Python in Machine Learning
The advancement in technology has grown immensely over the past few years. The impact of modern technologies in our daily lives can show us it’s significance. What once was just a part of sci-fi films or books is now becoming a part of our reality. The modern science and technology are influencing our routines considerably which shows us how much mankind has grown.
One such powerful field is machine learning, which can be considered as a subpart of artificial intelligence. Machine learning is basically a process in which machines or software models are given the ability to learn. Thus they may be able to make an accurate decision or a desired result by analyzing the input data without actually having the need of programming into it. This can create a whole new set of applications and can pave the way future innovations. According to a new report by Research and Markets, the machine learning market is expected to hit $40 billion by 2025. This can also create a number of new job opportunities in this field. Forrester, a marketing research company has pointed out that the percentage of companies using the machine and deep learning concept had a high increase from 2016 to 2017.
Python, a high-level programming language which is mostly known for its simplicity in using and understanding. Python is one of the most leading languages used in Machine learning projects. It was ranked in the top 3 positions of the TIOBE programming index of language popularity of September 2018 issue with a rating of 7.653 %.
The TIOBE programming index shows that the Python ratings have gone up over the years.
Compared to other languages Python has many inbuilt libraries and packages that facilitate programmers in implementing algorithms for machine learning process. We train the machines by giving a set of data which could be properly labelled or not and by processing this input set a prediction is made by the machine. Processing or analyzing the data involves special algorithms. This is the part where Python comes into action.
a comparison between Python and R in machine learning and data science from Jan 2012 – Aug 2017 in Google Trends.
Python consists of many open source libraries to execute ML projects or tasks. The Python packages usually used for ML tasks are:
- Numpy − a Python library commonly used for N-dimensional array objects.
- Pandas − is a data analysis library that consists of different data frames and structures.
- Matplotlib − is a 2D plotting library for producing graphs and plots.
- Scikit-learn − a library that consists of algorithms used for data analysis and data mining tasks.
- Seaborn − a library focusing on visual aspects based on Matplotlib.
Python leads when it comes to job postings in machine learning. (image source:www.ibm.com)
A report by World Economic Forum states that about 54% of the employees in India working in different sectors need to be re-skilled by 2022. And the fact that the investments in AI and ML applications have grown massively reminds us that being skilled in the right kind of software features can only help us in gaining a secure career path in the future. The main reasons for the popularity of Python when it comes to machine learning is its simplicity and the wide range of packages, which makes computations easier for ML tasks. PyBrain is such an example of an easy to use modular library for Machine learning. A 2017 survey by MIT Sloan shows that about 23%t of businesses have accepted different levels of machine learning automation in the US. In 2017 the analyst firm Forrester also stated that about 51% of the organizations they surveyed have already extended the use of AI or were planning to. This includes a 26% of Machine learning platforms, a popular part of AI.
By the look of things, we are fastly moving into a time where technology and machines will be taking over a huge part of the human workforce and execution of several applications and services. As daunting as it sounds, these will be exciting times for mankind to witness the true potential of Artificial intelligence and the vast spectrum of opportunities it can provide.