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Introduction
Python provides numerous functionalities to implement machine learning, with the help of different python libraries.
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NumPy
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1. numpy.type()Syntax:
``type(numpy.ndarray)``

This function is used to return the type of the parameter passed. In the case of the NumPy array, it will return numpy.ndarray.

```import numpy as np
a = np.array([[1,2,3],[4,5,6]])
print(type(a)) ```

The above code will return numpy.ndarray

2. numpy.zeros()Syntax:
`numpy.zeros((rows,columns), dtype)`

This function will create a numpy array of the given dimensions with each item being zero. If no dtype is specified, default dtype is taken.

```import numpy as np
np.zeros((3,3))
print(a)```

The above code will result in a 3×3 numpy array with each item is zero.

3. numpy.ones()Syntax:
`numpy.ones((rows,columns), dtype)`

The above function will create a numpy array of the given dimensions. If no dtype is specified with each item being one, the default dtype is taken.

```import numpy as np
np.ones((3,3))
print(a)```

The above code will return a 3×3 numpy array with each item being one.

4. numpy.empty()Syntax
`numpy.empty((rows,columns)) `

The above function creates an array whose initial content is random and depends on the state of the memory.

```import numpy as np
np.empty((3,3))
print(a) ```

The above code will result in a 3×3 numpy array with each item being random.

5. numpy.arange()Syntax:
`numpy.arange(start, stop, step)`

This function is used to make a numpy array with items in the range between the start and stop value with the difference of step value.

```import numpy as np
a=np.arange(5,25,4)
print(a)```

The output of the above code will be [ 5 9 13 17 21 ]

6. numpy.linspace()Syntax:
`numpy.linspace(start, stop, num_of_elements)`

This function is used to make a numpy array with items in the range between the start and stop value and num_of_elements as the size of the numpy array. The default dtype of the numpy array is float64. All the items will be spanned over the logarithmic scale i.e the resulting elements are the log of the corresponding item.

```import numpy as np
a=np.linspace(5,25,5)
print(a) ```

The output of the above code will be [ 5 10 15 20 25 ]

7. numpy.logspace()Syntax:
`numpy.logspace(start, stop, num_of_elements)`

The above function is used to make a numpy array with elements in the range between the start and stop value and num_of_elements as the size of the numpy array. The default dtype of the numpy array is float64. All the elements will be spanned over the logarithmic scale i.e the resulting elements are the log of the corresponding element.

```import numpy as np
a=np.logspace(5,25,5)
print(a)```

The output of the above code will be [1.e+05 1.e+10 1.e+15 1.e+20 1.e+25]

8. numpy.sin()Syntax:
`numpy.sin(numpy.ndarray)`

This function will return the sin of the provided parameter.

```import numpy as np
a=np.logspace(5,25,2)
print(np.sin(a)) ```

The output of the above code will be [ 0.0357488 -0.3052578]

Similarly, there are cos() ,tan(), etc.

9. numpy.reshape()Syntax:
`numpy.resahpe(dimensions)`

This function is used to change the dimension of a numpy array. The number of arguments in the reshape determines the dimensions of the numpy array.

```import numpy as np
a=np.arange(9).reshape(3,3)
print(a)```

The output of the above code will be a 2D array with 3×3 dimensions

10. numpy.random.random()Syntax:
`numpy.random.random( (rows, column) )`

This function is used to return a numpy ndarray with the given dimensions and each item of ndarray being randomly generated.

`a = np.random.random((2,2))  `

The above code will return a 2×2 ndarray

11. numpy.exp()Syntax:
`numpy.exp(numpy.ndarray)`

This function returns a ndarray with exponential of every item

`b = np.exp([10]) `

The above code returns the value 22026.4657948

12. numpy.sqrt()Syntax:
`numpy.sqrt(numpy.ndarray)`

This function returns a ndarray with the ex of every item

`b = np.sqrt([16])`

The above code returns the value 4