Machine Learning Tutorial Chap 5| Part-2 L1 Regularization

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Here is What You'll Learn

Understand the various problems of Linear Regression

Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable).

Understand Regularization and its types

This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this.

Understand Bias-Variance Trade-off

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.

Learn about ways to handle Non-Linear Data

Data structures where data elements are not arranged sequentially or linearly are called non-linear data structures. 

Distinguish between L1 and L2

Learn about Model Validation

Model validation is defined within regulatory guidance as “the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives, and business uses.”

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About Rohit Ghosh

Rohit Ghosh is a graduate of IIT-Bombay with over 5 years of experience as a data scientist. Rohit started his professional life working as a risk analyst at Nomura. It's here that he discovered his love for machine learning and decided to pivot into data science. After re-training as a data scientist, Rohit had a successful run working for ListUp and Data Science Labs. Post this stint, he decided to apply his data science skills to fix healthcare challenges. Thus, his first startup Qure.ai was born.

Qure.ai is a healthcare startup that leverages deep learning in radiology image processing. In addition to growing his startup, Rohit is also keen on learning about cryptocurrencies and reinforcement learning - an emerging niche in machine learning.

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