Data Science Projects
Struggle of a data scientist
What's the most struggling part while learning data science as per your experience?
β€6
Question 13:
How do you handle categorical variables in a dataset? What techniques do you use for encoding them?
How do you handle categorical variables in a dataset? What techniques do you use for encoding them?
π9
Question 14:
Explain the difference between a ROC curve and a Precision-Recall curve. When would you use one over the other?
Explain the difference between a ROC curve and a Precision-Recall curve. When would you use one over the other?
Question 15:
How do you deal with multicollinearity in regression models? What methods can be used to detect and address it?
How do you deal with multicollinearity in regression models? What methods can be used to detect and address it?
Question 16:
What is the difference between hard and soft clustering? Can you give an example of algorithms that use each approach?
What is the difference between hard and soft clustering? Can you give an example of algorithms that use each approach?
π2
Join our WhatsApp channel for more Data Science Resources ππ
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
WhatsApp.com
Artificial Intelligence & Data Science Projects | Machine Learning | Coding Resources | Tech Updates | WhatsApp Channel
Artificial Intelligence & Data Science Projects | Machine Learning | Coding Resources | Tech Updates WhatsApp Channel. Perfect channel to learn Machine Learning & Artificial Intelligence
For promotions, contact [email protected]
π° Learn Dataβ¦
For promotions, contact [email protected]
π° Learn Dataβ¦
π1
Forwarded from Finance, Investing & Stock Marketing
At the age of 19, 20, 21+ you will start to realize that life ain't easy. your circle of friends will start to become smaller. you lose yourself, you become frustrated, lonely for no reason, you will develop trust issues, you cry silently at night and wake up in the morning like nothing happened. you think about giving up many times but in the end you find yourself fighting again because you realize that this is stage where you must be strong to fight your fears and possibilities that everything will leave you.
Drop β€οΈ if u felt this
Drop β€οΈ if u felt this
β€102π13π₯2π1
Which library do you use mostly for deep learning?
Anonymous Poll
60%
Tensorflow
16%
Keras
23%
Pytorch
2%
Add any other in comments
π6π1
You don't need to buy a GPU for machine learning work!
There are other alternatives. Here are some:
1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim
Spend your time focusing on your problem.πͺπͺ
There are other alternatives. Here are some:
1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim
Spend your time focusing on your problem.πͺπͺ
π27β€9
I have uploaded a lot of free resources on Linkedin as well
We're just 94 followers away from reaching 100k on LinkedIn! β€οΈ Join us and be part of this milestone!
We're just 94 followers away from reaching 100k on LinkedIn! β€οΈ Join us and be part of this milestone!
π5β€3π2
If you want to invest in the future, invest in:
β’ Machine Learning
β’ Water Technology
β’ Quantum Computing
β’ Internet of Things (IoT)
β’ Augmented Reality (AR)
β’ Quantum Information Science
β’ Agri-tech and Food Technology
β’ Next-Gen Telecommunications
β’ Autonomous Vehicles and Robotics
β’ Genomics and Personalized Medicine
β’ Advanced Materials and Manufacturing
What would you add?
β’ Machine Learning
β’ Water Technology
β’ Quantum Computing
β’ Internet of Things (IoT)
β’ Augmented Reality (AR)
β’ Quantum Information Science
β’ Agri-tech and Food Technology
β’ Next-Gen Telecommunications
β’ Autonomous Vehicles and Robotics
β’ Genomics and Personalized Medicine
β’ Advanced Materials and Manufacturing
What would you add?
π23
Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donβt go your way, you might have had a brain fade, it was not your day etc. Donβt worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds donβt go your way, you might have had a brain fade, it was not your day etc. Donβt worry! Shake if off for there is always a next time and this is not the end of the world.
π19β€2
Classes That SHOULD Be Mandatory in High School:
β’ Taxes
β’ Investing
β’ Real Estate
β’ Negotiating
β’ Basic coding
β’ Building credit
β’ Microsoft Excel
β’ Personal Finance
β’ Entrepreneurship
β’ Time Management
β’ Money Management
What would you add to the list?
β’ Taxes
β’ Investing
β’ Real Estate
β’ Negotiating
β’ Basic coding
β’ Building credit
β’ Microsoft Excel
β’ Personal Finance
β’ Entrepreneurship
β’ Time Management
β’ Money Management
What would you add to the list?
β€35π10π4