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Python Machine Learning Tutorial

Python Machine Learning Tutorial

Machine Learning using Python is a vast subject to study completely. In this Python Machine Learning tutorial, we will try to include as many topics as we can, and here is the list of the topics that we are going to discuss:

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Let’s start with an introduction to Machine Learning using Python.

Introduction to Machine Learning

Machine Learning is nothing but making machines learn and think and act like humans. It is the process of enabling machines to learn from past experiences and to improve the accuracy of outputs over time. It is hard for anyone to program each and every task, right? But with Machine Learning, you do not need to program each task; instead, the computer itself develops an algorithm according to the task to be performed using the available data.

Here, we will walk you through a live example from our daily lives. One of the most seen Machine Learning applications is online product recommendations. You may have noticed that when you search or checkout for a product on any online shopping site, you will start seeing the same product or related ads on multiple sites that you browse, such as while watching a video on YouTube or using Google Chrome. This is an advanced application of Machine Learning.

But why Machine Learning?

Why Machine Learning?

To answer this question, first, we need to know how it was without it. What was the scenario before humans could come up with Machine Learning? To make any decision, humans had to work hard and struggle. How far can humans think? How far can a human remember all the data and make a perfect decision? Even after working hard, the outcomes were not satisfactory.

But, luckily, humans always come up with the latest technologies each day, and Machine Learning is one of them. Here, humans could make a machine learn so that it can make its own decisions based on the data and previous experiences with high efficiency and accuracy.

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Now, let’s learn why Python is the better choice for Machine Learning.

Why Python for Machine Learning?

Python is the most commonly used programming language for Machine Learning. But why? Why is Machine Learning using Python? Let’s try and answer this question.

Python has lately become the backbone of Machine Learning. It is an easy-to-use programming language compared to any other object-oriented language. It is often used for data mining and data analysis and supports the implementation of a wide range of Machine Learning models and algorithms. It is famous for its readability, and it also offers platform independence, which means that you can use the same code for any machine without changes. All of these make it the perfect language of choice for Machine Learning.

Now, Let’s talk about why many ML engineers are preferring Machine Learning using Python.

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Why is Python desired by many ML Engineers?

Most Machine Learning Engineers prefer the Python language for Machine Learning. Because as ML Engineers, they are responsible for data extraction, data processing, data refining, and understanding of the data to implement in various algorithms. So, they need a programming language that is easy to understand and helps them implement Machine Learning algorithms quickly. They need a language that helps them validate the algorithms instantly, and Python offers all these features. So, they implement projects of ML using Python. Python also has a few more advantages as mentioned below:

  • Python has a great library system.
  • It has a low-entry barrier.
  • Python is flexible and versatile.
  • It offers platform independence.
  • It has multiple visualization options.
  • Python is highly popular.

According to Stack Overflow,  the most widely preferred programming language is Python, and its usage is going to increase in the coming years.

Growth of Major Programming Languages

Let’s look into some most important Python libraries for Machine learning.


Python Libraries for Machine Learning

Python and Machine Learning are related to each other. To make projects in Machine Learning using Python, you have to learn Python and be aware of the most widely-used Python libraries. They are as follows:

  • SciPy: SciPy contains different modules for optimization, linear algebra, integration, and statistics. It is mostly used for image manipulation and scientific computations.

SciPy uses a multi-dimensional array given by the NumPy module as its underlying data structure. The array manipulation subroutines in SciPy are based on NumPy. SciPy is a Python library that was designed to work with NumPy arrays while also providing user-friendly and powerful numerical functions.

  • NumPy: For Machine Learning, NumPy is used for fundamental numerical computations such as linear algebra, Fourier transform, and random number capabilities.

NumPy allows you to identify arbitrary data types and integrate with most databases with ease. NumPy can also be used as a multidimensional array for any generic data, regardless of the data type. The strong N-dimensional array object, broadcasting functions, and out-of-the-box tools to incorporate C/C++ and Fortran code are just a few of NumPy’s highlights.

  • Matplotlib: Matplotlib has a MATLAB-like user interface and is extremely easy to use. It is used for the visualization of patterns in data. It provides various kinds of plots, charts, and graphs for data visualization.

Matplotlib works by providing an object-oriented API that allows programmers to integrate graphs and plots into their applications using standard GUI toolkits, such as GTK+, wxPython, Tkinter, or Qt.

  • Pandas: Data analysis can be done using Pandas. As mentioned earlier, before training machines, datasets must be prepared. For data extraction and preparation of datasets, Pandas are highly useful.

