Without a doubt, Machine Learning is one of the most indispensable branches of Artificial Intelligence which will definitely play an important role in everyone’s life. Machine Learning Applications range from Scientific works such as dealing and analyzing big data in Chemistry, Biology, etc, and also predicting future events in commercial activities such as tracking the behavior of customers and absorbing new ones.
- Supervised Learning: In Supervised Learning, the machine tries to learn a specific function which is a mapping from input data to labeled ones. Imagine that an e-commerce or shopping mall has a bunch of data that comes from the history of customer purchases and wants to track the behavior of its customers and classify them for example as Individual or Non-Institutional Buyers and Institutional ones. Common algorithms in this type of learning are Nearest Neighbor, Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines (SVM), and Linear Regression
- Unsupervised Learning: In Unsupervised Learning, the machine learns how to cluster a bunch of data in some clusters based on the similarities in the structure of data nature. This type of machine learning is mostly used for pattern detection and descriptive modeling. One of the common algorithms in this type of learning is K-means clustering.
- Semi-supervised Learning: Semi-supervised learning is a learning method that falls between Supervised and Unsupervised learning. in Supervised learning sometimes it’s difficult or costly to label training data to learn the algorithm, Semi-supervised learning comes to solve this problem by building a model from the environment.
- Reinforcement Learning: In this type, the agent observes the environment and after gathering information from the environment and after several trials and error interaction agent will build an optimizing policy which tells to the agent which legal action in which state will gain as more rewards as possible in a long-term activity. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed to decide the best action to select based on his current state. When this step is repeated, the problem is known as a Markov Decision Process. RLs common algorithms are Q-Learning, Temporal difference (TD), and Deep Adversarial Networks. Also, reinforcement learning can be shown like below picture which illustrates us the interaction between the agent and the environment:
1- Image Recognition
Image Recognition is one of the most common applications of machine learning. it can be used to identify objects, places, and people. Automatic friend tagging suggestion is one of the famous use of Image Recognition. Facebook provides this feature in its project. whenever we upload a photo with our Facebook friends this feature suggests the name of the friend using pattern recognition. This feature is based on the Facebook project named “Deep Face“ which is responsible for fore face recognition and person identification in the picture.
2- Speech recognition
While using Google, we get an option of “Search by voice”, which comes from speech recognition. Simply, it can be defined as changing the structure of voice to text which is also known as “Speech to text” or “Computer speech recognition“. Speech recognition is used by various applications such as Google assistance, Cortana, Siri, and Alexa.
3- Traffic Prediction
If we want to visit a new place, we possibly use some application which shows us the traffic congestion, slow-moving, whit the help of two ways:
- Real-time location
- Average time has taken
4- Product Recommendation
In this popular usage of machine learning various e-commerce and entertainment companies such as Amazon, Netflix, etc., use Product recommendations to recommend products and entertainment activities based on customers’ previous activities and other factors.
5- Self-driving cars
From past to present, people dreamed about cars and driving cars without a driver. Self-driving cars can greatly help us to achieve the dream of driverless cars. Almost the most reputable car companies such as Tesla, BMW, and Benz, etc are using this application of machine learning to provide driverless cars. In this application, the car acts as an agent and learns from the rewards and punishments of the environment.
6- Email spam filtering
Sometimes we might receive emails that are spam and harmful to our personal data. By using Email spam filtering spam or junk emails can be divided from nonspam ones. Below are some spam filters by Gmail:
- Content filter
- Header filter
- Rules-based filter
7- Medical Diagnosis
In medical science, machine learning is used for disease diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain. It helps in finding brain tumors and other brain-related diseases easily.
8- Automatic language Translation
One of the machine learning applications is translating from one language to another naturally. Algorithms in machine learning train lots of different equivalents in languages to act as a natural translator. The technology behind the automatic translation is a sequence to the sequence learning algorithm, which is used with image recognition and translates the text from one language to another language.
Machine Learning life cycle
Machine learning has given computer systems the abilities to automatically learn without being explicitly programmed.
Machine learning life cycle involves seven major steps, which are given below:
- Gathering Data
- Data preparation
- Data Wrangling
- Analyze Data
- Train the model
- Test the model
every step of the machine learning life cycle will be explained below:
1- Data Gathering: data gathering is the first step of the machine learning life cycle. There are various resources that data come from such as mobile devices, the internet of things (IoT), the internet, and so on. In data gathering, there are three main steps, such as identify various data sources, Collect data, and Integrate data obtained from various sources.
2- data preparation: in this step after collecting data, all gathered data should be cleaned and be randomized to be used in the next step. this step involves two steps, Data exploration, and Data pre-processing.
3- Data Wrangling: in this step, some data have to be deleted such as Duplicate data, Invalid data, Noise, and Missing values.
4- Data Analysis: the aim of this step is to build a machine learning model to analyze the data using various analytical techniques and review the outcome.
5- Train Model: now it turns to the train the prepared model. We use datasets to train the model in order to classify real data in the test phase.
6- Test Model: in this phase, the trained model will be appraised by real and unlabeled data.
7- Deployment: the last step of the machine learning life cycle is deployment, where we put the trained model in the real-world system.
To sum up, due to loads of applications of machine learning in various areas such as Medical, e-commerce, Chemistry, Computer science, Electrical engineering, and so on and because of the fact that this science is the intersection of various sciences such as phycology, Mathematics and etc, become popular among people and are used to solve the problems that most people cannot solve them. There is another concept that really helps the reputation of ML and that is Big data. Machine learning can act and solve problems just by feeding by Big data and everything that produces data.