How to Build Machine Learning Solutions with Python

 

Implementing Machine Learning with Python

In 2018, developers all around the world claimed that Python is the fastest growing programming language. According to a survey by Jetbrains, 69% of the developers use Python as their main language for machine learning. Due to its less complex behavior and ease of development, Python is rapidly stepping up the ladder in the tech world.

The coding simplicity and readability have led python to become the preferred choice of programmers. With the development of several modules like numpy, scipy & scikit-learn, among countless others, Python programming language has become the go-to language for developers when it comes to machine learning. A software development agency builds high-quality solutions in Python.

Leveraging Python for Machine Learning

In basic terms, machine learning refers to the ability of programs to learn by adapting to the environment. It is a form of Artificial Intelligence that portrays dynamic behavior when the dataset is changed. Due to Python’s easily understandable modules, developers prefer to implement machine learning with this language. The following points analyze how Python can be utilized for machine learning.

• Understanding Python basics Having a basic understanding of python skills is enough to skyrocket your machine learning implementation. Installing a Python implementation like Anaconda can help you with both scientific computing & machine learning. There are several tutorials available that teach python like Google developers python course, for the beginners in machine learning.

• Understanding data Machine learning involves experimenting with data. The developer must understand the dimensions, statistical attributes & breakdown of data according to the environment in which it will operate. Gaining a clear understanding of data sets will allow programmers to code software development solutions effectively.

• Evaluating scientific python packages A wide array of open source modules & python packages are available for programmers to integrate with machine learning. Depending on the operation, they can select the best module from these packages. Scikit-learn (for data mining), Pandas(for data frames), and numpy (for N-dimensional array) are some widely popular packages.

• Executing Python Using the Python application development and its standard machine learning library, the programmer can execute python. They also utilize iPython that gives an interactive environment to execute the language. Experimenting with different machine learning algorithms like k-means clustering and decision trees are a good step to kick-start your python journey.

• Taking Advanced Steps To make machine learning effective, you can dive deeper into the python environment. Giving attention to support vector machines, kaggle titanic competition & dimensionality reduction will augment your foundational machine learning skills. At later stages, learning neural networks can dramatically increase your accomplishments in machine learning solutions.