Pandas support quick, scalable, and expressive data structures for data analysis. It includes various types of data such as tabular data, organized and unordered data, arbitrary mix data, and any other type of statistical or observational datasets.

  • OpenCV: The purpose of the OpenCV library is to solve computer vision problems. From sorting images and videos to advanced robotic vision techniques, OpenCV is leveraged.

When OpenCV is combined with other libraries, such as NumPy, a highly optimized library for numerical operations with a MATLAB-style syntax, the number of arms in your arsenal increases as every operation that NumPy may do can be combined with OpenCV. This makes it easier to integrate with other NumPy-based libraries, such as SciPy and Matplotlib.

Hence, we have discussed what is Machine Learning in Python and what are its libraries. Now, let’s see the types of Machine Learning.

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Types of Machine Learning

Before looking into the types of Machine Learning. Let’s see the different types of data machines have to deal with. There are two types of data: Labeled data and unlabeled data

The labeled data is the data that is in a complete machine-readable format with both input and output parameters specified, but labeling the data requires human labor.

The unlabeled data is a type of data in which one or no parameters are in the machine-readable format. Though it does not require human intervention, the processing of unlabelled data is more complex.

Now, coming to the types of Machine Learning, this classification is done based on the ways used to train a machine. The pyramid below explains the types of Machine Learning. As you can see, the unsupervised learning method occupies more space as it is the most used model for Machine Learning. Let’s discuss all the types in detail further.

Types of Machine Learning

Supervised Learning

In supervised learning, as the name indicates, a supervisor is involved who helps the machine get trained. A human (supervisor) provides well-labeled input and output data to help the machine learn and predict. This labeled data will also help the machine understand the patterns in it.

Supervised learning is further classified into classification and regression.

  • Classification is based on the predictions of continuous values.
  • Regression is based on the predictions of discrete values.

Let’s look into an example here. Imagine you have fed a machine with labeled data of pen and book images, and you want to split the data into two parts. Here, the machine learns from the labeled data you have provided. It understands the difference between a pen and a book based on their shape and size, and then, based on its learning, it differentiates objects into two groups.

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Unsupervised Learning

Unlike supervised learning, no human intervention is required in unsupervised learning. Machines automatically train themselves in this type of learning without any human involvement. There will not be any labeled dataset, and also, the output will be unknown. The only way a machine learns here is through experience. The machine makes its own decisions using the trial-and-error method. It learns from its past mistakes and tries the next time not to make the same errors again. This is how unsupervised learning works without any human interference. This type is also classified into two:

  • Clustering: It is the method of dividing objects into clusters of similar objects.
  • Association: It is discovering the probability of the occurrence of an item in a collection.

Let’s consider the same pen and book example. Unlike in the previous instance, here, the input data is not labeled. You feed the machine with unlabeled input data with which the machine learns by itself. Then, when you give a new image to the machine, it classifies the image based on its characteristics of it. So, in unsupervised learning, even if you do not give names to the images, the machine learns by itself based on the similarities and dissimilarities of objects.

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Semi-supervised Learning

Semi-supervised learning is the most commonly used method in which the training involves both humans and machines. You can say that this learning is a combination of both supervised and unsupervised learning methods because in semi-supervised learning, the input data is given by humans and labeling happens here. However, decision-making is done by machines themselves by learning from past experiences.

An example of semi-supervised learning would be Internet content classification. There are millions of web pages on the Internet. It is practically impossible to label all these web pages if you wish to do so. Here, semi-supervised learning can help you as it comes in handy in audio-video analysis.

Reinforcement Learning

Reinforcement learning works on the principle of maximum reward and minimum penalty. When a machine gives the right output, it receives a reward, and it receives a penalty when the output is wrong. The machine makes decisions using predictions here and learns from its previous mistakes. To achieve the best output in unsupervised learning, reinforcement learning is necessary because it helps get the output accurately. This method is mostly used in gaming.

The next topic to discuss in this Python Machine Learning tutorial is the working process of Machine Learning. Read on!

Working Process of Machine Learning

For a better understanding of the working process of Machine Learning, let’s break the process into various steps.

Data Collection

Machine Learning works with data. Humans can do various tasks and recognize anything because of the knowledge we acquire throughout our lifetime. For machines, on the other hand, to learn something, they should be fed with data. So, in this, vast amounts of data should be gathered that is relevant and error-free. There should be zero error while selecting the data as even minor errors in this step can lead to bigger mistakes in the output.

Data Preparation

Data preparation is important to improve output efficiency. After collecting all the data required for a task, it is split into datasets, and these datasets get refined. This refining helps remove duplicate entries, eliminate incorrect readings, and deal with missing values. In this way, the data is sorted in such a way that it will be able to give the right output quickly.

