Machine Learning : How to Analyze your Data

Machine Learning is a process of learning from the data generated in the past. With statistical techniques, the computer systems get the ability to learn by processing the data and not necesaarily programming them to do so. It is an application of Artificial Intelligence (AI) and focusses on the development of programs that can access the data and use it for learning themselves. Just as humans learn from their past experiences, so can computers where data is equal to experience.

The process of learning begins with observations of data, such as examples, direct experience, instructions, in order to look for patterns in the provided data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention/assistance and adjust actions according to the situation.

There are basically 2 types of Machine learnings

• Supervised
  The computer is presented with example inputs and their desired outputs, given by a teacher, and the goal is to learn a general rule that maps inputs to outputs. Sometimes, the input signal can be partially available or restricted to special feedback
• Unsupervised
  No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It can be a goal in itself say discovering hidden patterns in data or a means towards an end (feature learning).

Machine learning enables analysis of huge volume of data. Usually it delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, however it may also require additional time and resources to train it properly. Therefore combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Everybody wants to gain using the potential of Machine learning but it is not easy to use. However you require data science expertise and knowldge of Machine learning techniques and methodologies. To solve such problem, Hydrogen platform is coming up with their Hydrogen Ion API, which can help you in executing machine learning techniques on complex data set with limited data science expertise.

Hydrogen Ion API automatically generates a data analysis pipeline that can include data pre-processing, feature selection, and feature engineering methods to train and evaluate models with an automated machine learning framework (“AutoML”). Hyperparameter optimization can be added to the data to automatically determine what parameters to optimize for.

Features of Hydrogen ion api are

• REST API
  Ion is a REST API which enables data scientists to pre-process datasets, and train and evaluate models with machine learning. All API responses are returned in JSON format.

• Cloud based data processing
  It allows uploading of datasets by specifying any URL and store pre-processing and training workflows which can be used with multiple datasets.

• Automated Machine Learning (Auto ML)
  AutoML built into the API can take any dataset and generate powerful machine learning metrics without any input from the user. It’s data science made simple!

• Hyperparameter Optimization
  Their proprietary hyperparameter optimization is very powerful and will automatically determine what parameters to optimize for in each machine learning model.

To know more about Hydrogen’s ion’s API please refer the documentation available on their website