Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning


# Intrinsic or Embedded Methods

Embedded methods learn about the features that contribute the most to the model's performance while the model is being created. You have seen Feature Selection methods in the previous lessons, and we will discuss several more in future lessons, like Decision Tree based methods. 

* Ridge Regression (L2-Regularization)
* Lasso Regression (L1-Regularization)
* Elastic-Net Regression (uses both L1 and L2 Regularization)

* Decision Tree-Based Methods (Decision Tree Classification, Random Forest Classification, XgBoost Classification, LightGBM).

We know regularization reduces some of the parameters in the equation below to zero. This property of regularization methods can be used as a Feature Selection Method.

$y = w_0 + x_1w_1 + x_2w_2 + x_3w_3... +x_nw_n$

## Scikit Learn implementation

We have already covered the implementation of regularization models like Ridge, Lasso, and Elastic-net regression in the previous lessons.

* Scikit Learn provides a `SelectFromModel` class. 

* It is used with models that provide `coef_` or `feature_importance_` attributes. 

* It takes in a `threshold` parameter. 

* The features are considered unimportant and removed if the corresponding `coef_` or `feature_importances_` values are below the provided `threshold` parameter. 





Feature Selection refers to the process of selecting the most appropriate features for making the model. Here, you can learn all about it.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Feature Selection (Intrinsic Methods)

Intrinsic or Embedded Methods