Model Selection

There are different Machine Learning models designed by Data Scientists. These modes have different goals. Some work with text, and some deal with images. The right model, according to the task at hand, has to be chosen for getting the desired result.

Model Training

After model selection, it is time for starting the learning process. The objective here is to use the collected and refined data to train the model and improve the predictions it can provide. Machine Learning has different types as discussed earlier. Labeled sample data is used for training the model in supervised Machine Learning; whereas, non-labeled data is used for unsupervised Machine Learning.

Model Evaluation

Once the model is trained, then it comes to evaluation. Evaluation helps understand how the model works in the real world. You need to check the accuracy of the model against the evaluation data, and the accuracy should reach 90 percent to get the best results when used in real-world scenarios. If the accuracy is less than or equal to 50 percent, then the chances of getting the desired results will be less, and in such a case, the model has to be modified.


The final step in this process is prediction. The model gains the ability of decision-making through predictions. It becomes capable of processing, linking, and learning from large amounts of data and eventually comes up with desired outputs. So, with Machine Learning, humans can skip manual methods of decision-making for better and more consistent results.

Now, let’s see some ML tools in this Python Machine Learning tutorial.

ML Tools

The top five Machine Learning software tools are listed below:

  • Scikit-Learn: A Machine Learning library that supports supervised and unsupervised learning algorithms
  • PyTorch: A Machine Learning library for Python programs that facilitates building Deep Learning projects. Machine Learning using Python is easy with the PyTorch tool.
  • TensorFlow: TensorFlow is an open-source Machine Learning system that explains classification and regression algorithms from start to finish
  • Weka: An open-source software that deals with deep neural networks, including convolutional networks and recurrent networks
  • KNIME: An analytical platform based on a GUI workflow and written in Java, which helps in creating data flows

Check out this blog for gaining in-depth knowledge of Machine Learning algorithms.

Next, let’s discuss some advantages and disadvantages of Machine Learning.

Advantages and Disadvantages of Machine Learning

In this section of the blog, let’s learn about some of the pros and cons of Machine Learning.


  • Machine Learning helps in automation that can boost productivity.
  • It has the capability of making quick decisions.
  • There will be minimal errors with Machine Learning. Humans can make any mistakes, but a machine cannot.
  • Machine Learning can improve itself with experience.
  • It is capable of handling multiple types of data.


  • Machine Learning has some possibilities of making errors. If the training data is not error-free or if the tracing and testing process were not done properly, then it impacts the result.
  • The algorithm selection in Machine Learning is a time-consuming process.
  • Data inconsistency can occur in Machine Learning, affecting the result.
  • More space is required to store the data and to process that, it takes more time and computing power.

Applications of Machine Learning

Now that you have understood a lot about Machine Learning, its working, its types, and its pros and cons, let’s see some real-life applications of Machine Learning in this section.

Applications of Machine Learning
  • Image recognition and speech recognition are some of the applications of Machine Learning. Smart assistants such as Siri, Google Assistants, and Alexa are the best examples of speech recognition. Image recognition techniques are mainly used for face detection.
  • Machine Learning applications are extensively used in the healthcare industry as well. It is helping in medical diagnosis. It also helps in data analysis for hospitals.
  • Prediction is another application of Machine Learning. It is the act of predicting something based on past experience. Machine Learning is used to forecast temperature, traffic, and many other things. For prediction, many Machine Learning models, like the Hidden Markov model, are used. You might have seen commute predictions in GPS services for navigation and traffic prediction. This is also an application of Machine Learning.
  • Almost all social media platforms work based on Machine Learning. You always see platforms like Facebook showing you contacts that you may be familiar with and posts according to your interests or searches. All of this is an application of Machine Learning.

If you are preparing for an interview, check out these Machine Learning Interview Questions prepared by our experts.


This is an overview of the topic “Introduction to Machine Learning with Python”. So, I think you have come to conclusion about how to learn machine learning in python step by step. Machine Learning is embedded in our lives through various technologies. These technologies have expanded to many sectors, which increases the scope of Machine Learning. So, if you want to become a part of the latest technologies and are looking for a Machine Learning Python course, then visit Intellipaat. It provides you with the best Machine Learning Course led by top industry experts.

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About the Author

Senior Research Analyst

As a Senior Research Analyst, Arya Karn brings expertise in crafting compelling technical content in Data Science and Machine Learning. With extensive knowledge in AI/ML, NLP, DBMS, and Generative AI, his works get lakhs of views across social platforms that benefit both technical and business spheres.