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

## Numpy array creation operations

### `np.empty`
It creates a Numpy array of specified shape and size without initializing it.

### `np.eye`
It creates a 2-dimensional Numpy array with diagonal entries as ones and the rest as zeros. It takes in a parameter `k`, which is zero by default. This means that the main diagonal entries are made as one. A positive value indicates making the upper diagonal entries as one, and a negative value indicates making the lower diagonal entries as one.

### `np.identity`

It makes an identity array. An identity array is a square array, meaning the number of rows and columns are equal with ones on the main diagonal.

### `np.linspace`

It returns an evenly spaced array over a specified interval. It has a start and a stop values that indicates the first and the last value in the sequence. We can exclude/include the stop of the interval specified by setting a parameter. We will discuss more on this in the coding example.

### `np.ones`

It makes an array of a specified shape and size initialized with only ones.

### `np.zeros`
 
It makes an array of specified shape and size initialized with only zeros.


## Coding Exercise






# Numpy array creation operations

## `np.empty`
It creates a Numpy array of specified shape and size without initializing it.

## `np.eye`
It creates a 2-dimensional Numpy array with diagonal entries as ones and the rest as zeros. It takes in a parameter `k`, which is zero by default. This means that the main diagonal entries are made as one. A positive value indicates making the upper diagonal entries as one, and a negative value indicates making the lower diagonal entries as one.

## `np.identity`

It makes an identity array. An identity array is a square array, meaning the number of rows and columns are equal with ones on the main diagonal.

## `np.linspace`

It returns an evenly spaced array over a specified interval. It has a start and a stop values that indicates the first and the last value in the sequence. We can exclude/include the stop of the interval specified by setting a parameter. We will discuss more on this in the coding example.

## `np.ones`

It makes an array of a specified shape and size initialized with only ones.

## `np.zeros`
 
It makes an array of specified shape and size initialized with only zeros.


# Coding Exercise






This lesson will reveal more about Numpy and the functions that operate on them.


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 ?

Numpy Array Creation

Numpy array creation operations

`np.empty`

`np.eye`