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Machine Learning | Basic terminologies in Machine Learning
Machine Learning
It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. The algorithms of Machine learning focus on applications that learn from experiences and improve their decision-making or prediction accuracy over time.
Examples of machine learning are provided below you might be familiar with:
- Self-driving Car.
- Online recommendation offers such as those from Amazon and Netflix.
- Knowing what customers are saying about you on Twitter.
- Fraud detection in diverse areas.
Who is using Machine Learning?
- Banking: Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data to maximize the output, and for fraud detection.
- Healthcare: The machine learning algorithms are also used by medical experts analyzes data to identify trends or red flags that may lead to improved diagnoses and treatment.
- Oil and gas: Finding new energy sources.
- Government: Government is using the technology in many areas like road safety, traffic purpose, fraud detection, and various other major projects.
- Retail: Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise supply planning, and for customer insights.
Working of Machine Learning:
There are four basic steps for building a machine learning model (or a application).
- Select and prepare a training data set.
- Choose an algorithm to run on the training data set
- Training the algorithm to create the model
- Using and improving the model
Basic terminologies in Machine Learning
- Supervised Learning
Where a program is “trained” on a pre-defined dataset. Based off its training data the program can make accurate decisions when given new data. Example: Using a training set of human tagged positive, negative and neutral tweets to train a sentiment analysis classifier.
There are two main types of supervised learning problems:
Classification: It involves predicting a class label
Regression: It involves predicting a numerical value.
2. Unsupervised Learning
Unsupervised learning describes a class of problems that involves using a model to describe or extract relationships in data. In unsupervised learning, there is no instructor or teacher, and the algorithm must learn to make sense of the data without this guide.
Where a program, given a dataset, can automatically find patterns and relationships in that dataset. Example: Analyzing a dataset of emails and automatically grouping related emails by topic with no prior knowledge or training which is also known as the practice of clustering.
Clustering: Unsupervised learning problem that involves finding groups in data.
Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data
3. Reinforcement Learning
The use of an environment means that there is no fixed training dataset, rather a goal or set of goals that an agent is required to achieve, actions they may perform, and feedback about performance toward the goal.
Some popular examples of reinforcement learning algorithms include Q-learning, temporal-difference learning, and deep reinforcement learning.
4. Semi-supervised learning
It is supervised learning where the training data contains very few labeled examples and many unlabeled examples.
The goal of a semi-supervised learning model is to make effective use of all of the available data, not just the labelled data like in supervised learning.
Many problems from the fields of computer vision (image data), natural language processing (text data), and automatic speech recognition (audio data) fall into this category and cannot be easily addressed using standard supervised learning methods.
5. Self-Supervised Learning
Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it.