In the 21st century, most of the apps produced by corporations are somehow designed utilizing Artificial Intelligence, Machine Learning, or Deep Learning that employs Python Machine Learning library.
Usually, AI initiatives are unique from normal projects in the software business.
Variations in development techniques lie in the application framework, the essential skills needed for the AI-based application, and the requirement for in-depth analysis. Let’s look into what are some of the prerequisites For learning Machine Learning.
Also, Checkout our course on libraries that are prerequisites for learning machine learning NumPy, Pandas, Matplotlib in Python for Machine Learning
One of the essential elements involved in building AI-based applications is the usage of an appropriate programming language.
We should utilize a programming language that is efficient in making the applications reliable and extendable.
For this, organizations utilize the Python programming language as it offers a lot of libraries and packages for the development process, and therefore, it is frequently used for working on AI-based projects.
Why Python is considered best for machine learning
Simplicity
One of the first things that hit developers’ thoughts when they hear the phrase Python is simplicity.
The simple to comprehend and understandable code and brief syntax is highly beneficial for Data Science and Machine Learning process.
In Machine Learning, Python’s usage helps to focus on addressing logical issues rather than wasting time on the nitty-gritty of programming language.
This is one of the major reasons developers agree that data science and machine learning using python is the best choice.
Vast library
The well-structured libraries of Python aids in decreasing the time necessary to develop code.
It features a distinct library set specifically for Artificial Intelligence & Machine Learning with a strong technological stack.
The tools such as TensorFlow, Scikit-Learn, and Keras are fantastic for Machine Learning, while Scipy, Seaborn, Pandas for python, and NumPy for python and matplotlib for python are great for data visualizations and many more helpful libraries for making data science and ML operations efficient.
Some of the prerequisites for learning machine learning
Pandas For Python
One of frequently used Python’s Machine Learning package is Pandas. Pandas is the greatest Python package that is primarily used for data manipulation.It leverages convenient and informative data structures such as DataFrames to construct programs for implementing functions. Developed on top of NumPy for python, it is a fast and easier-to-use library.
Pandas for python provides data reading and writing capabilities utilising different sources such as Excel, HDFS, and many more.
Advantages
- It has descriptive, fast, and compliant data structures.
- It enables actions such as grouping, integrating, iterating, re-indexing, and representing data.
- Pandas for python has intrinsic data manipulation features that may be developed using simple instructions.
- Pandas for python may be utilized in a broad range of fields, notably linked to business and education, due to its optimal performance.
NumPy for Python
NumPy is a package for Python. The name is an abbreviation for “Numeric Python” or “Numerical Python”.
NumPy for python is an extension module for Python, primarily written in C.
This makes sure that the precompiled mathematical and numerical functions and capabilities of Numpy ensure high execution performance.
Furthermore, NumPy extends the programming language Python with sophisticated data structures, including multi-dimensional arrays and matrices.
These data structures ensure efficient calculations using matrices and arrays.
The implementation is even targeted at large matrices and arrays, well know under the title of “big data”.
Besides that the numpy for python offers a wide library of high-level mathematical functions to work on these matrices and arrays.
Advantages
- Numpy for python consumes less memory as we need another eight bytes for the reference to the new object in python list.
- It is fast as compared to the python List.
- It is convenient to use.
Matplotlib for Python
Matplotlib is a Python 2D plotting toolkit which creates publication grade figures in a range of hardcopy formats and interactive settings across platforms.
Matplotlib may be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. You can build up all types of charts and visualization with matplotlib for python.
Advantages
- Simple and easy to understand for novices.
- Easier to use for those who have had prior expertise with Matlab or other graph plotting programs.
- It offers high-quality pictures and graphs in different formats such as png, pdf, pgf, etc.
- Provides control to different elements of a figure such as DPI, figure colour, figure size.
- It supports GUI toolkits that include wxPython, Qt, and Tkinter.
- Matplotlib for python is used with a framework that can handle Python as well as IPython shells.
Sklearn in python
The Scikit-Learn or sklearn library is an extension of SciPy and is must as prerequisites For learning Machine learning. It is extensively used for implementing Machine Learning algorithms.
Previously, it was merely a part of a summer project at Google.
Then, it became a frequently used library as it is open-source and also owing to its numerous capabilities that assist create Machine Learning models.
It provides a simple and resilient framework that allows the ML models learn, change, and predict with the assistance of data.
The Scikit-Learn package includes functions that assist construct classification, regression, and clustering models.
Also, it includes a large range of applications for preprocessing, statistical analysis, model assessment, and many more.
Advantages:
- The Scikit-Learn library has a go-to package that consists of all the methods for implementing the standard algorithms of Machine Learning.
- It has a simple and consistent interface that helps fit and transform the model over any dataset.
- It is the most suitable library for creating pipelines that help build a fast prototype.
- t is also the best for the reliable deployment of Machine Learning models.
Conclusion
As a novice, these are the qualifications for machine learning that you have to posses to get started with machine learning.If you put together and build these prerequisites for learning machine learning, the rest should follow as you move to a profession in machine learning.
Checkout our course on libraries that are prerequisites for learning machine learning NumPy, Pandas, Matplotlib in Python for Machine Learning
