#2 | 4 Mistakes made while Learning PYTHON || How to Learn Python || Python for Data Science

Publicado em: 21 Fevereiro 2023
no canal de: Datahat -- Simplified AI

Learning a new skill takes hard work along with strategy.
Often beginners make several mistakes when learning a new skill

In this video we discuss 4+1 major mistakes made by beginners when learning Python. These are the biggest mistakes that you must avoid.

I know that you are a smart learner and would avoid making such mistakes in the future

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Why learn data science??

"Data Science is the sexiest job of 21st century"

If you are a #btech student, #diploma in #computerscience, #graduate looking to #career #tranisition into #datascience , #machinelearning, #dataanalysis, this is a place for you

Everyone of us use social media platforms such as instagram, facebook, or binge watch on OTT platforms such as #youtube , netflix, prime, hotstar or window shop on e-commerce sites such as amazon, flipkart, myntra

It certainly amazes us to see the products of our choice and interest being #recommended to us, our favorite shows being filtered among millions of others

However, not many get to understand how it works. Not to worry, we shall help you understand the concepts in a #simplified and #concise fashion, helping in career transition into machine learning or data science

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Why learn from datahat??

"Datahat is a bridge"
learners --- professionals by simplified data science

It provides a platform to learn, create and collaborate helping in the #career #transition to data science, machine learning and data analysis

Understand the broad domain of data science and its related subdomains in machine learning, data analysis, data engineering in a self paced, #structured and #guided learning

Here are few interesting reads on our blog: / souravagarwal54321

Connect with us on linkedIn: / datahat-unfoldingmystery

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Additionally, here are few more useful resources for beginners and curious data enthusiasts

1. kaggle: a platform to learn, practice, compete and win cash prizes [https://www.kaggle.com/]

2. paperswithcode: website presenting the latest in machine learning and data science research and the code implementations [https://paperswithcode.com/]

3. google colab: a platform to run code, build machine learning solutions, explore the capabilities of GPU and TPU without any hassle in setting up the environment: [https://colab.research.google.com/]

Remember, "the greatest investment ever made is investment in self growth and learning"

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