Machine learning - When to use it and when not

What is Machine Learning? Machine learning is a field of computer science that allows computers to learn without being explicitly programmed. In machine learning, computers are able to learn from data, without being specifically programmed for each task. This learning process can be considered as a type of artificial intelligence, where the computer takes actions based on what it has learned.In more technical terms, a machine learning system is any program or algorithm that gains knowledge (perception) or improves its performance (reasoning) with experience and data. Machine deep learning algorithms can be used in applications such as search engines, fraud detection systems, or in the self-driving car.Machine learning is a technology that has developed strongly in 2019 and continues to maintain its popularity, affecting many sectors of various industries.

Difference between Artificial Intelligence and Machine learning model

Artificial intelligence and machine learning are two distinct terms, but are frequently used interchangeably. Machine learning is a subset of artificial intelligence that focuses on making predictions from data. AI involves the use of software to make intelligent machines, enabling them to think for themselves in a way similar to human cognition. Some argue that AI is really a branch of machine learning, which is concerned with data analysis rather than just doing calculations or operations. Machine learning systems include such techniques as artificial neural networks and support vector machines.

Deep learning models

Deep learning is a type of machine learning that is based on artificial neural networks. An artificial neural network is a set of algorithms that are modeled after the way biological neurons in the brain work. It’s made up of layers, and each layer has weights, which are variables that can be adjusted over time. Neural networks have been around since the 1980s, but it wasn’t until recently that they became widely used in data science because of their ability to recognize patterns and make predictions.

Machine Learning algorithms categories 

Machine learning has three main models developed: supervised learning, unsupervised learning and reinforcement learning.

  • Supervised Learning – in this type of machine learning, you have labeled data to train your deep learning model on. This is useful for things like training an image classifier to identify images as cats or dogs (or something else), or for training a natural language processing system to identify different types of sentiment in tweets. If you’re working with labeled data and trying to predict values from it (like probability distributions), then you’ll be using supervised learning. Supervised machine learning focuses on predicting the likelihood of a customer buying a product or service given their recent behavior (e.g., how many times they visited the website, how much money was spent).
  • Unsupervised machine learning algorithms include clustering and anomaly detection. Clustering is the process of grouping labeled data into clusters. Clusters are groups of similar elements in hidden patterns —in other words, they’re like sets that contain a lot of items with similar characteristics. The most common example would be grouping all your friends into their respective high schools so you can easily find them on Facebook.
  • Anomaly detection is another technique that identifies data points that are different from the rest of the dataset. It’s useful for detecting fraud and hidden problems in data (like when there’s an error in your credit card statement).
  • Reinforcement learning is a type of machine learning algorithm that learns by trial and error. The algorithms are given a reward signal and learn how to maximize it. It’s not always clear what the right thing to do is in reinforcement learning, so you need some way of measuring whether you’re doing well or badly. Reinforcement learning algorithms generally use an experience replay buffer to keep track of the previous experiences in the game (or other task), which can then be used for training the next generation of policies. The most common application of reinforcement learning is teaching computers how to play video games or perform tasks like driving cars autonomously. But it’s also used for robotics, computer vision, natural language processing, machine translation, music composition…the list goes on!

When to use Machine Learning?

You should use machine learning if you have a lot of data, want to reduce the number of parameters you need to specify, want to make a decision without having to program it, or want to make a decision based on experience and historical data. Machine learning is a powerful tool that can help you find patterns in large amounts of data. It can also be used to identify patterns in data that humans cannot see. However, machine learning is not a magic bullet: the process has its drawbacks and requires a lot of effort to build and maintain.Here are some guidelines for when machine learning algorithms might be a good fit for your application, use machine learning when:

  • You have large amounts of data that can’t be easily organized into a traditional database.
  • You want to predict or categorize new data.
  • You want to optimize an existing process or workflow.
  • You want to automate business processes that would otherwise require many human tasks

Machine learning can help you with actions that require real-time decisions like predicting the weather; identifying potential customers or collaborators; determining whether or not a piece of content is spam (and removing it from your site).

Why traditional programming is sometimes better?

Machine learning is different from other forms of AI because it can solve problems that are too complex for humans to understand or solve through brute force methods (like using trial and error). Machine deep learning algorithms are designed to find solutions that are usually more accurate than those found by human beings. There are certain situations where Machine Learning doesn’t work well and traditional software methods would be a better fit: 

  • When there’s no data available: Machine learning requires training data—examples of how you want something done or how you want something recognized (such as recognizing faces).
  • When there’s too much “noise”: Noise in Machine learning is anything that interferes with the intended process of the system, including interference from other processes operating at the same time.
  • When you have a simple problem to solve.

Machine learning for your business

Machine learning is constantly more and more popular. Machine learning models are used to achieve different goals and answer different business needs. From customer service analytics to fraud prevention and even navigation in autonomous vehicles, this technology has become prominent in our daily lives. Sometimes, however, it’s traditional app development that can be behind the success of your business. If you’re still wondering what technology will be best for your app, contact us. Our experienced mobile app developers with in-house expertise will help you with any project, whether created with trending deep learning technology or other most popular application development technologies. 


Please enter your comment!
Please enter your name here