Machine learning seems to be more and more prominent as businesses are adopting it. From streaming services that use algorithms to study viewer behavior to self-driving cars, it is clear that machine learning solutions will continue to benefit humanity. And why should that not be the case when there is so much machine learning can offer us.
But are things really as good as they seem? And what is the future looking like for machine learning? Let’s try to answer these questions while looking at the situation from Python’s point of view.
Advantages of Machine Learning
Trend identification and data processing
One of the biggest benefits of machine learning is how it discovers specific patterns and shows behavior that is not noticeable to humans. For example, if an eCommerce store wishes to make more sales, it can use machine learning to track customer actions on the website.
Python coders have a few different options as far as available Python libraries for this operation go. However, Pandas is the go-to choice. While it is not related to machine learning directly, the library is still great because of its high feature variety, ranging from tools to analyze vast amounts of data and predict trends.
Numpy is also worth a shout. This open-source library exists for mathematical operations and is useful for data analysts who seek insights.
Thanks to machine learning, businesses can create a more suitable, almost tailor-made shopping experience instead of guessing what approach to take when trying to convert website visitors.
Automation
Automation is another solid reason why machine learning is so prominent. Artificial intelligence is coded so that it can learn without human intervention. In other words, you do not have to babysit a project constantly. It will develop on its own.
Take this, for instance. Antivirus software and some cleanup utility tools for a computer, some of which you can find more about by clicking here, adapt by recognizing new threats and redundant system junk to eliminate it without you, the user, needing to make adjustments.
Since new cybersecurity threats appear regularly, it is great to have a tool that is smart enough to deal with these threats efficiently.
Now, as for how you can automate machine learning using Python, it again comes down to working with the right libraries. For machine learning automation, there are three standout options:
- Auto-Sklearn
- Tree-based Pipeline Optimization Tool (TPOT)
- Hyperopt-Sklearn
Unlike TPOT, Auto-Sklearn and Hyperopt are open-source Python libraries. Regardless, all three are solid in what they do as far as ML goes.
Constant improvements
Machine learning would unlikely get too far if it was not created in a way to improve constantly. As algorithms improve and develop into more complex equations, they become more accurate and efficient.
Take chatbots in eCommerce, for example. Right now, one of the eCommerce trends is the shift from real people to bots in customer support. A chatbot collects information by receiving and responding to customer queries.
Such a chatbot is available 24/7 (so long as there are no technical issues) and it sends answers as soon as customers submit their questions.
Because of how useful chatbots are, it is no surprise to see a plethora of different chatbot development tools, and many of them support Python. For example, BotMan, Wit.ai, IBM Watson Assistant, and Api.ai.
Flexibility
Whether you are in the automotive industry, e-commerce, health care, or another industry, it is most likely that you can still apply machine learning in some capacity and make it work for you.
As a Python coder, you have access to multiple open-source libraries that provide you creative freedom and flexibility to create new ML implementations or improve existing ones.
Disadvantages of Machine Learning
Data acquisitions
It would be foolish to think that machine learning offers nothing but positive things. To make machine learning work, you need massive data sets, and it is one of the biggest downsides so far.
While you have more than a fair share of Python libraries, you cannot rely entirely on them. Some functions and algorithms are still not solved, meaning that you will need to figure the stuff out yourself or be patient until a library updates, or a new library with answers appears. Only then can you continue with the progress.
Chance for errors
Even though there is plenty of information about machine learning which gets updated regularly, as a concept, ML is still relatively new. Imagine leaving a project unattended for a bit and letting it run without realizing that there has been a mistake in algorithms. There is no telling how difficult it could be to restore the status quo.
While working on machine learning using the Python language, it is important to be systematic and thorough, particularly when someone is not that experienced.
Result interpretation
Despite having the right algorithms and not encountering potential issues with the ML, it is still common to struggle with the result interpretation. Besides, you might have multiple algorithms for the same purpose, and choosing the right one is not that simple. Not to mention trying multiple libraries to confirm the results and getting different numbers.
Resource requirements
Accommodating ML might take more of a toll than you expect as far as the necessary resources go. Some learning requires powerful computers that cost a lot of money. Lackluster hardware does not cut it.
It is also worth mentioning that more complicated machine learning cases need a lot of time, which is another resource you could lack.
Thankfully, when you are working on machine learning with Python, you have access to different open-source libraries that are free, which is a significant advantage.
The Future of Machine Learning
Having said all that, while machine learning has a few drawbacks, the advantages outweigh disadvantages by a lot, and we will continue to see more implementations of ML in different industries.
According to Fortune Business Insights, the machine learning industry worldwide is expected to be worth about 118 billion dollars in 2027. That would be about 14 times more compared to 2019.
Industries like healthcare, pharmaceuticals, eCommerce, video games, and car manufacturers will continue to be the leaders and push ML further.
Simultaneously, you have other, less prominent industries in education, computer security, forecasting, and speech recognition. These might not rake in as much money, but they are still a big part of the machine learning concept as a whole.
If you are an aspiring Python coder with an interest in machine learning or have already established yourself as one, you should feel pretty confident about the future.
Here are some useful tutorials that you can read:
