Machine Learning Application are everywhere. Each and every tech giants are using Machine Learning for better applications.

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Artificial Intelligence (AI) is everywhere. A possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is Machine Learning (ML).

%

of the industry are using Machine Learning.

Machine learning is closely related to computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data miningwhere the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

Daily Uses

If you are using Facebook, YouTube, Google, Amazon, Netflix, Twitter, Snapchat, Pinterest, etc.., then Congratulations, you are enjoying the Machine Learning Models.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.

Machine Learning Methods:-

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
  • Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.
  • Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.

Machine Learning Application

Image Recognition
One of the most common uses of machine learning is image recognition. There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image. In the case of a black and white image, the intensity of each pixel serves as one measurement. So if a black and white image has N*N pixels, the total number of pixels and hence measurement is N2. In the colored image, each pixel considered as providing 3 measurements to the intensities of 3 main color components ie RGB. So N*N colored image there are 3 N2 measurements.
  • For face detection – The categories might be face versus no face present. There might be a separate category for each person in a database of several individuals.
  • For character recognition – We can segment a piece of writing into smaller images, each containing a single character.  The categories might consist of the 26 letters of the English alphabet, the 10 digits, and some special characters.
Speech Recognition
Speech recognition is the translation of spoken words into text. It is also known as “Automatic Speech Recognition” (ASR), “computer speech recognition”, or “speech to text” (STT). In speech recognition, a software application recognizes spoken words. The measurements in this application might be a set of numbers that represent the s