6 Predictive Models that every Data Scientist Should Master
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Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This video brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
📰 References:
📈 Linear Regression:
- https://statisticsbyjim.com/regression/linear-regression/
- https://towardsai.net/p/machine-learning/closed-form-and-gradient-descent-regression-explained-with-python
- https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
📈 Logistic Regression:
- https://towardsdatascience.com/logistic-regression-explained-from-scratch-visually-mathematically-and-programmatically-eb83520fdf9a
🌳 Decision trees:
- https://365datascience.com/tutorials/machine-learning-tutorials/overfitting-underfitting/
🌳🌳 Random Forest:
- https://towardsdatascience.com/a-visual-guide-to-random-forests-b3965f453135
- https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
🌳🚀 Boosting Models
- https://xgboost.readthedocs.io/en/stable/
- https://lightgbm.readthedocs.io/en/latest/index.html
- https://towardsdatascience.com/all-you-need-to-know-about-gradient-boosting-algorithm-part-1-regression-2520a34a502
🧠Neural Network:
- https://towardsdatascience.com/first-neural-network-for-beginners-explained-with-code-4cfd37e06eaf
- https://www.hackerrank.com/blog/types-of-neural-networks-explained/
- https://theaisummer.com/attention/
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🎶🎷 bedtime after a coffee by Barradeen | / barradeen Creative Commons Attribution-ShareAlike 3.0 Unported https://creativecommons.org/licenses/... Music promoted by https://www.chosic.com/free-music/all/
✍️ My Substack: https://thedatajourney.substack.com/
🐍 Udemy’s Python For Absolute Beginners: https://www.udemy.com/course/the-python-for-absolute-beginners-bootcamp/?referralCode=8D25992A055C19079B8A
🧑🏫 Udemy Profile: https://www.udemy.com/user/ivo-bernardo/
✍️ My Medium: https://ivopbernardo.medium.com/
🌍 My LinkedIn: https://pt.linkedin.com/in/ivobernardo/en
————————————————————————————————-
Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This video brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
📰 References:
📈 Linear Regression:
– https://statisticsbyjim.com/regression/linear-regression/
– https://towardsai.net/p/machine-learning/closed-form-and-gradient-descent-regression-explained-with-python
– https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
📈 Logistic Regression:
– https://towardsdatascience.com/logistic-regression-explained-from-scratch-visually-mathematically-and-programmatically-eb83520fdf9a
🌳 Decision trees:
– https://365datascience.com/tutorials/machine-learning-tutorials/overfitting-underfitting/
🌳🌳 Random Forest:
– https://towardsdatascience.com/a-visual-guide-to-random-forests-b3965f453135
– https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
🌳🚀 Boosting Models
– https://xgboost.readthedocs.io/en/stable/
– https://lightgbm.readthedocs.io/en/latest/index.html
– https://towardsdatascience.com/all-you-need-to-know-about-gradient-boosting-algorithm-part-1-regression-2520a34a502
🧠Neural Network:
– https://towardsdatascience.com/first-neural-network-for-beginners-explained-with-code-4cfd37e06eaf
– https://www.hackerrank.com/blog/types-of-neural-networks-explained/
– https://theaisummer.com/attention/
_____________________________________________________________________
🎶🎷 bedtime after a coffee by Barradeen | / barradeen Creative Commons Attribution-ShareAlike 3.0 Unported https://creativecommons.org/licenses/… Music promoted by https://www.chosic.com/free-music/all/
