Machine Learning Project Ideas
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๐ฐ ๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
These free, Microsoft-backed courses are a game-changer!
With these resources, youโll gain the skills and confidence needed to shine in the data analytics worldโall without spending a penny.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jpmI0I
Enroll For FREE & Get Certified๐
These free, Microsoft-backed courses are a game-changer!
With these resources, youโll gain the skills and confidence needed to shine in the data analytics worldโall without spending a penny.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jpmI0I
Enroll For FREE & Get Certified๐
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โก๏ธ Big ML cheat sheet
Here you will find the basic theory of Machine Learning and examples of the implementation of specific ML algorithms - in general, this is just the thing to brush up on your knowledge before the interview.
๐ Crib
Here you will find the basic theory of Machine Learning and examples of the implementation of specific ML algorithms - in general, this is just the thing to brush up on your knowledge before the interview.
๐ Crib
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๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ & ๐๐น๐ฒ๐๐ฎ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ต๐ฏ๐ผ๐ฎ๐ฟ๐ฑ ๐๐ฎ๐บ๐ฒ!๐
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4cwsGL2
Enjoy Learning โ ๏ธ
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4cwsGL2
Enjoy Learning โ ๏ธ
The Data Science skill no one talks about...
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesnโt.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Letโs go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. โLyft is offering better prices for that geoโ (pricing problem)
2. โCar waiting times are too longโ (supply problem)
3. โThe Android version of the app is very slowโ (client-app performance problem)
You build this list โ by asking the right questions to the rest of the team. You need to understand the userโs experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA ๐.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For exampleโฆ
Scenario 1: โLyft Is Offering Better Pricesโ (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, Eโฆ) to test different pricing points.
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesnโt.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Letโs go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
๐ฉโ๐ผ: โWe want to decrease user churn by 5% this quarterโ
We say that a user churns when she decides to stop using Uber.
But why?
There are different reasons why a user would stop using Uber. For example:
1. โLyft is offering better prices for that geoโ (pricing problem)
2. โCar waiting times are too longโ (supply problem)
3. โThe Android version of the app is very slowโ (client-app performance problem)
You build this list โ by asking the right questions to the rest of the team. You need to understand the userโs experience using the app, from HER point of view.
Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?
This is when you pull out your great data science skills and EXPLORE THE DATA ๐.
You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.
For exampleโฆ
Scenario 1: โLyft Is Offering Better Pricesโ (Pricing Problem)
One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:
The A group. No user in this group will receive any discount.
The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.
You could add more groups (e.g. C, D, Eโฆ) to test different pricing points.
In a nutshell
1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem
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COMMON TERMINOLOGIES IN PYTHON - PART 1
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
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๐๐ป๐ณ๐ผ๐๐๐ ๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resumeโat no cost.
Whether youโre a student, graduate, or working professional, this platform has something valuable for everyone.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jsHZXf
Enroll For FREE & Get Certified ๐
Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resumeโat no cost.
Whether youโre a student, graduate, or working professional, this platform has something valuable for everyone.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jsHZXf
Enroll For FREE & Get Certified ๐
Data Science Learning Plan
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
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