Artificial intelligence has been finding its way into the everyday tech we use for quite some time now. If you’ve ever wondered how the product recommendations by Amazon are exceptionally relevant to you, the answer is AI. Artificial intelligence systems work like magic, but to create a good AI system, you must have relevant and huge data sets at your disposal. The machine learning algorithm should be fed with data, and the more data you give it, the better it gets at its job. Let’s look at how machine learning works at a glance.
Components of a Machine Learning system
Every machine learning system has three major components:
Model: The component that takes care of identifications and predictions.
Parameters: The factors or signals that are used to form decisions.
Learner: A system that makes changes to the parameters which in turn results in modification of the model, by taking cues from differences in predictions and outcomes.
Let’s take a real-world example to understand the concept better. Consider that you’re a teacher who’s trying to identify the optimal amount of time students should spend in studying to get the top grade in an exam. Let’s look at how this can be solved by taking some help from machine learning.
Building the model
As we discussed, it all starts with the model. Initially, the human being building the ML system must give it a model to start with. In our case, the teacher can assume that studying for five hours should give the best test score.
The model will further depend on the provided parameters to make calculations and adjust itself. Here, the parameters would be the test scores received and the hours spent studying. Something like this:
0 hours = 50% score
1 hour = 60% score
2 hours = 70% score
3 hours = 80% score
4 hours = 90% score
5 hours = 100% score
The ML system will express the above in a mathematical equation to develop a trend line of the expected outcome.
Learning from the conflict
Now that we have the initial model, it’s time to input the parameters. You have to feed the model with the data, which would be the ‘test scores and hours studied’ for different students. As expected, the input scores won’t match exactly with the manually programmed model. The actual results would be higher or lower than the predicted trend line.
This situation of conflict is what triggers the learning activity in a machine learning system.
The learning process
The data that was fed to the machine learning system is what we call a ‘training data set’ and is used by the learner component in a machine learning system to train and optimize the model in order to make it better.
In our case, the learner would compare the input scores and check how far off they are from the initial model. The learner then uses complex math to modify the model to bring it more in line with the actual data. The model might get altered to something like this:
0 hours = 45% score
1 hour = 55% score
2 hours = 65% score
3 hours = 75% score
4 hours = 85% score
5 hours = 95% score
6 hours = 100% score
The prediction has been altered and it shows that 6 hours of study is required to hit that best score on this test. This way, the learner keeps making small and relevant changes to the model as it gets hold of more data. As the process is repeated for a certain number of times, the prediction achieves a fairly good confidence score and this means the ML system has been successful. The accuracy of its prediction is largely influenced by the amount of data it receives. This was a simple example and real world use cases might be a lot more complex.
Applications of ML in major industries
Machine learning can be applied to almost all industry verticals to bring about radical changes and growth. Let’s look at some of the popular applications from the major domains.
Create customer-centric search: Wouldn’t it be great if the e-commerce search engines could think exactly like humans? One of the common issues with e-commerce search is the users abandoning an ecommerce portal because the product results returned by the site for a particular search weren’t relevant. This issue can be solved by leveraging natural language processing to contextualize and narrow down the meaning of a search query, thus improving the e-commerce search experience.
Retarget potential customers: Retargeting is a great way to bring back customers who abandoned the cart without checking out or visited a certain product page several times, without making a move. By intelligently identifying the intent of an ecommerce shopper, you can send them an offer that they simply cannot reject. This is a great way to multiply your conversion rates without much effort.
Identify exceptional target prospects: Identifying your high potential prospects is key to generating more revenue. With the use of machine learning to analyze the buying patterns of your customers, you can easily spot the exceptional prospects and target them with improved precision, thereby improving your lead generation.
Improve recommendations for customers: Recommendation engines are built to record the buying patterns of customers so as to recommend products that they are likely going to need next. A simple example would be the suggestion of a phone case to someone who just purchased a new smartphone. The relevance of the recommendations would be extremely high considering there is already a goldmine of historical data available on the buying patterns of customers.
Tackle fake reviews: Customer reviews, both positive and negative will impact the buying decisions of ecommerce shoppers. Brands have been known to engage in propagating negative reviews to bring down their competitors. Many ecommerce retailers have started using artificial intelligence to fight fake reviews by emphasizing the verified and helpful reviews.
Attracting Talent: Identifying and attracting relevant talent with the help of AI has seen an upswing in the recent years. Linkedin, for example uses machine learning to recommend jobs by matching them with the candidate’s’ skills and qualifications. Other popular job sites like Glassdoor, Seek and Indeed also use similar machine learning algorithms to create interaction maps from user’s’ previous searches, posts, clicks and connections.You can learn more about job matching and how it works here.
Attrition Detection: Understanding employees and why they decide to leave or stay in a company is one of the primary questions in HR analytics. Identifying the risk of attrition demands advanced pattern recognition and an array of variables that should be custom-set for the company in question. With the help of machine learning, the seemingly far dots can be connected in seconds, freeing the time of HR representatives to focus on minimizing the risk than identifying it.
Applicant tracking and assessment: In the companies that receive high volumes of applicants, tracking and assessment is a heavy workload that can only be minimized by the use of machine learning. While the quest for the best talent is on the rise, many HR representatives have started using algorithmic-based assessments to make the task faster and significantly efficient.
Dynamic pricing and fare forecasting: Hotel prices and flight fares are changing at the blink of an eye and it can also vary greatly depending on the service provider. These changes cannot be tracked manually. Hence, web crawling APIs are leveraged to monitor the pricing changes and this data is used to predict future fares and for fine-tuning the pricing strategy. With historical pricing data at your disposal, you can create a machine learning algorithm capable of predicting future price changes. The input parameters could include seasonal trends, special offers, demand growth and the active competitors.
Intelligent travel assistants: Since convenience is the king in today’s fast-paced world, smart services powered by AI are gaining popularity in many industries. Travel booking is one such area where automation powered by algorithms can be of great help. Intelligent bots can be trained to listen to your travel plan and make the booking for you. AI-powered virtual assistants and chatbots are even integrated into popular IM apps such as Facebook messenger, Telegram, Skype and Slack. With this, users can do a host of things like finding the cheapest deals, making hotel reservations and booking flights. Such intelligent assistants can also give valuable suggestions to the users about popular destinations, dining places, tourist attractions and more.
Training data for machine learning
Now that the concept of machine learning is clearer to you, it’s time to apply it in your business and reap innumerable benefits. In all the innovative applications of AI, one thing that stays common is the training data. You would require a constant supply of data to train your machine learning system as it is the most vital component of a machine learning system by all means.