Regression Regression models are used to predict a continuous value. Predicting prices of a house given the features of house like size, price etc. is one   of the common examples of regression. It is a supervised technique. Types of Regression: -   Simple Linear Regression  Polynomial regression Support Vector Regression   Decision Tree Regression   Random Forest Regression 1.         Simple Linear Regression:-   This   si one of most common and interesting type of regression technique. Here we predict a target variable Y based on the input variable X. A linear relationship should exist between target variable and predictor and so comes the name linear regression. 2.         Polynomial Regression: -   In polynomial regression, we transform the original features into polynomial features of a given degree and then apply linear regression on it. 3.         Support Vector Regression :-   In SVR, we identify a hyper plane with maximum margin such that maximum numbers of data po

  Machine Learning Machine learning is a tool for turning information into knowledge.   Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that   we would otherwise struggle to discover. The hidden patterns and knowledge about   a   problem   can be used to predict future events and perform all kinds of complex decision making. Tom Mitchell gave a “well-posed” mathematical   and relational definition that   “A computer program its performance on T, as measured by   P, improves with experience   E”. Types of Machine Learning:- 1)       Supervised   Learning: -   Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it. 2)        Unsupervised Learning:-   The model learns through observation

  Machine Learning and it’s Types: - Machine learning is a tool for turning information into knowledge.   Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that   we would otherwise struggle to discover. The hidden patterns and knowledge about   a   problem   can be used to predict future events and perform all kinds of complex decision making. Tom Mitchell gave a “well-posed” mathematical   and relational definition that   “A computer program its performance on T, as measured by   P, improves with experience   E”. Types of Machine Learning: - 1)        Supervised   Learning: -   Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it. 2)       Unsupervised Learning :-   The model learns t

 Hypothesis function and testing: - Hypothesis testing is a statistical method that is used in making statistical decision using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter. Ex : you say avg student in class is 40 or a boy is taller than girls. All those example we assume need some statistic way to prove those. we need some mathematical conclusion what ever we are assuming is true. Hypothesis testing is an essential procedure in statistics. A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. When we say that a finding is statistically significant, it's thanks to a hypothesis test. The process of hypothesis testing is to draw inferences or some conclusion about the overall population or data by conducting some statistical tests on a For drawing some inferences, we have to make some assumptions that lead to two terms that ar

 Scope :- Increase in Data Generation:- Due to excessive production of data, need a method that can be used to structure, analyze and draw useful insight from data. This is where machine learning comes in. It uses data to solve problems and find solution the most complex tasks faced by organizations. Improve Decision Making :- By making use of Various algorithms, Machine Learning can be used to make better business decisions. For example, Machine Learning is used to forecast sales, predict downfalls in the stock market, identify risks and anomalies, etc. Uncover patterns & trends in data:- Finding hidden patterns and extracting key insight from data is the most essential part of machine learning. by building predictive models and using statistical techniques, machine learning allows you to dig beneath the surface and explore the data at a minute scale. Understanding data and extracting patterns manually will take days, whereas machine learning algorithms can perform such computati

 Model :- A model is a representational abstraction containing a set of logical and quantitative relationships between the members of a set of variables or primitives. An important reason behind constructing a model is that it helps to manage the complexity in a problem and facilitates arriving at good solutions and at the same time helps to reduce the design costs. A model helps to understand the problem easily. Life cycle process: - A life cycle model specifies the different activities that need to be performed to develop a software product and the sequencing of these activities. The software life cycle is also sometimes called as the systems development life cycle (SDLC). Basically, the classical waterfall model is the basic life cycle customer. This is called product conception. Starting with his stage it undergoes transformation through a series of identifiable stages until it is fully developed and released to the customer. After release, the product is used by the customer and

Booting Process : - Whenever a computer system is "cold started", say, after being powered or following a system crash, at least a portion of the operating system must be brought into main memory and given control of the processor. This activity is known as system booting or bootstrapping of an operating system. Typically, the hardware initially transfers control to a known address where a starting routine in ROM is placed. This routine is called the bootstrap loader . It can be used to bring the rest of the system gradually to main memory, for instance, from secondary memory or from another node in a distributed system. In disk-based systems, the core portion of the operating system is often placed at a known address, called the boot block or boot area of a known system disk drive. Thus, the bootstrap loader routine can include a rudimentary from of a disk driver whose primary function is to load and to activate the initialization section of the operating system. This