Introduction to Machine Learning
Machine Learning is a set of techniques and tools that give machines the capability to learn from data and previous experiences by adapting and determining patterns to generate predictions for new processes with little or no human intervention.
Machine Learning algorithm helps AI to predict and execute tasks strictly based on the learned pattern from previous sample inputs and not a predefined program instruction.
Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
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The application of machine learning is very common in our everyday life even without knowing sometimes. An example is email providers being able to implement spam filtering through an algorithm that recognizes and moves these incoming spam emails to your spam folder. Other popular examples include apps and virtual assistants like Google Assistant, Alexa, Google Maps, etc.
Here are some applications of machine learning in our everyday life
1. Language translation:
Machine Learning is responsible for the translation of texts into a known language. Google’s GNMT (Google neural machine translation) is a neural machine learning that provides this feature called automatic translation. The technology is a sequence learning algorithm which uses image recognition and translates the texts from one language to another.
2. Predicting traffic patterns:
Google Maps helps to predict the traffic conditions of a destined location when it is inputted like whether the traffic is heavily congested, cleared or slow-moving. This is done with the help of the historic data of that route collected over time, data from people currently using the service and the real-time location of the vehicle.
3. Image recognition
This is used to identify persons, places, objects, a feature or an object in a digital image, etc. This is the technology adopted for face detection and face recognition in cases like Automatic friend tagging suggestions as seen on Facebook where you upload a photo of yourself and your friend and it gives you a tagging suggestion.
4. Self-driving cars
Tesla is a good example of this machine-learning application. Their Artificial Intelligence is based on Unsupervised Learning Algorithm. The model works on Deep Learning by crowdsourcing data from all of its vehicles and drivers. It gathers information on its cameras and sensors, evaluates it and chooses the action to perform.
5. Product recommendations
Machine Learning is used by different companies for product recommendations to their users. This is the reason why whenever we search for a product online, we start getting random advertisements on that product while surfing the internet or media. Machine Learning makes the browser recognize the user’s interest by tracking their search history.
6. Speech recognition
The ‘search by voice’ feature on Google is an application of machine learning. This comes under speech recognition in which voice instructions are converted into texts. It is also known as ”computer speech recognition” or “speech to text”. Google Assistant, Alexa and Siri also use this technology to follow voice instructions.
7. Online fraud detection
The application of machine learning in online transactions helps to detect fraudulent transactions through a Feed Forward Neural Network that checks the genuineness of the transaction. Whenever a transaction is carried out, the machine learning model scans the profile of the customer for suspicious activity.
8. Virtual personal assistants
Virtual personal assistants like Alexa, Siri, Google Assistant, etc, help us find information using voice or text instructions. Machine Learning algorithm is an important aspect of their function. Some applications used here include speech recognition, natural language processing, speech-to-text conversion and text-to-speech conversation.
9. Email spam filtering
Machine Learning is the technology behind the filtering of emails into your important inbox or your spam box. Some spam filters used by Gmail include header filter, content filter, general blacklists filter, permission filter and rule-based filter. Machine Learning algorithms like Decision tree, multi-layer perceptron and Naïve Bayes classifier are used for spam filtering.
10. Stock market trading
Machine Learning uses algorithm trading to automate or support crucial investment activities. It also supports successful portfolio management and managing when to buy and sell stock. The prediction of stock market trends is also done by machine learning’s long short term memory neural network.