How to learn Python for Machine learning?

If you are new to Machine learning or Python and want to know how to learn Python for Machine learning, well, in this short article we will discuss how we can learn Python for machine learning.

Machine learning is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks. Python is one of the most popular languages for machine learning. Check why Python is for machine learning and data science. If you are new to Python and want to know what kind of things you need in Python to start programming in it focusing on machine learning. In this article we will discuss:

  • Introduction to Python
  • How to install Python and Jupyter Notebook
  • Basic syntax and concept of Python
  • handling errors
  • Importing modules
  • Popular machine-learning modules

Why use Python for Machine Learning?

In 2021, the worldwide machine learning market held an 88.71% market share. The reason why machine learning is so popular is that artificial intelligence may learn from its mistakes and enhance its functions, user interface, and forecasts with the use of machine learning. These days, many businesses may improve their business operations by using artificial intelligence and machine learning. 57% of customers indicated that their user experience has improved in 2021. Businesses began applying AI and ML to stay one step ahead of their rivals.

Many data science applications now only use Python. It blends the flexibility of domain-specific scripting languages like MATLAB or R with the power of general-purpose programming languages. Libraries for data loading, visualization, statistics, NLP, image processing, and other tasks are available in Python. Data scientists have access to a wide range of general- and specialized-purpose functionality thanks to this enormous toolkit.

The ability to interact directly with the code using a terminal or other tools like the Jupyter Notebook is one of the main advantages of using Python. Data drives machine learning and data analysis, which are fundamentally iterative processes. These processes must have access to technologies that enable quick iteration and simple engagement.

As a general-purpose programming language, Python also allows for the creation of
complex graphical user interfaces (GUIs) and web services, and for integration into
existing systems.

Here are some of the main and popular reasons for Python being a popular language.

  • It is easy to learn.
  • 100% compatible
  • The code is clear and short
  • Fast in development
  • It has a large number of libraries
  • It is an object-oriented language
  • It is open-source and is free and available
  • It is a high-level language
  • Data-structure is built-in

How to install Python on your system?

Before going to the implementation part, it is necessary to install the Python programming language on your system. Linux, Mac, Windows, and a number of other platforms all support Python. On macOS and the majority of Linux distributions, it is preloaded. However, downloading and installing the most recent version is probably necessary if you wish to stay current. If you’d like, you can also choose to use several Python versions in various projects.

You can check which version of Python is installed on your system by running the following command in the terminal:

Python3 --version

In my case I have the following version of Python installed on my system.


If the command gives an error, then most probably Python is not installed on your system. In that case, you can download Python from their official website.

Installing Jupyter Notebook

Once we install Python on our system, we need an IDE or text editor to write the Python code, save the file and run it. You can use any text editor you want to use, but if you are learning Python for Machine learning and data science, I will recommend using Jupyter Notebook to write and run Python code.

The Jupyter Notebook is an open-source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. You can install Jupyter Notebook on any operating system.

There are many ways to install Jupyter Notebook on your system. For example, you can either use Jupyter notebook by installing anaconda or you can directly install Jupyter Notebook by running the pip command. For the pip command, make sure that pip is installed on your system. Open the terminal and run the following command to install the Jupyter notebook.

pip install jupyter notebook

Once the Jupyter notebook is installed, run the following command to open it.


It will open the following window on your browser.


Now, click on the new button in the top right corner and then click on Python3 to create a new Jupyter notebook.

How to learn Python for machine learning

As you have installed Python and Jupyter Notebook on your system, it is now time to jump into the practical part and start writing the actual code in Python.

The good news is that Python is a very easy language to learn and anyone who is not familiar with Python or programming can guess what the program is going to do. For example, to print anything in Python, we use the keyword print followed by the message that we want to print.

# printing how to learn python
print("How to learn Python for machine learning?")



As you can see, the above code has successfully printed the message on the screen. Here are some different methods and data types that we can print using the print method.

# printing message
print("How to learn Python for machine learning")
# printing using variable
message = 'How to learn python for machine learning'
# printing numeric value
# printing operation
# printing message with number
age = 23
print("My age is :", age)



The next basic staff to learn is how to take messages from the user. In Python, we use the input keyword enclosed by the type of input that we expect from the user. For example, the following Python program will take the name of the user and then prints it:

# taking user input
name = input("what is your name? ")
# printing the name



But if you want to get any numeric value, you need to specify it as shown below. We will learn about the different data types in the upcoming section.

# taking the age of user
age = int(input("please enter your age "))
# printing 



Python statement

Instructions that a Python interpreter can execute are called statements. In other words, anything that is written in Python is called a statement. For example, conditional statements, while loop statements, for loop statements, etc.

Another important thing to note is that Python supports multi-line statements as well. For example, the following code shows the multi-statement.

# Python multiline statement
summation =  1 + 2 + 3 + \
    4 + 5 + 6 + \
    7 + 8 + 9

You can also write multi-line statements using brackets as well. We can use different types of brackets to write multi-line statements. For example, see the Python program below:

# multiline statement using brackets
summation = (1 + 2 + 3 +
    4 + 5 + 6 +
    7 + 8 + 9)
# multi-line statement using square brackets
colors = ['red',

As you can see, we have multi-line statements.

Python comments

Comments in Python are the inclusion of short descriptions along with the code to increase its readability. A developer uses them to write his or her thought process while writing the code. It explains the basic logic behind why a particular line of code was written.

Python interpreter ignores the comments. A single-line comment is written after the # symbol as shown below:

# Python comment - how to learn python for machine learning
print("How to learn python for machine learning")
# this line will also be ignore by python

The first and last lines in the above program will be ignored by the Python interpreter as they are comments.

