Python is a dominant interpreted language and platform that is used for developing production systems in addition to research and development. In this article, I would like to consider a new corner of machine language in Python.
Introduction to Python
Having some basic understanding of Python is necessary to leverage Python to perform machine learning. Due to its widespread popularity as a general-purpose programming language, its adoption in both machine learning and scientific computing across beginner’s tutorials is not terrible. Choosing a starting point, your experience in both programming and Python is very crucial.
Start with installing Python. Installing Anaconda is suggestible as it is the perfect Python implementation for Windows, Linux, and OSX and for implementing industrial-strength. It comes with all the packages of machine learning such as sci-kit-learn, matplotlib, and numpy. Additionally, it also provides an interactive environment for tutorials. Python 2.7 is the best-suggested version as it is the dominating one till date.
Getting Started with Machine Learning
The intelligent behavior of a machine is termed as Machine learning. It deals with system programming to improve the experience in addition to learning. By learning, it recognizes the input data and makes decisions accordingly.
Machine learning precisely refers to the study of developing algorithms that enables decision-making in machines. Specific data is passed through these algorithms to train the machine. After enough training, the machine is used for real-time decision-making.
Machine learning at its simplest level is the sequence of processes for optimizing mathematical equations.
Machine Learning is of different kinds and with distinct functionalities. More forms of machine learning are supervised and unsupervised learning.
Supervised Learning: It uses labeled examples of known data to predict future outcomes. It is necessary to maintain the system with answers that are proven in this type of learning. The machine reads through the provided information iteratively and uses it to make predictions and to form patterns. Applications of supervised learning include predicting in case people will default on their loan payments.
Unsupervised Learning: It is referred to another type of machine learning which doesn’t require answers as in the supervised learning. It is more suitable for clustering work. K-Means and LDA are the popular algorithms where unsupervised learning is applied.
Overview of Scientific Python Packages
Beyond Python, several open source libraries used to facilitate practical machine learning. These are the central scientific Python libraries mostly used to perform simple machine learning tasks. Some of them are as follow:
- Pandas- Used in Python data analysis library, mostly for structures.
- Numpy- Used in N-dimensional array objects.
- Scikit-Learn-Machine learning algorithms used for data mining and data analysis tasks.
- Matplotlib- Used in the 2D plotting library for producing quality publication figures.
Deep Learning in Python
Despite the hype, Deep learning in Python is the application of multi-layered artificial neural networks to machine learning problems. Since the neural networks contain several classification levels instead of a single layer, it is known as deep learning.
For instance, if a deep learning algorithm has to classify the faces in photos, first it should learn to classify the shapes of eyes, noses and then mouths and finally the spatial relationship between all of them together. Instead of recognizing the whole face at once, it breaks down into several parts to get a better understanding.
Deep learning has been a lot lately in the news. Before this, a computer system never had been able to beat a human at a Go game. Therefore, this is marked as a new milestone in artificial intelligence.
DeepDream, the trippy image generation project that Google released in 2015 is the best example of Deep learning.
Machine Learning in Python
The best thing about Python is the fact that several libraries are available. Anyone can create a Python package and can submit it to PyPI (Python Package Index). Like several others, there are numerous packages available for Machine and Deep Learning without any exception.
Python, in fact, is one of the popular languages for data scientists due to the wealth of scientific packages available and its ease of use. Many Python developers like to use Jupyter Notebooks, especially in data space because it allows them to iterate and refine models and codes without running the entire program each time.
For example, Caffe is the best and fastest open framework written in Python for deep learning. An AI research team at UC Berkeley developed this one. Used by several large companies like Microsoft, Facebook, Pinterest, and more as it performs well in image processing scenarios.
Why Machine Learning in Python?
- Python is very to read and learn.
- Being a general-purpose language, Python can be used in a wide variety of scenarios and has a wealth of packages available for any purposes.
- Developers can iteratively create their code using Jupyter and can test it as they run.
The era of Big Data is everywhere and is not going away. In spite of several types of machine learning and significant technologies that companies are using, Python is the perfect source to solve any intensive data problems.