Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Creator: Goku Mohandas
Stars ⭐️: 34.8k
Forked By: 5.6k
https://github.com/GokuMohandas/Made-With-ML
#ml #machinelarning #datascience
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Learn how to design, develop, deploy and iterate on production-grade ML applications.
Creator: Goku Mohandas
Stars ⭐️: 34.8k
Forked By: 5.6k
https://github.com/GokuMohandas/Made-With-ML
#ml #machinelarning #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - GokuMohandas/Made-With-ML: Learn how to design, develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications. - GokuMohandas/Made-With-ML
Probability and Statistics cheat sheets for data science
By Stanford
https://stanford.edu/~shervine/teaching/cme-106/
By Stanford
https://stanford.edu/~shervine/teaching/cme-106/
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
🔗 Course Link
#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
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👉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
Forwarded from Cool GitHub repositories
weijie-chen/Linear-Algebra-With-Python
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
Stars ⭐️: 1.9k
Forked By: 456
https://github.com/weijie-chen/Linear-Algebra-With-Python
#Linear_Algebra #Python
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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
Stars ⭐️: 1.9k
Forked By: 456
https://github.com/weijie-chen/Linear-Algebra-With-Python
#Linear_Algebra #Python
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GitHub
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...
HDFS (Hadoop Distributed File System) is the primary storage system used by Hadoop applications. This open source framework works by rapidly transferring data between nodes. It's often used by companies who need to handle and store big data.
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.
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group