Machine Learning is the technology of the future. It is a part of all the spheres of our life and has a great impact on us. With technology evolving at such an accelerated pace and new data being generated every second, machine learning is a great boon. Machine learning refers to the technology involved in training machines so that they don’t have to be supervised or operated by humans (What is Machine Learning? A definition – Expert System, 2017). It belongs to superset artificial intelligence (AI) and involves developing software or programs that learn from the data provided to them.
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A machine learning project involves the following processes:
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Getting the Data
In this process, the data is loaded into the programming environment. The problem statement is framed, and a performance measure is selected based on the requirement of the system (Tyagi, 2020). Then, the assumptions are verified.
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Visualization and Discovery to Derive Insights
In this process, the data is visualized by experimenting with various combinations of attributes and finding the correlations between the data.
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Pre-processing of the Data
This step involves handling the missing values, categorical values, and textual values. This process involves outlier detection and correction (Tyagi, 2020). Feature Scaling is also performed.
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Selecting and Training a Machine Learning Algorithm
A machine learning algorithm is selected and trained based on the input data. The user has a choice to experiment with different algorithms.
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Fine-Tuning the Model
This step involves hyper-parameter tuning, feature engineering, and the like. Ensemble techniques are used to evaluate the test set of data (Tyagi, 2020). This is done to increase the performance of the model.
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Launch, Monitor, and Maintain the System
This is the final step in the development of an end-to-end machine learning project. It involves deploying the model on a server to implement a machine learning model (Tyagi, 2020). The system is maintained and monitored so that problems can be detected and resolved.Â
Python is the most preferred programming language for the machine learning projects. Python is a widespread choice for machine learning due to the numerous advantages it offers. It is a general-purpose, interpreted programming language that does not have to be compiled. Also, the syntax of Python is extremely easy to learn and use. Some of the benefits that Python has over other languages are:
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Numerous Libraries for Machine Learning
Python has a lot of libraries applicable to machine learning. Libraries in Python provide ready-to-use functions and methods that the developers can directly use and don’t have to code them before implementing them (Luashchuk, 2019). Machine learning involves continuous data processing, and Python has libraries that assist in accessing, handling, and transforming data. Some of the most widely-used Python libraries are:
- Scikit-learn: It has all the basic machine learning algorithms that can directly be imported and used for developing machine learning models.
- Pandas: It supports high-level data structures and can also be used for analysis. It has features for merging and filtering the data and loading it from different sources like excel files, text files, or the web.
- TensorFlow: One of the most common libraries used in deep learning that can be used for setting up and training models.
- Matplotlib: This library is primarily used for the visualization of data through charts, plots, and histograms.
Python can also be used for complete system development as it has frameworks that also support front-end development.
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Flexibility of Python
Python is a very flexible programming language as it offers the user the option to choose either Object Oriented Programming concepts or scripting concepts (Luashchuk, 2019). Since Python need not be compiled, this gives developers an advantage when they quickly want to implement changes and view the results. Developers can conveniently use Python in combination with other programming languages.
The Platform Independence of Python
Python can be used on different platforms like MacOs, Linux, Windows, and Unix, and it is not restricted to any one platform (Luashchuk, 2019). Developers only need some small-scale changes in order to port code from one platform to another.
Readability and Easy Validation
Python provides rapid execution and thus helps machine learning developers make better choices. Since Python has an English language-like syntax, it can be easily read and understood by anyone. Python also possesses a multi-paradigm nature, which adds to its adaptability, and resolves issues in the simplest way possible (Luashchuk, 2019).
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