Machine Learning Tutorial Ch 5| Part-4 Bias-Variance Trade off | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 2019-06-06

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist, and an Instructor at GreyAtom will take us through polynomial regression in machine learning through a simple introduction series. 

Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data.

Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data.

 If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data.

This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time.


Study Bias Variance Trade Off in Machine Learning through this introductory session for beginners. 

This is the 4th in 4 videos about Advance Machine Learning in Machine Learning. 

In this video, we will explore the limitations of linear regression and need of polynomial regression in machine learning. 

Complete Playlist for the Course: https://bit.ly/2Q1zvK6 

After completing our 4-part Series on Polynomial Regression in Machine Learning, you will be able to do the following:

Understand the various problems of Linear Regression
Learn about ways to handle Non-Linear Data
Understand Regularization and its types
Distinguish between L1 and L2
Understand Bias-Variance Trade-off
Learn about Model Validation


Here’s the full syllabus of our 4-part video on Polynomial Regression in Machine Learning:


Limitations of linear regression 
Polynomial Basis Function
Regularization in machine learning
     - L1 Regularization
     - L2 Regularization
     - L1 vs L2
     - Elastic Net Regularization
Bias-variance trade-off
Model Validation

#machinelearningtutorial #polynomialregression #DataScience101 #Greyatom

Please feel free to post your doubts, questions, feedback in the Comments section and we will sure to get back to you. 

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