Learning deals with acquiring skills or knowledge which is gained from experience. When machines learn in a way which help to train an algorithm so that it can learn and improve by itself, it is referred to as AI Learning or Machine Learning in AI. Machine learning may involve little or no human intervention.
AI learning is a very important aspect of artificial intelligence as it involves algorithms that allows the machines to learn automatically from their experience without the need of human (programmer) intervention.
Components of Machine Learning in AI
There are three main building blocks of machine learning. These are as follows:
- Representation of data – First and foremost we need to identify the type of data we have to deal with. It isthis component which selects the model in which data can be represented. These include decision trees, constraints, instances etc.
- Evaluation – This component helps a machine to evaluate the situationbased on an evaluation criterion and create a hypothesis based on which decisions can be taken.
- Optimizationand OutputUnit– The way by which hypothesis are generated is known as optimization.It is here that the machine learning system would interact with the outside worldwith the help of the output unit and take actions accordingly.
What is Generalisation?
Before moving to details of machine learning we must know what generalisation actually is in AI.
Imagine that you are playing scramble with computer. In the beginning you might win every time. But after many games as the computer starts to learn how to win at scramble you might not be able to win against him. If the computer applies this strategy against other players as well in order to win, this is referred to as generalisation.
Types of Machine Learning
There are various ways in which machine learning are classified. Based on the nature of learning, the various types of machine learning are as follows:
In supervised learning machines are generally provided with training data and target data. Training data is the input data in the form of examples. Target data refers to the correct answers or labels for these training data which the machine will provide as outputs. The training data and the target data are provided to the machine as an input.
Now, when possible, inputs are put into the machine, the machine uses generalization and responds according to the target data.
Supervised learning is usually used in real world applications. It is used in applications such as face recognition, handwriting recognition and speech recognition, movie recommendations, sales forecasting and even product recommendations.
Supervised learning is further classified into two types. These are:
- Regression – Here the machine trains and predicts the value of response continuously. An example of regression is predicting real estate prices.
- Classification –Here the machine tries to classify the output based on the inputs given to it. For instance, classifying data into males or females, secure and secure loans, positive or negative statements etc.
The algorithms used in supervised learning are known as supervised learning algorithms. These algorithms are used to analyse training data and provide the desired function for mapping the data with outputs.
Logistic regression, neural networks, NaiveBayes classifiers, and support vector machines are examples of supervised learning algorithms.
In unsupervised learning,Target data is not provided to the machine.Instead, the machine tries to find the similarities among the training data and categorise the output according to the similarities found. This approach to unsupervised Learning is referred to as Density Estimation.
Unsupervised learning algorithms must be powerful so that they are able to analyse data and identify patterns and trends.
Some of the unsupervised learning algorithms include k-means, random forests, and hierarchical clustering.
Semi Supervised Learning
Semi supervised learning is a mixture of supervised and unsupervised learning. This learning makes use of a large amount of unlabelled data for trainingpurpose and a small amount of labelled data for testing purposes.
In this the machine uses daughter from previous experiences to evolve and learn. The algorithm is told if the answer is correct or wrong. if the answer is wrong, the machine is not told about the correct answer but it needs to evolve the correct answerby working out and trying different possibilities.
Reinforcement learning is also referred to as learning with a critic as the correct answer needs to be worked out by the machine itselfas it is not told how to correctits answer. Hence the answer given by the machineis simply scored and improvements are not suggested.
Applications of Machine Learning
Machine learning is used in order to solve many complex problems of the real world. Following are some of the applications of machine learning in the real world:
- Speech Recognition
- Object Recognition
- Detection and Preventionof Errors
- Detection and Prevention ofFrauds
- Weather Forecasting and Predictions
- Stock Market Analysis
- Handwriting Recognition
- Recommendation of products in Online Shopping
- Sentiment Analysis
- Customer Segmentation and many more.
Advantages of Machine Learning
Machine learning Proves to be of an advantage in various aspects. some of them are as follows:
- It helps us analyse a large amount of data which is otherwise not possible.
- It supports complete automation. This is possible as machine learning requires zero human intervention.
- It takes decisions based on various trends and patterns with ease.
- It has the ability to handle a variety of data easily.
- It is much more efficient than the traditional data analytical methods.
- Machine learning is much more reliable and efficient.
- Once a machine is well learned it can accommodate in various forms of applications.
Challenges in Machine Learning
Machine Learning is rapidly evolving but still it has a long way to go. The reason behind this is a number of challenges which machine learning has to face. These challenges are as follows:
- Lack of quality of data – maintaining good quality data with the use of machine learning algorithms is a big challenge. This is because low quality data needs to be pre processed and takes more time in extraction. Noisy data also results in inaccurate predictions.
- Time consuming task – machine learning models consume a lot of time for data acquisition, extraction of features and hence retrieval of results.
- Difficulty in deployment – due to increasing complexity in machine learning models it becomes quite difficult to deploy it in real life.
- Need of well defined goals–for a machine to learn properly objectives and goals for the business problems need to be well defined.
- Lackofexperts – when machine learning was is in its infant state trained experts are required for defining the machine learning process. This becomes a tough job when appropriate experts are not available.
- Lack in data security –Saving confidential data on machine learning servers often seems to be risky. this is because of the factthat machines cannot differentiate between sensitive and insensitive data.
- Data bias –at times certain aspects of a data set are given more important than the others in machine learning models. If machine learning models do so in order to generalise the outcomes, then this would lead to inaccurate results.
Although machine learning is facing many issues but today it is one of the most evolving fields in artificial intelligence. Machine learning is of high importance from the fields of medical diagnostics to predictions and even classification of data.