Python also supports multi-line comments as well. For example,

'''This is python multi-line commnet
We will print how to learn python in machine learning
in the next line'''
print("how to learn python for machine learning")

In the above program, only the last line will be executed because the first three lines are part of the multi-line comment.

Variables in Python

A Python variable is a symbolic name that is a reference or pointer to an object. Once an object is assigned to a variable, you can refer to the object by that name. We already have declared variables in the above sections.

A variable can store any data type. It can also be used to store different data structures as well.

The following variables store different data types.

# Python variables
name = 'bashir'
age = 23
colors = ['red', 'blue', 'black']

Officially, variable names in Python can be any length and can consist of uppercase and lowercase letters (A-Za-z), digits (0-9), and the underscore character (_). An additional restriction is that, although a variable name can contain digits, the first character of a variable name cannot be a digit.

Keywords in Python

Python keywords are special reserved words that have specific meanings and purposes and can’t be used for anything but those specific purposes. These keywords are always available—you’ll never have to import them into your code.

Another important thing to note is that we are not allowed to use the keyword as variable names because then there will be conflict in the names.

You can print these keywords in Python using the keyword library as shown below:

# importing keywords
import keyword
# storing the keywords in variable
s = keyword.kwlist
# printing the keywords



As you can see, we got the list of all the keywords in Python.

Data types in Python

Data types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data. Since everything is an object in Python programming, data types are actually classes and variables are instances (objects) of these classes.

The following are some of the built-in data types in Python.

  • Numeric
    • Float
    • integers
    • complex
  • Sequence Type
    • strings
    • list
    • tuples
  • Boolean
  • Set
  • Dictionary
# floating number
num = 8.343
# integer
integer = 4
# complex number 
import math
z = complex(23,3)
# string in python
string = 'how to learn python in machine learning'
# python list
list1 = [1,2,3,4,5,5]
# python tuples
tuple1 = (1,2,3,4,5,6)
# boolean
x = True
y = False
# set in python
set1 = set([1,2,3,4])
# dictionary in Python
dic1 = {1:3, 4:2, 8:3}

As you can see, we have declared and initialized various data types in the above program.

Various types of loops in Python

In computer programming, a loop is a sequence of instructions that is continually repeated until a certain condition is reached. Typically, a certain process is done, such as getting an item of data and changing it, and then some condition is checked such as whether a counter has reached a prescribed number.

In Python, there are mainly two types of loops.

  • for loop
  • While loop

A for loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string). This is less like the for keyword in other programming languages and works more like an iterator method as found in other object-orientated programming languages. Let’s take an example to see how the for loop in Python is working.

# for loop to print numbers 
for i in range(10):



As you can see, the for loop iterated through the specified range and printed the numbers. It can also be used to iterate through an iterable (list, set, or dictionary) to print the elements.

# list
list1 = [2,3,5,6,3,6,2,7,4,7]
# for loop
for i in list1:



The while loop in Python is used to iterate over a block of code as long as the test expression (condition) is true. We generally use this loop when we don’t know the number of times to iterate beforehand. Each time it iterates the loop, it checks the condition and if the condition is satisfied, the loop will be executed, and if not then the loop will be terminated.

# condition
num = 0
# while loop
while num <7:



As you can see, we have defined the condition to be less than 7 and when the num was greater or equal to 7, the loop was terminated.

Function in Python

A function is simply a “chunk” of code that you can use over and over again, rather than writing it out multiple times. Functions enable programmers to break down or decompose a problem into smaller chunks, each of which performs a particular task.

Similar to any other programming language, in Python, we can create our own function that can perform some specific tasks, and each time we run the function, the specified task will be executed and we don’t need to write the code again and again for the same task.

Let us create a function that takes a number and multiplied it by 100 and returns the product.

# creating a function
def main(num):
    # returning the product
    return num*100
# calling the function



As you can see, the def keyword is used to define a user-defined function in Python followed by the name of the function.

Build-in functions

Built-in functions are ones for which the compiler generates inline code at compile time. Every call to a built-in function eliminates a runtime call to the function having the same name in the dynamic library. In other words, built-in functions are those functions that had already been created for us and we don’t need to write code for them. All we need to do is just call those functions.

Examples of build-in functions in Python are print(), input(), int(), float(), sum(), max(), and min().

# list 
list1 = [1,2,3,4,5,6,7,8]
# printing max



As you can see, the max() function has returned the maximum number from the given list.

How to use Modules in Python?

In Python, Modules are simply files with the “. py” extension containing Python code that can be imported inside another Python Program. In simple terms, we can consider a module to be the same as a code library or a file that contains a set of functions that you want to include in your application

We don’t need to write all code by ourselves, in Python we have a large number of already build modules that we can import and use in our program. For example, if we want to find the square root of a number, we can import the math module and use the sqrt() function from there.

# importing math module
import math
# printing the square root



As you can see, we first imported the math module and then used the sqrt() function from it to find the square root of the number.

List of Machine learning modules

If you are interested in learning Python for machine learning, then we will recommend you have basic knowledge of the following Python modules.

  • Pandas: They can be used to manipulate and visualize data frames
  • NumPy: Can be used to perform many mathematical operations on the data.
  • sklearn: It contains a lot of useful stuff for machine learning. It has a large number of machine-learning algorithms including KNN, SVM, decision trees, random forests, isolation trees, Ada boost, Gradient boosting, and many more. At the same time, it also contains many evaluation matrices that can be used to evaluate the performance of models.
  • Matplotlib: It can be used to visualize data in 2D and 3-D.
  • seaborn: It is also used to visualize datasets.


Python is truly great with its frameworks, libraries, and community support. As a programming language, it is fast, and easy to learn, with clear code and amazing compatibility. It is easy to learn and has tons of built-in modules that we can use. In this article, we give an introduction to Python and discuss how to learn Python for machine learning.

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