basics of machine learning, Python basics, and various machine learning models.

💡Python

Python is a high-level programming language known for its readability and versatility, widely used in fields like web development, data analysis, and artificial intelligence. The script mentions Python as the primary language for the machine learning course, highlighting its importance in implementing machine learning algorithms.

💡Course Order

Course Order refers to the sequence in which the course content is presented. The script specifies that two videos per week will be posted following a course order, focusing on specific topics to ensure a structured learning experience for the viewers.

💡Machine Learning Project

A Machine Learning Project typically involves applying machine learning techniques to solve a specific problem or analyze a dataset. The video mentions that one detailed video per week will be dedicated to a machine learning project, showcasing practical implementations of the concepts taught in the course.

💡Prerequisite

A prerequisite is a condition or requirement that must be met before starting a course or activity. The script states that the only prerequisite for the course is an interest in learning machine learning, emphasizing that even beginners can participate.

💡Google Colaboratory

Google Colaboratory is a cloud-based environment that allows users to write and execute Python code through the browser, which is useful for those who do not wish to set up a local development environment. The script mentions Google Colaboratory as part of the Python basics module, indicating its use for running machine learning code.

💡Data Preprocessing

Data Preprocessing involves cleaning, transforming, and preparing data for analysis or modeling. In the context of the video, data preprocessing is part of the curriculum, where learners will be taught how to handle missing values and standardize data, which is crucial for effective machine learning model training.

💡Classification Models

Classification models are a type of machine learning algorithm used to predict categorical labels. The script lists several classification models, such as logistic regression, support vector machines, and decision trees, that will be covered in the course, showing the practical applications of these models in machine learning tasks.

💡Regression Models

Regression models are used to predict continuous values based on input variables. The video script includes regression models such as linear regression and logistic regression for regression problems, indicating the course will cover techniques for predicting numerical outcomes.

💡Unsupervised Learning

Unsupervised Learning is a type of machine learning where the algorithm learns from data without any explicit instructions on what to predict. The script mentions clustering models, such as k-means and hierarchical clustering, as part of the unsupervised learning module, which will teach learners how to find patterns and groupings in data.

💡Association Models

Association models are used to discover rules that describe large portions of data. The script refers to two main association models, a priori and eclat, which will be discussed in the course, highlighting the importance of understanding data relationships in machine learning.

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