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What Is MATLAB Programming?
MATLAB® is a programming platform designed specifically for engineers and scientists to analyze and design systems and products that transform our world. The heart of MATLAB is the MATLAB language, a matrix-based language allowing the most natural expression of computational mathematics.
How to Learn Matlab and Data Analysis Algorithms
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Get a hold over the language to be used for implementation – practice the basics till you are comfortable.
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Understand and implement one algorithm at a time. You may not understand it completely in the beginning. Give it time. Do not get stuck at one place. Try something else and come back later. Get the intuition of what is going on behind the few lines of code written to implement it. With practice things will keep getting clearer. Keep reading about it from multiple sources.
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Make extensive notes when trying to understand / watching videos – it helps with internalizing the information as well as with the review.
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Understand limitations of each algorithm, if any.
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Understand the usual application areas of each of the algorithms and why are they used there.
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Try understanding how these algorithms differ from each other. Using a single problem statement and solving it using different applicable algorithms should help here.
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Remember, the algorithms are just tools to solve problems. Don’t lose sight of the main problem statement during implementation.
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Many times, simple implementations are good enough. Build a simple solution first quickly and then iterate - you may want to try different features, tuning the parameters and hyperparameters, different algorithms, stacking different algorithms together and so on. Make sure you try one thing at a time and not everything together since you would want to know what change made the algorithm(s) better or worse.
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Explain what you have done to one person who knows about the algorithms in technical terms and to another who does not know the algorithms per se but can follow the problem and its solution logically. Gaps in understanding are best understood when explaining to others.
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Learning is an iterative process – your first implementation may not be the best. It can be made better over time. Please be patient.
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It al known as a graphical model or probabilistic graphical model (PGM) or structured probabilistic model. It is the probabilistic model for which a graph expresses the conditional dependence structure between random variables.
Probabilistic Graphical models (PGMs) are statistical models that encode complex joint multivariate probability distributions using graphs. Basically
PGMs divided into two categories:
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Directed Graphical Models (DGMs), otherwise known as Bayesian Networks (BNs) and
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Undirected Graphical Models (UGMs) or Markov Random Fields (MRFs).
Object Segmentation
In machine learning there are different types of segmentation are used. Segmentation, the technique of splitting customers into separate groups depending on their attributes or behavior, makes this possible.
Below the some important segmentation techniques that are used in machine learning:
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Thresholding Segmentation.
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Edge-Based Segmentation.
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It is the process of reducing the number of independent variable, if your problem related to dimensionality reduction then we will help you to do your task and fit dimensionality reduction algorithms easily in your code. Below the some features which makes it better to improve machine learning accuracy and for which you use this in machine learning algorithms.
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Reducing Dimensionality of independent variables helps in many ways.
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Remove multi-collinearity to improve ML model performance
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Here we can say that dimensionality reduction plays a significant role in analyzing data.
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Feature Elimination
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Feature Extraction(PCA, t-SNE)
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Continue Reading About machine learning & Data Science
Stock Market Analysis Using Yahoo Finance
Multivariate Linear Regression and Polynomial Regression
Matrix as a Linear Transformation Using Applied Machine Learning
Exploring Tree-Based Regression Methods for 3D Sinusoidal Data
Decision Tree Grid Search In Applied Machine Learning
Machine Learning APA-7 Statistical Research Paper Writing
Implement and Train LSTM-based Models and Transformer-based Models
Algorithms Which Used In MATLAB
Linear Regression
Linear regression is a method of modeling relationship between a dependent variable y and one or more independent variable X. There can be one or more independent variables. When there is only one independent/explanatory variable, it is called Simple Linear Regression and for more variables, it is called Multiple Linear Regression
SVM
Support Vector Machine(SVM) is a supervised machine learning algorithm used for both classification and regression. The objective of SVM algorithm is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. Support vector machine analysis deals with even smaller data but more precisely since they involve the use of data from various hyperplanes.
Decision Tree
This is a very famous and widely used supervised learning algorithm which is used in classification problems. It works for both categorical and continuous variables. It used for both classification and Regression problems, but mostly it is preferred for solving Classification problems
Naive Bayes
This is a classification method. Naïve Bayes works on Bayes% theorem. This classification method assumes that the features in this method are independent. Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e.
KNN
It is both: a classification and a regression method. However it is widely used for classification problems. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure.
Random Forest
Random Forest or Random Decision Forest is supervised classification algorithm. It can also be used for the regression. In random forest, we can resolve the problem of over fitting if there are enough trees in the forest. This algorithm has two steps. One is to create the random forest and other is to make the predictions.
K-Means
k-means is a type of unsupervised learning algorithm, which is used for unlabelled data. In k-means we have clusters and each cluster has its own centroid. It work as, k-means picks k number of points for each cluster known as centroid.
Ensemble Models
The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decision. In other way we can say, a group of models or classifier are called ensemble. If you need any help related to ensemble methods then hire us.
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