Machine Learning with Python

Machine Learning jobs is the new career desire for most of us in the technological domain. It should be of no surprise as the demand has been manifold in recent times. Job portals are filled with vacancies for job roles like Machine learning engineer, Data scientist, Analyst, Data modeler, and others from renowned companies like Amazon, Flipkart, Reliance, etc. Also, the average salary in the field is more as compared to other technologies. As per a recent report, Machine Learning jobs have jumped by 75% in the past years and will continue to grow. Most importantly it will impact each sector and industry! The advantage of Machine learning technology can be best leveraged with Python. Most engineers across the world prefer using the Python programming language for it. Let us explore further its importance in the article.

Introduction to Machine Learning

We are living in an era where data is the new fuel. This data can be enhanced and leveraged with the help of computational power and also increased storage resources. The amount of data we receive on a daily basis is increasing, but the challenge is to make sense of this data. Therefore organizations are trying to use the data to aim for efficiency and profitability using methodologies and concepts. Machine Learning refers to the science and application of algorithms that provide outcomes to the data.

In simple words, Machine Learning is making machines learn, think, and also act similarly to humans. It refers to the process of helping the machines learn from previous experiences and improve the quality of outputs with time.

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Why Machine Learning?

Before machine learning, humans work harder. But it was difficult for humans to remember the data and make sound decisions. Even after working hard the output was unsatisfactory. Humans come up with technology every day and Machine Learning is one among them. In a nutshell, humans can make machines learn to make their own decision that is based on past experiences and data with accuracy and efficiency. Hence Machine Learning has now become an important part of life.

Why Python for Machine Learning?

Python has recently become the backbone of the technology of Machine Learning. With the increasing trends engineers across the world use Python as a preferred language. Let us understand in detail why we should use python over other languages.

1. Python is easy to understand: Python is considered the best among other programming languages, especially for beginners. Its syntax is very similar to that of the English language and therefore it can be read and understood easily.

2. Python allows easy and powerful implementation: The large and massive library of python helps in the implementation of numerous programs and algorithms easily.

3. Community: It is open-source software. Python has a large community of developers that run local, national and international events. The millions of developers from the community can interact either online or offline, virtual as well as at physical locations.

4. Python comes with a large number of libraries: The library of Python consists of around 200+ modules. It makes programming in Python simple and also convenient, as we don't have to rewrite the same code every time for the various other programs. Some of the famous libraries are Tensor flow, Pytorch, Scrapy, and others.

Working Process of Machine Learning

To understand the process of Machine Learning, let's understand it steps by step.

1. Data Collection: The fundamental working of Machine Learning depends on the data. Therefore large amounts of data need to be gathered. This data should not only be relevant but also error-free. A minor error in the data can hamper the quality of the output.

2. Data preparation: One of the important factors to improve the quality of output is Data preparation. When all the data is collected that is required for the task, it is further split into various datasets for refining purposes. This process helps to deal with missing values, remove duplicates, etc. This helps to sort the data to get the desired output.

3. Selection of Model: Data scientists have designed numerous models for Machine Learning. Each model has its own goal. A few work with text and some with images. Therefore a user will have to select the right model considering the task that needs to be done so that the desired results can be obtained.

4. Training of the model: After the model has been selected, the user needs to start the learning process. The main objective is to use the refined data which is collected in training the model and improving the outcome that it provides. As discussed earlier Machine Learning has different types i.e. labeled sample data is used in supervised learning and unlabeled data in the unsupervised learning method.

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5. Evaluate the model: After the model is trained, it needs to be evaluated. The user needs to understand how the model will work in reality. The user will have to check the accuracy against the evaluated data, and the result should reach 90% to get quality results if used in scenarios of the real world. In case the accuracy is equal to or less than 50% the chances of getting quality results will be less. In such cases, the model needs to be modified.

6. Prediction: The last step of the process is prediction. The ability to make decisions with the help of predictions is gained by the model. It is now capable of linking, learning, and processing vast amounts of data and comes with the required output. Hence with the help of Machine Learning humans can make better decisions and have more consistent results by skipping the manual methods.

Python Libraries for Machine Learning

To make projects in Machine learning with the help of Python language you need to know Python and its widely used libraries. These are as follows:

1. Pandas: Pandas are used for data analysis. Before training the machines we need to prepare datasets. To extract and prepare the data sets, Pandas are very useful. It supports speedy. scalable data structures for data analysis. It includes numerous types of data such as arbitrary mix data, tabular data, unordered data, organized data, and other types of observational or statistical datasets.

2. NumPy: In Machine Learning, It is used primarily for numerical computation like Fourier transforms, random number functions, and linear algebra. It allows users to find arbitrary data types. This will help to integrate with many databases easily. Even for generic data, Numpy can also be utilized as multidimensional arrays, irrespective of the type of data. Broadcasting functions, N-dimensional array objects, and tools that are out of the box to integrate Fortran, and C/C++ are a few features of NumPy.

3. SciPy: It contains various modules for statistics, integration, optimization, and linear algebra. SciPy is used for scientific computations and also image manipulation. As an underlying data structure, it uses the multidimensional array which is given by the NumPy. The array manipulation procedure in SciPy is conducted via NumPy. It is ideally a python library, designed for working with the arrays of NumPy, and also provides numerical functions that are powerful and user-friendly.

4. Matplotlib: Matplotlib has a similar user interface as MATLAB and is very easy and simple to use. It is used in data to visualize patterns. It provides users with charts, graphs, and plots for visualizing the data. It works by facilitating the programmers with an object-oriented application programming interface (API) for integrating the plots and graphs into the applications by using the GUI toolkits that are standard like Qt, wxPython, GTK, or Tkinter.

