Machine Learning in Python: Introduction, Steps, and Benefits

6 Min Read. |

Machine learning in Python provides computers with the ability to learn without being programmed explicitly. 

Machine learning, which is a type of artificial intelligence, has its main focus on developing computer programs that are dynamic to new data. 

Python is a high-level programming language whose main emphasis is on code readability. 

Python is a very dynamic language. It is designed to support several programming paradigms that include procedural structure, functional programming, and object-orientation. This language is supported by many operating systems and is very flexible to use. 

Python is generally called a ‘Batteries included’ programming language because it provides good standard libraries. There are several modules created for programmers to help them with the implementation of machine learning in Python. 

Machine learning requires computer training, uses a data set, and further uses the training to make predictions of the new data given. It is always important to have basic python knowledge to know how to use machine learning in Python. 

Things to learn before setting up Machine Learning 

Python provides libraries like NumPy, scipy, and scikit. These can be installed by using a particular command, i.e. – 

‘pip install NumPy scipy scikit-learn’

To understand Python’s machine learning, there are several things you need to learn and understand about Python before going any further.

Python is a vast subject and requires a lot of knowledge, as it is a high-level programming language. If you are trying to learn how to use machine learning in Python, it can be daunting. 

Here are a few things you need to follow up before starting Python – 

  • Learn and understand Python and machine learning concepts. 
  • Learn data analysis, visualization, and manipulation with Numpy, Scipy 
  • Start learning machine learning with scikit-learn (a python library that has many different and helpful machine learning algorithms) 
  • Read books about Python and how it works, how to implement it, and the way of coding. 

Each step here takes a long time, say six months each. To learn machine learning in Python takes a lot of time because it is difficult and has too much information. Build up foundational knowledge through different courses and referring resources. 

This is because the Python programming language is vast, and you should have a piece of good knowledge about it. You should not learn everything in one go, but go slow and try the codes regularly to understand and analyze the different errors that might occur and how the programming language works. 

Steps to set up Machine Learning in Python 

After you are done learning, the main question is how to use Python machine learning? There are certain steps one has to follow to set it up –

(NOTE: these steps involve the SciPy platform)

  • Download, install, and start Python SciPy if not already. 
  • Load the data – remember, while importing the files, you need to load everything without an error. The dataset should also load without any incident. 
  • Summarize the datasets, i.e., take a close look at your data and check the dimensions, statistical summary, and data breakdown. 
  • Visualization of the data. Now that there is a basic idea about the data ready with you, look into two types of plots – Univariate plots, i.e., to understand and analyze each attribute, and Multivariate plots, to understand and analyze the relationships in between the attributes. 
  • Evaluation of algorithms. Here you need to check the accuracy of the data you have just collected. 
  • Make and evaluate predictions about your module and compare the predictions to the expected results in the validation set. 

(NOTE: the below-mentioned steps now involve the Anaconda platform)

  • Install Anaconda Python software 
  • Remember to update your Anaconda. To update it, you need to type in these commands in the prompt – 

command update conda

conda update — all

  • Create the right environment for Anaconda. Open the Anaconda prompt and create an environment of any name you want. Activate the environment by using the ‘activate’ command. 
  • Install the deep learning libraries for the smooth working of Python. 

After all these steps, your machine learning in Python is set up. 

You can use either of the steps and step up your machine learning environment. Remember, if you are a beginner, the coding might not go right. You will face many errors in the beginning, and it will create a lot of confusion. But start with the coding first. 

Try to run the things without any errors and then get into the statistical analysis of everything. This will help you know how to use machine learning in Python easily if you can run your code properly. 

Remember, you can always use ‘help(“FunctionName”) in Python and get any help if you have a problem with any function. 

Why use Machine Learning in Python? 

Machine learning is something that holds the future. We want more personalization options, good recommendations, and smart search functionality. 

Now the question arises – what programming language is best for machine learning? The answer to this is Python. It is best to learn machine learning in Python. 

Machine learning projects require deep research. For the implementation of your machine learning aspirations, the use of a stable and flexible programming language is important. Python does not fail to offer these features, and therefore we see a lot of people learning how to use machine learning in Python projects these days. 

Simplicity, consistency, flexibility, access to frameworks and libraries for machine learning, platform independence, and a really wide community are a few benefits of Python to make it the best fit for machine learning. 

Machine learning makes the work easier and helps you explore all Python features to make your project better. Python offers readable code, and therefore developers find it easier to solve any machine learning problem and put all their efforts into it than going into the technical complications.

Additionally, python is a human-readable language and makes it easier for the developers to build the models. For beginners, it is easier to learn machine learning in Python than other computer languages. Yes, it takes time to learn and understand everything, but it is much easier and understandable than other computer languages. 

To implement the codes for machine language can be a bit tricky and can consume a lot of time; it is necessary to have a well-tested environment for the best coding solutions. To save time, programmers use libraries and frameworks. This saves time and helps them program the right code. 

Many programmers say that languages like C, C++, and Java are much more difficult to understand, but Python’s syntax is easy and simpler, and it has a lot of code libraries that one can use to understand the functions and codes that one finds difficult to understand. Though Python is a bit slower than other programming languages, the capacity to handle the data is very convenient, and the users find it flexible enough. 

Unlike other programming languages, Python can interact with all the third-party platforms. Machine learning makes the computer do the tasks without any explicit programming that requires a dynamic language like Python. 

Summing Up!

Python is a programming language that allows the developers to implement things in one system and use them in another machine without making any changes. If you are a beginner, it is not that you cannot learn how to use machine learning in Python. 

Your main aim is to run the program without any errors. You are not asked to learn everything in one go. Make heavy use of ‘help(“FunctionName”)’ syntax to understand different functions you are using in machine learning in Python. 

It is also not important to know about the working of the algorithms, but you need to learn about certain limitations and the configuration of the algorithms of machine learning. The python language is easy for beginners to understand. It is intuitive. You don’t have to be an expert or a programmer to understand this language. Using function calls and assignments 

Don’t think that you need to be an expert in learning how to use machine learning in Python. That’s not the case. You need to know the basics. You have to know about how to set up Python and a few programming codes; the other algorithms can be dealt with later. There are a lot of steps that one has to follow while setting up and knowing about machine learning in Python. But in the beginning, you need to brush up your skills with basics and try running the coding first. 

According to many programmers and developers, it took them too long to learn and get the coding right. But it would help if you were slow in the beginning because understanding a programming language takes time. 

You might get your codes wrong in the beginning, and it will be filled with a lot of errors but learning machine learning in Python requires deep knowledge because it is a vast topic to be covered. If you also want to learn and master Python, a Data Science course will help you get trained and be certified in machine learning using Python.

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