Machine Learning is the advanced method of data analysis which iteratively learns from the data. Today, Machine Learning is not limited to the books or the theoretical knowledge only, it has crossed its limit and benefitting the entire world. As a new breed of software that can learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities.
Evolution of Machine Learning:
Machine Learning is not new and it was discovered in 1950’s with simple and basic algorithms, but why it has gained so much of popularity in recent years? Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from the pattern recognition but now it can adapt the new data behavior from the previous computations. It has gained the ability to learn the problem-solving approach without being explicitly programmed for every single task.
The machine learning has changed the mindsets. Now, the things are possible which we cannot even think earlier.
Here are a few widely-publicized examples of machine learning applications you may be familiar with:
A self-driving Google car. The essence of machine learning.
Amazon and Netflix recommendations for daily life.
Why is Machine Learning Important?
Let’s look at the facts, why machine learning is so important? There are several reasons:
The four V’s (Volume, Variety, Velocity & Veracity) of Big Data which really needs to handle more accurate and in an efficient manner.
Machine Learning made it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale and avoid unknown risks.
Who’s using it?
Most of the industries are using “Machine Learning” technology, especially those who are working with a large amount of dataset on daily basis. They understand the value of machine learning technology.
The sectors, who are using machine learning on a large scale have been discussed below:
The financial industries like banks or other businesses using machine learning technology to fulfill their two important objectives: to identify the important insights of the data and prevent fraudulent activities. The insights can help to introduce new services and to understand their customers to serve them in a better way.
Marketing and sales
The machine learning helps to recommend and promote items you’d be interested in. It’s completely based on your past product interest and your purchase history. It captures the data, analyze it and uses it to personalize the shopping experience. The companies also using these analyses for many other things like running campaigns or offers etc.
Machine Learning Methods:
Two of the most commonly used machine learning methods are Supervised Learning and Unsupervised Learning – but there are also other methods of machine learning.
The majority of practical machine learning uses a supervised learning. All data is labeled and the algorithms learn to predict the output from the input data.
Supervised learning problems can be further categorized into two categories:
Classification: A classification problem is when the output variable is a category, such as “A” or “B” or “Yes” and “No”.
Regression: A regression problem is when the output variable is a real value, such as “rupees” or “weight”.
Algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a part or a product could have data points labeled either “S” (stock) or “O” (out). The learning algorithm receives a set of inputs along with the corresponding defined outputs, and the algorithm learns by comparing its actual output with defined outputs to find the differences. It then modifies the model accordingly. Supervised learning is commonly used in applications where historical data points are likely to predict future. For example, it can anticipate when the order attempt is likely to be random or a genuine.
Unsupervised learning is where you only have input data (X) and no corresponding output variables.
These are called unsupervised learning because unlike supervised learning above there is no correct answers or defined labels. Algorithms are left to their own devices to discover and present the interesting structure in the data.
Unsupervised learning problems can be further categorized into two categories:
Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customer types by their purchasing behavior.
Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy eggs also tend to buy bread.
Problems where you have a large amount of input data (X) and in that only some of the data is labeled (Y) are called semi-supervised learning problems. The semi-supervised learning method is closely related to Supervised and Unsupervised methods.
Most of the real-world problems deal with the semi-supervised learning method because in the real world most of the data inputs are partially labeled. It’s easy to get and store the unsupervised data.
The scope of Machine Learning:
Machine Learning is a large future promising scope. It mainly focuses on neural network data processing algorithms. In the near future, we will be able to deny that a computer does not have IQ.
What Yet to Come?
There are several things which are yet to come, among of all some of them are listed below:
Real -Time speech translation.
Prolonging the mobile devices battery life – will prevent the unnecessary consumptions of the battery from apps.
Neural-Network running on our mobile devices.
Health and Fitness -will detect and help to cure diseases.
Cyber security optimization.
Worldwide dedicated teams are working on a large scale to make “Machine Learning” more accurate and reliable for taking complex business decisions.Do let us know your feedback on email@example.com or you can reach out to us at firstname.lastname@example.org