Businesses have to evolve with time to survive. We have seen many companies going obsolete because they failed to do so. Companies, especially start-up businesses, need to be innovative to stand out among their competitors, and technology plays a critical role in their success.
You might already be familiar with Artificial Intelligence and how businesses are increasingly adopting it for survival. Today, we will be discussing one of its branches, Machine Learning. If you are contemplating using this technology in your start-up, here is everything you need to know about it.
What is Machine Learning?
Machine learning (ML) is a subset of Artificial Intelligence that uses historical data to predict new outcomes. Software applications become more accurate with ML, and search engines use it for pattern detection.
Here are some things you should know about ML before incorporating it into your start-up.
ML is Different from AI
Although Machine Learning (ML) is a part of Artificial Intelligence, they are not the same. Where AI focuses on tasks that require human intelligence, ML solves tasks and makes predictions based on data. In simple words, ML is AI, but not all AI is ML.
It Requires High Quality and Quantity of Data
Machine Learning is sensitive to the quality of data. Its algorithm depends on large amounts of training data to learn and adjust its internal parameters to differentiate similar patterns. Since it requires a large quantity of data, even the slightest error in training data can result in massive errors in the output. Therefore ML requires high-quality datasets to develop models.
It Allows You To Create Simple Models for Less Data
ML requires immense volumes of data to create models. However, it is possible to form simple models if you have less quantity of data. Additionally, renew these models regularly to get the most out of them.
It Detects Only Correlations
ML algorithms only identify correlations and have nothing to do with causation. It is purely mathematical. Hence, it is used for predicting outcomes rather than understanding causality.
Is Machine Learning Suitable for a Start-Up?
While AI and ML are transforming the way businesses operate, it can be difficult for start-ups to use them effectively due to their high cost and lack of technical skills. However, if you can overcome these challenges, ML can help your start-up work faster and smarter. Furthermore, the increasing use of cloud computing has enabled companies to smoothly and affordably tap into ML.
Also, remember that this technology requires a lot of clean data, which is not always the case for start-up businesses.
How to Incorporate ML in Your Business?
Many companies in different industries already use ML. This technology is especially thriving in marketing and advertising. YouTube, Spotify, Netflix, and more, use ML in their search engines. So, if you have also decided to use this technology, you can follow the following steps.
Figure Out Your Main Challenges
This step is essential for creating a machine learning strategy for your business and helps you address your pain points. Many small businesses have a plethora of problems they would like to address with ML, but it is not feasible with your limited budget and resources. Therefore it is better to break down your challenges and handle a simplified version of your most pressing concern.
Understand How ML Works
Once you have figured out the main problem you want your business to deal with, take your time to understand the possibilities of machine learning. It is crucial to understand the available capabilities of this technology before including it in your business. You can easily find many resources with ML’s basic concepts.
Collect Data
The next step is to collect relevant data in line with the problem you defined in the first step. The amount of data will depend on the machine learning algorithm you will use and the complexity of the problem. Furthermore, the quality of your data affects the performance of the algorithm. Therefore, you should include noise and control factors to improve the quality of your data.
Analyze Your Data
After data collection, identify outliers, trends, exceptions, and incorrect or skewed information. Your data should be objective (normally distributed) and free of biases. This step is necessary to avoid feeding inaccurate data to the machine learning algorithm. You can use standard statistical analysis techniques to analyze your data.
Prepare Your Data
Preparing the data is the most crucial and time-consuming step of using ML. It ensures that your data is consistently formatted to suit your model. This step includes:
- Data cleaning
- Data labelling
- Dealing with missing/inconsistent data
- Data normalization
- Segmentation
- Data flattening
- Data imbalancing
Train Your Model To Predict The Future
You select, train and validate a machine learning model in this step. This training is a process where your algorithm determines the correlation within your data. Therefore, the quality and quantity of your data are crucial for this step.
After training your algorithm, you can introduce it to new datasets to generate insights and predictions based on that data. These insights will help you address the problem you highlighted in the first step. Unfortunately, there is no easy way of knowing which model suits your business problem. You have to test various algorithms to check their compatibility. However, you can use performance metrics like low bias and variance to judge them.
Assess the Process
Use your time efficiently. Go through rapid tests to check the compatibility instead of wasting time picking the perfect machine learning algorithm. Select a chunk of your data to test your fully trained algorithm. This data should be new for your algorithm, and you can use it for running multiple algorithms.
Final Words
In short, ML can help you gain an edge over your competitors. However, you’ll need high-speed internet to make the most of this technology. We recommend Spectrum internet packages because they allow you to perform your business operations smoothly while giving you unlimited data and a free Spectrum Security Suite. Spectrum is ranked amongst the leading service providers that are known for providing the most reliable and secure internet connection. They always strive to enhance internet security and that is why it is a crowd favourite amongst business internet users.
Machine learning can be valuable for your business. It can help you solve your business problems by helping you extract meaningful information from a tremendous volume of data, and it can help you make profitable decisions. Additionally, you can use it to predict customer behaviour, perform preventive and corrective maintenance, detect spam and eliminate manual data entry.