What exactly is ‘Machine Learning’?

Machine learning is a set of techniques, which help in dealing with vast data in the most intelligent fashion (by developing algorithms or a set of logical rules) to derive actionable insights.

By the time, you get through the basics, you would have wondered how is ‘Machine Learning’ different from other terms like Artificial Intelligence, Deep Learning, and Statistics.

So, let's understand the importance of these terms in the context of machine learning:

Artificial Intelligence

Artificial Intelligence refers to the procedure of programming a computer (machine) to make a rational decision.

Machine Learning is a subset of AI where the machine is trained to learn from its past experience. The past experience is developed through the data collected. Then it combines with machine learning algorithms to deliver the final results.

Statistics

Statistics is that branch of mathematics that utilizes data, either of the entire population or a sample drawn from the population to carry out the analysis and present inferences.

Some statistical techniques used are regression, variance, standard deviation, conditional probability, and many others. Machine Learning algorithms use statistical concepts to execute machine learning.

Deep Learning

Deep Learning is associated with a machine learning algorithm (Artificial Neural Network, ANN) which uses the concept of the human brain to facilitate the modeling of arbitrary functions.

Some Real-World Examples of Machine Learning

• Image Recognition — Machine learning is frequently used for facial recognition within an image, tagging in social media, and recognizing handwriting.
• Speech Recognition — Devices like Google Home or Amazon Alexa convert live voice and recorded speech into a text file, based on machine learning.
• Medical diagnosis — It can help with the diagnosis of diseases. Many physicians use chat-bots with speech recognition capabilities to discern patterns in symptoms.
• Statistical arbitrage — Arbitrage is an automated strategy that uses a trading algorithm to analyze a set of securities using economic variables and correlations, in finance to manage a large volume of securities. Machine learning optimizes it to enhance the results.
• Predictive analytics — Machine learning can classify available data into groups, which are then defined by algorithms. This classification is used to calculate the probability of a fault and determine the accuracy.
• Extraction — Machine learning can extract structured information from unstructured data, for predictive analysis, thus helping in creating data sets.

Now that, you have tested the waters and have some idea on Machine Learning, I hope you would dive into this world of data, algorithms, system models, graphs, and discover much more.

Have fun with Machine Learning !!