Have you listened to your peers presenting Machine Learning yet have only an indefinite idea of what that implies? It is safe to say that you are stung out on gesturing your way through discussions with associates?

Let’s grow that!

The way toward learning automation starts with understandings of information, for example, models, direct understanding, or guidance. Such as to search for examples in information and settle on better choices later on dependent on the models that we give. The prime aim is to allow the computers to learn automatically without human interference and coordinate things accordingly. Machine Learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Introduction To Machine Learning

The name Machine Learning was first coined by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms of technological advancements.

If you browse through the net about ‘what is Machine Learning’, you’ll get at least 100 different definitions. However, the very first formal definition was given by Tom M. Mitchell:

“A computer program is said to learn from experience E concerning some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

In simple phrases, Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed to do so. In the sense, it is the practice of getting Machines to solve problems by gaining the ability to think.

Why Machine Learning?

You may ask why there is such a great amount in the Statistical Analysis System report about Machine Learning now. Why now, when artificial intelligence (AI), the parent technology to machine learning, has been around for more than 50 years? The purpose is that there is an exceptional convergence of massive volumes of Big Data, unique computing power, and advanced self-learning algorithms taking place. The affordability, viability, and feasibility of these three technologies are the driving forces behind why machine learning is becoming more and more prevailing today.

Development of Machine Learning

One can intuitively surmise machine learning is the present hot commodity, creating a strong impact on businesses, academia, and government in recent years. Presently, there is information all in one position – that documents growth across many indicators, including startups, venture capital, job openings, and academic programs.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in AI/ML wanted to see if computers could get from data. The iterative part of AI is significant because as models are presented to new information, they can autonomously adjust. They have gained from past calculations to deliver solid, repeatable choices and results. It’s a science that is not new – but rather one that has increased crisp force.

Why now?

• World’s data multiplying every 2 years.

• Extensive computation is available at a low cost.

• Easiness of use of Machine Learning and Artificial Intelligence platform

• New addition tools and paradigms

• Adoption of real user interfaces

• Expectations for apps to be creative

Machine Learning for the business

Lending in Machine Learning will be like investing in mobile a decade ago it can transform your business.

In the era of technology reformation, Machine Learning is encouraging its roots deep inside the industry. Machine Learning is the next boundary in data analysis. As organizations have access to more data, machine learning permits them to draw insights from the data at scale, at a level of granularity that ranges from single user interaction to global trends and their impact on the planet. The use of those insights can also range from customizing an individual user’s experience at the pixel level to creating new products and business opportunities that don’t currently exist.

How does Machine Learning Work? Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction based on the model.

The prediction is evaluated for precision and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.

Types of Machine Learning

Machine learning is sub-categorized into three types:

1. Supervised Learning – Train Me!

2. Unsupervised Learning – I am self-sufficient in learning

3. Reinforcement Learning – My life My rules! (Hit & Trial)

What is Supervised Learning?

Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset that acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.

What is Unsupervised Learning?

The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.

Suppose we presented images of apples, bananas, and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

What is Reinforcement Learning?

An agent can interact with the environment and find out what is the best outcome. It follows the concept of the hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and based on the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.

Putting it altogether

In similarity with raw computational power, complex new algorithms have been developed to allow data scientists to run models using all the accessible information. Previously models had to be generalized to simplify the analytical process, but machine understanding can now ingest 100% of the data generated by every asset or person. The result is a far higher degree of accuracy that would be achieved with human analysis.