Don't waste time on this | #datascience #datasciencecareer

Data Science with Marco · Intermediate ·📐 ML Fundamentals ·3y ago

Key Takeaways

The video discusses the importance of using libraries such as TensorFlow and PyTorch in industry settings, rather than implementing algorithms from scratch, with the exception of learning the inner workings of an algorithm or preparing for interviews.

Full Transcript

never we never do that in industry okay never you will ever implement an algorithm from scratch yes we rely on the libraries so i can learn tensorflow pytorch whatever we use those libraries to implement our algorithms the only time i guess you could implement an algorithm from scratch is if you really want to learn the inner workings of the algorithm or if you are preparing for some kind of an interview although those questions are ridiculous in an interview to ask because again you'll never have to write an algorithm from scratch um we still have to do those interviews to get a job
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Playlist

Uploads from Data Science with Marco · Data Science with Marco · 24 of 38

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1 Linear Regression in Python | Data Science with Marco
Linear Regression in Python | Data Science with Marco
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 2 Classification in Python | logistic regression, LDA, QDA | Data Science With Marco
Classification in Python | logistic regression, LDA, QDA | Data Science With Marco
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 3 Resampling and Regularization | Data Science with Marco
Resampling and Regularization | Data Science with Marco
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 4 Decision Trees | Data Science with Marco
Decision Trees | Data Science with Marco
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 5 Suppor Vector Machine (SVM) in Python | Data Science with Marco
Suppor Vector Machine (SVM) in Python | Data Science with Marco
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 6 Unsupervised Learning | PCA and Clustering | Data Science with Marco
Unsupervised Learning | PCA and Clustering | Data Science with Marco
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 7 Data Science Portfolio Project: Regression #1 | Data Science with Marco
Data Science Portfolio Project: Regression #1 | Data Science with Marco
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 8 Data Science Portfolio Project: Regression #2 | Data Science with Marco
Data Science Portfolio Project: Regression #2 | Data Science with Marco
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 9 What Are Time Series - Applied Time Series Analysis in Python and TensorFlow
What Are Time Series - Applied Time Series Analysis in Python and TensorFlow
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 10 Basic Statistics - Applied Time Series Analysis in Python and TensorFlow
Basic Statistics - Applied Time Series Analysis in Python and TensorFlow
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 11 Autocorrelation and White Noise - Applied Time Series Analysis in Python and TensorFlow
Autocorrelation and White Noise - Applied Time Series Analysis in Python and TensorFlow
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 12 Stationarity and Differencing - Applied Time Series Analysis in Python and TensorFlow
Stationarity and Differencing - Applied Time Series Analysis in Python and TensorFlow
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 13 Random Walk Model - Applied Time Series Analysis in Python and TensorFlow
Random Walk Model - Applied Time Series Analysis in Python and TensorFlow
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 14 Moving Average Process - Applied Time Series Analysis in Python and TensorFlow
Moving Average Process - Applied Time Series Analysis in Python and TensorFlow
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 15 Autoregressive Process - Applied Time Series Analysis in Python and TensorFlow
Autoregressive Process - Applied Time Series Analysis in Python and TensorFlow
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 16 ARMA Model - Time Series Analysis in Python and TensorFlow
ARMA Model - Time Series Analysis in Python and TensorFlow
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 17 What is data science?
What is data science?
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 18 Answering DATA SCIENCE questions #1 - Why learn SQL when Python and R exist?
Answering DATA SCIENCE questions #1 - Why learn SQL when Python and R exist?
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 19 R vs Python in the Industry - Data Science Q&A #datascience #datasciencecareer #careeradvice
R vs Python in the Industry - Data Science Q&A #datascience #datasciencecareer #careeradvice
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 20 Data science or data engineering - which is best for you? #datascience #datasciencecareer
Data science or data engineering - which is best for you? #datascience #datasciencecareer
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 21 Where to find data for data science projetcs? #datascience #datasciencecareer
Where to find data for data science projetcs? #datascience #datasciencecareer
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 22 Data science certificates on resume? #datascience #datasciencecareer #careeradvice
Data science certificates on resume? #datascience #datasciencecareer #careeradvice
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 23 Should you aim for data science or data engineering? | Data Science Q&A #1
Should you aim for data science or data engineering? | Data Science Q&A #1
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 ▶ Don't waste time on this | #datascience #datasciencecareer
Don't waste time on this | #datascience #datasciencecareer
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 25 Low-code AI tools - are they good? | #datascience #datasciencecareer #careeradvice
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 26 How to grow as a data scientist after 2+ years of experience? #datascience #datasciencecareer
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Transition into DATA SCIENCE without a masters or bootcamp #careertransition
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 29 How to learn Python for data science?
How to learn Python for data science?
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 30 Does Scrum/Agile work for data science?
Does Scrum/Agile work for data science?
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 31 What are the major roles in analytics and how to choose?
What are the major roles in analytics and how to choose?
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 32 Thoughts and advice for a live SQL coding round
Thoughts and advice for a live SQL coding round
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 33 Data science interview question: difference between type 1 and type 2 error
Data science interview question: difference between type 1 and type 2 error
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 34 Feature selection in machine learning | Full course
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 35 Anomaly detection in time series with Python | Data Science with Marco
Anomaly detection in time series with Python | Data Science with Marco
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 36 Podcast - TimeGPT, predicting the future, and more
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 37 Big announcement - Revealing my new book
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 38 Get Started in Time Series Forecasting in Python | Full Course
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The video teaches the importance of using libraries in ML and data science, and how implementing algorithms from scratch is not a common practice in industry settings. It also touches on the topic of interview preparation and the relevance of implementing algorithms from scratch in that context.

Key Takeaways
  1. Learn the basics of TensorFlow and PyTorch
  2. Understand how to implement ML algorithms using libraries
  3. Recognize the importance of library usage in industry settings
  4. Prepare for ML-related interviews by learning the inner workings of algorithms
💡 Implementing algorithms from scratch is not a common practice in industry settings, and using libraries is the preferred approach.

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