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Small Discussion About Linear regression in Machine Learning?

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What is Linear regression?

Linear regression is a linear system and the coefficients can be calculated analytically using linear algebra. ... 

Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms.

Linear regression is a very simple approach for supervised learning. Though it may seem somewhat dull compared to some of the more modern algorithms, linear regression is still a useful and widely used statistical learning method. Linear regression is used to predict a quantitative response Y from the predictor variable X.
Linear Regression is made with an assumption that there’s a linear relationship between X and Y.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.

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posted Nov 30, 2017 by Manish Tiwari

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What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

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

Machine learning is closely related to (and often overlaps with) 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 mining, where 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.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

  • Supervised machine learning algorithms
  • unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms
  • Reinforcement machine learning algorithms 

 

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What is Deep learning?

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. 

Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. 

Deep Learning

It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

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What is H2O in Machine Learning?
H2O.ai is a leader in the 2018 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.

H2O is open-source software for big-data analysis. It is produced by the company H2O.ai. H2O allows users to fit thousands of potential models as part of discovering patterns in data.

The H2O software runs can be called from the statistical package R, Python, and other environments. It is used for exploring and analyzing datasets held in cloud computing systems and in the Apache Hadoop Distributed File System as well as in the conventional operating-systems Linux, macOS, and Microsoft Windows. 

The H2O software is written in Java, Python, and R. Its graphical-user-interface is compatible with four browsers: Chrome, Safari, Firefox, and Internet Explorer.

H2O is a Java-based software for data modeling and general computing. The H2O software is many things, but the primary purpose of H2O is as a distributed (many machines), parallel (many CPUs), in memory (several hundred GBs Xmx) processing engines.

There are two levels of parallelism:

 - within node
 - across (or between) nodes
The goal of H2O is to allow simple horizontal scaling to a given problem in order to produce a solution faster. The conceptual paradigm MapReduce, along with a good concurrent application structure,  enable this type of scaling in H2O.

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**What is Math Kernal Library?**
Math Kernel Library (Intel MKL) is a library of optimized math routines for science, engineering, and financial applications. Core math functions include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier transforms, and vector math. The routines in MKL are hand-optimized specifically for Intel processors.

The library supports Intel processors and is available for Windows, Linux and macOS operating systems.

Main Categories for Math Kernal Library

  • Linear algebra: BLAS routines are vector-vector (Level 1),    matrix-vector(Level 2) and matrix matrix(Level 3) operations for real and complex single and double precision data. LAPACK consists of tuned LU, Cholesky and QR factorizations, eigenvalue and least squares solvers.  
  • MKL includes a variety of Fast Fourier Transforms   (FFTs) from 1D to multidimensional, complex to complex, real to    complex, and real to real transforms of arbitrary lengths.  
  • Vector math functions include computationally intensive core mathematical operations for single and double precision real and complex data types. These are similar to libm functions from compiler libraries but operate on vectors rather than scalars to provide better performance. There are various controls for setting accuracy, error mode and denormalized number handling to customize the behavior of the routines. 
  • Statistics functions include random number generators and probability distributions. optimized for multicore processors.    Also included are compute-intensive in and out-of-core routines to    compute basic statistics, estimation of dependencies etc. 
  • Data fitting functions include splines (linear, quadratic, cubic, look-up,    stepwise constant) for 1-dimensional interpolation that can be used in data analytics, geometric modeling and surface approximation applications. 
  • Deep Neural Network
  • Partial Differential Equations    
  • Nonlinear Optimization Problem Solvers

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