What is Machine learning?

Machine learning is a subset of artificial intelligence(AI) and is a field of computer science that concentrates on datasets and algorithms and the type of learning that imitates human intelligence and behavior. 

In machine learning, 

Input + Output = Program 

Humans learn from their own experience, while machine learns through data driven by a program or set of instruction and without being programmed. 

It aims at improving efficiency and accuracy. Machine learning is an essential part of the Data Science field, one of the growing fields. The main functions of machine learning systems are as follows: 

1. Descriptive: The data is used by the system that describes what has happened.
2. Predictive:  In this type, the data used by the system will predict what will happen.
3. Prescriptive:  The system will utilize the data to make recommendations regarding what steps to be carried out.

Basic terminology in machine learning

• Features - The data is called features that define the meaning of the data. It is a measurable property of a data object. It is vital to choose distinguishable and independent data. For example, in a real-time object like a book, the quality of pages, type of book cover, and contents, are its feature. 
• Target/Labels - It is the type of value to be predicted using machine learning. It is used mostly in supervised learning. If the feature is input into the system, a target or label is the output of the system. Labels are mostly used for building and identifying models. In some cases, labels are used as models. Here, as referred to in the above book's example as an object, the name of the book and the author is the label.
• Model - It is a theory that defines a relationship between features and targets. It works through algorithms and mathematics that can predict the model.
• Training - In this, the relation between labels and features can be learned. In training, the model, feature and expected targets are known. 
• Prediction - It applies the model to new unseen data. Based on the data target/labels are predicted. It refers to the output of an algorithm based on previously recorded data and is applied to new datasets when predicting the probability of certain results. 

Types of Machine Learning

• Supervised Learning:

1.  It is also called supervised machine learning. If the machine is told to learn input from the output, it is supervised. 
2. The expected output is taken as input. Its models are trained with labeled data sets that can organize data or predict results accurately. Its learning process is task driven.
3. Supervised learning helps the organization solve a variety of real-world problems.
4.  A few of the techniques used in supervised learning contain neural networks, Naive Bayes, support vector machine(SVM), etc.
5.  It is broadly classified into two categories mainly, clustering and regression. 

• Unsupervised Learning: 

1. It is based on self-learning. It is also called unsupervised machine learning. In simple words, it is a method without any supervision.
2.  It has the ability to classify both similar and dissimilar data. It examines patterns in the information that is unlabeled. 
3. It is data-driven type of learning. 
4. It helps in identifying hidden and unfamiliar patterns or groups of data with any human assistance.
5.  This type of example is used in online sales for detecting and analyzing consumer data and buys. 
6. The common type of unsupervised learning contains Principal Component Analysis (PCA),k-means clustering, mean shift algorithm, etc. 
7. It is classified into two categories mainly, clustering and association. 

• Semi-supervised Learning: 

1. It is a mixture of both supervised and unsupervised types of learning.
2.  It utilizes combined datasets both unlabeled and labeled for training its algorithm. 
3. This type of learning overpowers the flaws of the other two learning. 

• Reinforcement Learning: 

1. It is a feedback-based process. It is much similar to supervised learning, it is a type of behavioral machine-learning model. 
2. The model learns through trial and error.It learns from the error.  It is a type of learning that doesn't need sample data for the training phase of the algorithm.
3. It is reward as well as punishment type system according to type of favorable or non-favorable outputs. 
4. This type of learning is used in applications related to autonomous vehicles to train and drive by controlling machines for making the correct decision and is also used in training models to play games.

Applications of Machine Learning

Machine learning is advancing in many technological fields and industries, it is mainly used in :

• Finance industries 
• Healthcare
• Retail
• Virtual assistants
• E-commerce sectors
• Speech recognition
• Traffic predictions
• Online customer support
•  Social media 
•  Automation industries,  and many more. 

Conclusion

Around 67% of companies use machine learning. The primary need for machine learning is that it can increase data generation, helps in improving decision-making, and recognize the data's trends and patterns and ways to help solve complex problems, it can handle a large number of different types of data. 

Thanks for reading my blog. Do read, like, comment, and follow back my other blogs.Have a wonderful day ahead…