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


# Feature scaling

Feature scaling comes under Feature Engineering. Feature scaling refers to the process of normalizing the features, or columns, or dimensions. Many Machine Learning algorithms are sensitive to the scale or magnitude of the features.

## Benefits

It has the following benefits

* It helps in gradient descent based algorithms to converge faster.

* It helps in distance-based algorithms to give equal weight to each feature while computing the similarity.

* It helps to compare the Feature Importance. 


> Distance-Based Algorithms take into account the distance or similarity between instances of the dataset to do the computation.

## Types of feature scaling

There are these two famous types of feature scaling. 

1. Normalization: It involves rescaling the values of features in the range between 0 and 1. **Normalization is a good technique to use if someone doesn't know the distribution of the input columns or the distribution is not Gaussian.** The formula for it is:

$$X' = \frac{X - X_{min}}{X_{max} - X_{min}}$$

* $X'$ is the new rescaled value formed from $X$.
* $X_{min}$ is the minimum value in the corresponding feature.

* $X_{max}$ is the maximum value in the corresponding feature.

* The above formula maps the values $X$ in the range of 0 and 1.

 
2. Standardization: Values in the features are standardized in such a way to have a mean of 0 and a standard deviation of 1. **Standardization assumes that the values in the column are normally distributed. This condition doesn't have to be strictly true, but if the distribution is normal then it is good.** The formula for it is:


$$X' = \frac{X - \mu}{\sigma}$$
 

* $X'$ is the new rescaled value formed from $X$.

* $\mu$ is the mean of the relevant feature.

* $\sigma$ is the standard deviation of the relevant feature.





In this lesson, we'll uncover the details surrounding Feature Scaling, which is an important step before training the models because it speeds up the computations while building the model. 

Feature Scaling

In this lesson, we'll uncover the details surrounding Feature Scaling, which is an important step before training the models because it speeds up the computations while building 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 Scaling

Feature scaling

Benefits