In this era of digital revolutions data is floating in trillions and billions , but to tap data and convert them into a meaningful information we need to feed them into a computer and analyse it at length and breadth. In-order to harness the power of data sets , Arthur Samuel in 1959 coined the world Machine Learning
Machine learning is a branch of data analytics where the machine based on the input Models (Experience) predicts certain behaviours and also learn to adapt without much programming intervention.
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the Machine Learning field: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” This definition of the tasks in which machine learning is concerned offers a fundamentally operational rather than defining the field in cognitive terms.
Why ML is Important ?
Machine Learning is a key finding in this digital evolution & it’s undoubtedly going to shape the future of how organisations & a nations will make decisions. The volume and the scale at which we human beings are generating information , it becomes really important that this ML a branch of AI takes the responsibility to predict where we the most powerful creation of this mother earth (Human Being) are heading to. Our computers are no more used for only simple calculations they are capable of processing peta bytes of data in seconds , so ML algos when supplemented with right set of data can be a game changer for sectors like manufacturing , healthcare , auto industries, Banking & financial , Science
Types of Machine Learning :
- Supervised ML : Supervised learning is the machine learning task of inferring a function from labelled training data . The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyses the training data and produces an inferred function, which can be used for mapping new examples
it’s the basis for features in everyday apps like search functionality in Google Photos or Apple Photos, wherein ML can differentiate locations, people, and time of day without any written information.
2. Unsupervised ML :
In Unsupervised ML, no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
Unsupervised learning is where you only have input data (X) and no corresponding output variables.
The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
: Neural Network :Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used unsupervised learning algorithms.
: Data Security : Behavioral-based detection in network security has become a good application area for a combination of supervised- and unsupervised-machine learning.
3. Reinforcement ML : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent. The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
Between supervised and unsupervised learning is semi-supervisedlearning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing.
Problems where you have a large amount of input data (X) and only some of the data is labeled (Y) are called semi-supervised learning problems.
Application of ML :
Machine Learning algorithms is being used currenlty in many areas of reserach like
- Health Predictions
- Data Security
- Fraud Detection specially in banking & insurance sectors
- Financial Trading & Analysis specially in predicting which listed stocks will perform better
- Self driving machines(Cars)
- Smart Searches
- Writing recommendations engine specially in e-commerce sectors and financial sectors.
Our Data needs to make some sense else it will be a lost opportunity to create a future.