ALL ABOUT MACHINE LEARNING

Hello My Dear Connections ,

I'm very glad to share an interesting article on Machine Learning. It is one of the emerging technologies around the world. Today why I'm posting this article is because in our college there is 7+7 thrust areas where we learn about the 7 Innovation Technologies and 7 Innovation Industries , in which my interest is towards Machine Learning.

Machine Learning is a subfield of Artificial Intelligence that gives computer the ability to learn without explicitly programmed. Machine Learning is a branch of Artificial Intelligence (AI) and Computer science which focuses on the use of data and algorithms to imitate the way that humans learn , gradually improving its accuracy. IBM has rich history with machine learning. Arthur Samuel is credited for coining the term " Machine Learning" with his research around the game of checkers.

Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix's recommendation engines and self-driving cars. Machine learning is an component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predications , and to uncover key insights in data mining projects.

How Machine Learning Works ?

• A Decision Process :

In general , machine learning algorithms are used to make a prediction or classification. Based on some input data , which can be labeled or unlabeled , your algorithm will produce an estimate about a pattern in the data.

• An Error Function :

An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.

• A Model Optimization Process :

If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this " evaluate and optimize " process, updating weights until a threshold of accuracy is reached.

Real World Machine Learning Use Cases

• Image Recognition :

Image recognition is a well - known and widespread example of machine learning. It can identify an object as a digital image , based on the intensity of the pixels in black and white or color images. E.g. : Label an X-ray as cancerous or not .

• Medical Diagnosis :

Machine learning can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. E.g. : Oncology and pathology use machine learning for recognize cancerous diseases.

• Speech Recognition :

Machine learning can translate speech into text. The speech can be segmented by intensities on time frequency band as well. Some of the most common uses of speech recognition software are devices such as Google Home or Amazon Alexa. E.g. : Voice search, Voice dialing, Appliance control.

Challenges of Machine Learning

• Technological Singularity
• AI Impact on jobs
• Privacy
• Bias and Discrimination
• Accountability
• Lack of Training Data

Conclusion :

Today we seen about the overview of machine learning. Through this article I have learnt a lot new things about machine learning. I hope you people also got an idea of what is machine learning. Thank you all for this opportunity to express my own thoughts with you.

" INNOVATION DISTINGUISHES BETWEEN A LEADER AND A FOLLOWER ."