5. OpenCV: The primary objective of OpenCV is solving problems related to computer vision. It is leveraged to sort images and also videos to more advanced techniques of robotic vision. On combining the open CV with other libraries such as NumPy it results in the library for all the numerical operations along with the syntax like MATLAB style which is highly optimized, the arm’s number in the arsenal increases because all the operations that a NumPy will perform and are combined with OpenCV.

Applications of Machine Learning

Since you have now understood the concept of Machine Learning. Let us understand its application in a real-world scenario.

  • Image & Speech recognition are the most recognized applications of the technology. Smart assistants like Alexa, Google Assistants, and Siri are a few well-known examples of speech recognition. The technique of face recognition is majorly used to detect the face.
  • It is also used in the healthcare industry. It helps in the analysis of the data for hospitals and also medical diagnoses.
  • Machine Learning is also used to predict traffic, temperature, and others. You might have come across prediction during commutes in GPS services used for navigation and also traffic prediction.
  • Many social media platforms are based on Machine Learning i.e. for instance Facebook shows you connect with those you are familiar with and also the content is shown as per the searches and interest.

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Types of Machine Learning

The classification of different types of machine learning is based on the way it is utilized to train the machine. From the below image, you can make out that unsupervised learning occupies the majority of the space as it is the model which is used most for Machine learning.

Types of Machine Learning

Supervised Learning: As the name implies, a supervisor is always involved and assists the machine in getting trained. The supervisor provides inputs as well as outputs to machines to learn, predict and also understand patterns. It is further classified into two parts i.e. Regression and Classification. Both are based on predicted values, the former is based on discrete values, and the latter continuous values.

Unsupervised Learning: Unlike supervised learning, there is no human intervention here. In unsupervised learning, machines train themselves automatically without the involvement of humans. There won't be labeled datasets. The output also will not be known. Machines learn via experience only. The machines make decisions based on the trial and error method. It learns from the mistakes done in the past and ensures not to repeat in the future. It is also classified into two parts i.e. Clustering and Association. Clustering refers to the method of grouping objects into similar clusters. Whereas, Association refers to the finding of the probability of the item in a collection based on the occurrence.

Semi-supervised Learning: Semi-supervised learning refers to the combination of both supervised learning and the unsupervised learning method because the input data is provided by humans and also labeling is done here. However, the decisions are taken by the machines themselves with the help of learning from past experiences.

Reinforcement Learning: Reinforcement learning works on a fundamental principle of the minimum penalty and maximum reward. If a machine gives the required output it is rewarded and vice versa. Predictions are being used by machines to make decisions and it also learns from past mistakes. In unsupervised learning to get the best output, reinforcement learning is important as it helps with an accurate output.

Machine Learning tools

The following is the list of the top tools of Machine Learning

  1. Weka: It is software that helps users with deep neural networks. This includes networks like convolutional and recurrent.
  2. Pytorch: It refers to a library for Machine Learning for the Python programs that help build projects in deep learning. It makes Machine Learning with Python easy for the users.
  3. TensorFlow: It is a Machine Learning system that assists users with the explanation of classification and also regression algorithms right from start to end.
  4. Scikit-Learn: It is a Machine Learning Library and primarily supports both unsupervised and supervised learning algorithms.
  5. KNIME: It is a platform used for analytics. It is written in the java language and is based on the GUI workflow. It helps users to create data flows.

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Why is Python desired by many ML Engineers?

As per Stack Overflow, Python is the most preferred language for programming by engineers across the globe and its usage will increase tremendously. As an ML engineer, one is responsible for data refining, data extraction, and also understanding the data so that it can be implemented in different algorithms. Hence, the need for a programming language that can be understood and implement all the ML algorithms quickly. Python also helps engineers to validate the algorithms instantly. Therefore ML engineers prefer Python. It also offers a few other advantages as listed below.

  1. Python has numerous visualization options
  2. It is very popular across geographies
  3. It has a vast library system
  4. The barrier to entry is low
  5. It is versatile and flexible

Advantages and Disadvantages of Machine learning

The following are the advantages of Machine learning

  • Automation is possible with the help of machine learning to boost productivity.
  • It helps organizations make quick and sound decisions.
  • The number of errors has reduced to a few numbers with the use of machine learning as compared to human errors.
  • It can handle huge volumes of data along with various types.

The following are the disadvantages of Machine Learning

  • If the quality of data is not good, inconsistency in data will affect the accuracy of the results.
  • A lot of space is required for storing and processing the data and it will consume more time and also the computing power.
  • The selection of algorithms in Machine Learning is a tedious and cumbersome process.
  • If the testing process is done correctly and the trained data is accurate then the quality of results will be maintained.


Hope you have now understood the concept of Machine Learning. Since Python can be easily used in analyzing and composing data, it is one of the most preferred languages of engineers. The massive libraries of Python make the work easier for the developers. Even though the technology has been around for some time it is still not mature which means it has tremendous growth opportunities. If you are looking to build your career in Machine Learning, then visit HKR Trainings. Your one-stop destination for career growth and development!

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As a content writer at HKR trainings, I deliver content on various technologies. I hold my graduation degree in Information technology. I am passionate about helping people understand technology-related content through my easily digestible content. My writings include Data Science, Machine Learning, Artificial Intelligence, Python, Salesforce, Servicenow and etc.

The programming language of Python attracts a vast community consisting of developers which makes it a preferred language not only for machine learning but also for other projects like web development, regression, etc.

The syntax of Python is similar to the English language. Therefore making it one of the easy languages to implement in machine learning.

In machine learning with Python, you will learn the use of python libraries like Tensorflow, SciPy, NumPy, Versatile workflows, Pandas, Keras, complex algorithms, and others.

Python is widely used along with being the primary choice for many users. Thus the best programing language for machine learning is Python.

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