R Programming 2023: Hands on R Programming for Beginners
Learn R Programming and R Studio. Data Analytics. Data Science. Data Visualization. Packages: GGPlot2, Dplyr, StringR
Rating ⭐️: 4.5 out 5
Students 👨🎓 : 5,603
Duration ⏰ : 1hr 53min of on-demand video
Created by 👨🏫: Prashanth Chidambaram
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#Data_Science #data_analytics #R
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Learn R Programming and R Studio. Data Analytics. Data Science. Data Visualization. Packages: GGPlot2, Dplyr, StringR
Rating ⭐️: 4.5 out 5
Students 👨🎓 : 5,603
Duration ⏰ : 1hr 53min of on-demand video
Created by 👨🏫: Prashanth Chidambaram
🔗 Course Link
#Data_Science #data_analytics #R
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Udemy
Free Data Analysis Tutorial - R Programming 2023: Hands on R Programming for Beginners
Learn R Programming and R Studio. Data Analytics. Data Science. Data Visualization. Packages: GGPlot2, Dplyr, StringR - Free Course
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Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc.
Creator: Weijie Chen
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GitHub - weijie-chen/Linear-Algebra-With-Python: Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes…
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative ski...
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Understanding Bias and Variance in Machine Learning
Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).
Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)
There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting.
Techniques like cross validation can be helpful in these cases.
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Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).
Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)
There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting.
Techniques like cross validation can be helpful in these cases.
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