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.

new.tar.gz

data_science_notebooks

docker_env

data_science_notebooks-copy

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


# Wrapper Methods

In Wrapper Methods the problem of Feature Selection is reduced to a search problem. A model is built using a set of features and its accuracy is recorded. Based on the accuracy, more features are added or removed, and the process is repeated. We have the following methods in Wrapper Methods. 

## Forward Selection

Forward Selection is an iterative method. In this method, we start with one feature and we keep on adding features until no improvement in the model is observed. The search is stopped after a pre-set criteria is met. This is a greedy approach because it always targets the features in a forward fashion, which gives a boost to the performance. If the number of features are large, it can be computationally expensive.

## Backward Elimination 

This process is the opposite of the Forward Selection Method. It starts initially with all the features and keeps on removing features until no improvement is observed.  

## Exhaustive Search 

This Feature Selection Method tries all the possible combinations of features to select the best model. This method is quite computationally expensive. For example, if we have five features, we will be evaluating $2^5 = 32$ models before finalizing a model with good accuracy. 

## Recursive Feature Elimination

Recursively Feature Elimination (RFE) involves the following steps:

1. We train the model on the initial set of features and the importance of each feature is calculated. 
2. In the second iteration, a model is built again using the most important features and excluding the least important features. 
3. These steps are repeated recursively until we are left with the most important features for the problem under consideration.

Scikit learn library provides functions for Recursive Feature Elimination. 



In this lesson, you’ll learn about Feature Selection, which refers to the process of selecting the most appropriate features for making the model.


Feature Selection (Filter Methods)

In this lesson, you’ll learn about Feature Selection, which refers to the process of selecting the most appropriate features for making the model.

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 (Wrapper Methods)

Wrapper Methods