Machine Learning Basics | What Is Machine Learning? | Introduction to Machine Learning

Humans learn from past experiences, while Machines follow instructions.

So, humans train machines to learn from past data and that’s what Machine Learning is all about.

Machine learning is a subset of Artificial Intelligence. Machine learning enhance machines with the ability to learn autonomously based on observation and analysis within a given data set without specific programming.

Machine Learning Types

  1. Supervised Learning:

Deals with Regression and Classification whereby algorithms learn the relationship between specific inputs and outputs based on training data and human feedback.

2. Unsupervised Learning:

Mostly Clustering and Dimension reduction where algorithm analyze data for trends and patterns without being given a specific output variable or human feedback.

3. Reinforcement Learning

A model free and Model based aspect of ML where the algorithm learns over time to maximize returns based on the rewards it receives for performing certain actions.

Applications of Machine Learning

1.    Robotics Engineering:

ML has improved automation which has become a major part of robotics engineering with repetition of multiple tasks involving collection of large data for improvement processes.

2. Healthcare:

ML has improved the healthcare industry through EHR (Electronic health records) to improve predictive medical care, radiology, cognitive decisions, AI-assisted robotic surgery, virtual health assistance for patient, customer support, genetics and genomics.

Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning

3.  Gaming

The gaming sector has improved so much with the help of machine learning algorithms positioning the motion gaming by analyzing subsequent moves of opponents and re-adjusting itself according to level of play amongst opponents.

4.  Driverless Cars:

Driverless cars collect a lot of user data in form of real time images and videos of traffic variations with the help of its sensors. So, by automation of data collected, ML based on information collected automate commute to various positions with the help of GPS (Global positioning systems)

5.   Image and Speech Recognition

Image recognition means identifying and detecting objects through a digital platform such as digital image or video.

WHILE

Speech Recognition deals with recognizing speech patterns, despite your inability to type on your device and convert voice requests to text.

With the invention of product services such as Google Voice, Cortana, Siri, Amazon echo etc. ML has been very helpful in making sure past data collected are translated into meaningful use for consumers by improving image recognition with the use of image data sets, computer vision and image recognition software’s and so on.

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