Distance MBAs do not be left behind, know the key terms in Artificial Intelligence and Machine Learning
Industry experts predict that artificial intelligence and machine learning is the future. Automation and machine learning are threatening to take away jobs which are low level and repetitive. On the other hand, this AI and ML has ability to detect early signs, help him get best treatment plan and save his life. Whether we like it or not, AI and ML are going to stay for quite some time and affect all of us.
As a student of distance MBA in AI and ML, let us try to understand the key terms in this fast growing domain. Here our focus is not to understand the terms in complete depth but to the extent that one can understand its applications in the real world.
Artificial intelligence is a special branch of computer science which focusses on creating automated computer systems which can take decisions based on the input data. Here the attempt is to develop machine which can act and take decisions like humans. Using artificial intelligence machines can learn and solve problems.
We have seen that computers needs programming to perform some task. However, there are situations where programming in advance is not possible at all. In such cases, a computer software takes a step and based on the outcome decides the next step. In this fashion after some iterations the program discovers how to reach the required outcome. This is machine learning. Machine can learn on its own and better its output with each iteration without human intervention.
Machine learning is a subset of Artificial Intelligence. Here software algorithms learn based on input data. These algorithms apply probability theory to establish co-relation between input parameters and the outcome.
There are various types of machine learning algorithms. Theas algorithms can be used to do prediction based on given input parameters. They find applications in every domain starting from Human resources, finance, healthcare, manufacturing and so on.
Some of the examples of application of machine learning are
- In predicting who are better customers to promote financial loans
- Predicting what should be marketing budget allocation across various channels
- To find probable loan defaulters
- To predicts sales of certain products
- To predict no-shows in the appointments for doctors
Deep learning and Neural Networks
Deep Learning is a subset of machine learning. It mainly employs Artificial Neural Networks (ANN). Here the learning structure is a hierarchy for levels in the neural network. This is applied in image and video processing applications.
In neural network there is a hierarchy or layers. Here each layer takes the input data and transforms it into slightly more composite representation. Let us take case of face recognition. The first layer of neural network takes raw input of pixels and converts it into edges. These edges are fed into second layer which then formulate arrangement of edges. With the edges, the subsequent layer can encode features like nose or eyes and then ultimately a face can be recognized.
Computer vision is about automatic extraction, processing and analysis of useful information from digital imagery or video. Face recognition is one of the examples of computer vision. Computer vision has many applications in advance robotics.
Supervised learning is a type of machine learning. Here the machine learns by example. Input data sets are labelled. Using these the algorithm can identify labels for the new data sets.
For example, we have a dataset of various attributes about the person and the outcome that a person has defaulted in the loan payment or not. Based on this data, a supervised learning algorithm can create a model. This model can be used to predict if the person would likely default or not from a list of new customers.
Unsupervised learning is different from supervised learning. Here there is not specific output variable to be predicted. Unsupervised learning algorithm creates groups or clusters of similar or equivalent records based on various parameters. It is also called clustering.
This type of machine learning can be used for doing segmentation of customers.
A recommendation engine is used to suggest the customers, most relevant items which they are most likely to consume. Recommendation engine captures the past behavior of the customer like his/her browsing habits. Additionally it also processes similar data of other users and based on the similarity recommends the products which the users might be interested in. Amazon website is one of the most popular example of recommendation engine.
Sentiment analysis is doing analysis or mining of opinions. Everyday users write various things about movies, products, services etc. on social media. There are comments and reviews written. Sentiment analysis is an automated process of analyzing all such data to determine whether the opinion expressed is positive, negative or neutral. Based on this analysis, many decision regarding marketing, product launches can be taken.
Distance MBA and AI ML
You may be aiming to become manager in any trade, be it finance, healthcare or marketing, AI and ML influence has started growing in all industries. Considering this, you need to be aware of these terms from the latest trending technology buzz called AI and ML.