Humans learn from their past experiences while Machinesfollow the instructions given by humans. But we can train/learn machines to follow instructions to learn from past data using past experiences which are called Machine Learning.
Machine Learning Model
If we need to keep the accuracy of our Machine Learning Model we need two most important things. They are:
More Data
Better Model
Application of Machine Learning
Health Care
Sentiment Analysis (Like, Dislike)
Fraud Detection
E-Commerce
Basically, Machine Learning can be mainly divided into two categories. They are supervised
learning and unsupervised learning.
Supervised Learning
In supervised learning, before the analysis is done it
builds the model and then we can apply
the algorithm to estimate the parameters of the model. Classification,
Decision Tree, Bayesian Classification, Neural Networks, Association Rule Mining
are common examples of supervised learning.
Classification is a supervised learning technique. It
maps the data into predefined groups. It used to develop a model that can
classify the population of records at a large level. The classification algorithm
requires that the classes be defined based on the data attribute value. The
classifier training algorithm uses these pre-defined examples to determine the
set of parameters required for proper discrimination.
Decision Tree
A Decision Tree is a flow chart-like tree structure, where
each node denotes a test on an attribute value, each branch represents the result
of the test, and tree leaves represent classes. The drive model can be
represented in different forms such as
classification (if-then) rules, decision tree, mathematical formula, or neural
networks Decision tree can easily be converted to a classification tree. Decision
trees are simple to understand and provide good results even with small data.
Decision tree algorithms can be used for classification in a wide range of application
areas such as manufacturing, financial analysis, fraud detection, and education.
ID3, CART, J48, NB Tree REP Tree are some commonly used data mining
algorithms.
Bayesian
Classification
Bayesian Classifier is a statistical classifier that
could be used to predict class membership probabilities indicating that whether
the tuple belongs to the particular class or not. Bayesian Classification is
based on the Bayes theorem.
Bayes theorem is
as follows:
P(H/X) = P(X/H) P(H) / P(X)
Neural Networks
Neural Networks is a collection of neurons such
as processing units with the weighted
connection between the units. It composes of many elements, called nodes which
are collected in between. The collection between two nodes is weighted and by
the adjustment of this weight, the training of the network is performed. A
classification model can be represented in different forms such as Neural
Networks and Decision Tree. There are many advantages of neural networks such as
adaptive learning ability, self-organization, real-time operation, and
insensitivity to noise. Neural networks are used to identifying patterns or trends
in data and well suited for prediction or forecasting needs. The most common neural
network algorithms are Black Propagation, NN Supervised Learning, and Radial
Base Function Network.
Unsupervised Learning
In unsupervised learning, we do not create a model
before the analysis is done, instead, we simply apply the algorithm directly to
the dataset and observe the result. Then a model may be created according to the
basis of the obtained results. Clustering is one of the examples of
unsupervised learning.
Clustering
Clustering is the process of grouping data into classes,
so that objects within a cluster similarity is high in comparison to one
another, but are very dissimilar to objects in other clusters. Dissimilarities
have been observed on the basis of attribute value describing the objects often
distance used. Clustering has been frequently used in data mining applications
for discovering patterns in huge datasets. There are many clustering techniques
like Partitioning methods (K-means, K-medoids), Hierarchical methods(CURE,
CHAMELEON), Density-based methods(DBSCN & OPTICS), Grid-based
methods(STING, CLIQUE) and Model based methods(EM algorithm).
Other Data Mining Techniques
Other Data Mining techniques are Association Rule
Mining, Prediction, Time Series Analysis and Sequential Patterns.
Association Rule Mining is the discovery of
association relationships or corrections among a set of items. Association and
correction are used to find the frequent itemset among large data sets.
Association rule for a given dataset is very large and they are generally in
value.The main task of association rule
mining is to find sets of the binary variables that co-occur together frequently in the transaction database. Association rule holds many algorithms like Apriori,
CDA, DDA, interestingness measure. Association rules are if-then statements
that find uncover relationships between unrelated data in the relational database. The most common association rules are multilevel association rules, multi-dimensional association rules, and quantitative association rules.
Prediction is a data mining technique used to
identify the relationship between independent variables and the relationship
between the dependent and independent variables. The regression technique can be used to generate a model for prediction. Regression analysis can be used to model the
relationship between one or more independent variables and dependent variables.
Prediction techniques can be used to predict the possible values of some missing
data and the value distribution of certain attributes in a set of objects. Most
common Regression techniques such as Linear Regression, Nonlinear Regression,
Multivariate Nonlinear Regression.
Time Series Analysis is a sequence of data points,
measured typically at successive times speed at uniform time intervals such as
stock prices, currency exchange rates, and the volume of product sales, collected
over monotonically increasing time. Rule induction algorithm such as Version
Space, AQ15, C4.5 are commonly used in time series data mining applications.
Sequential Patterns is one of the data mining
techniques that seek to discover similar patterns in data transactions over a
business period. The uncovered patterns are used for further business analysis
to recognize relationships among data.
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Real estate marketing: - Real estate marketing will not differ much from online marketing, so if you have previously worked in the field of online marketing, you will be able to start working in the field of real estate marketing easily. Real estate marketing can be divided into three stages only; the first stage is to find The real estate, for example, that you found an apartment, a mansion or a villa for sale at a certain price and with certain specifications, here comes the second stage, which is the stage of real estate marketing, so now the currency of promoting the property that you have found begins, and during this stage, you may face difficulty in the beginning, but with time and work you will master it, and finally the third stage which is fun to finish The deal between the seller and the buyer and you are the middle men, and of course, here the deal should end on profit for all, not only you.
How to earn from real estate marketing: The question here is can anyone work in the field of real estate marketing? The answer will definitely be no !! Because the real estate marketer must have some qualities or features in order to start working in this field, one of the most important of these qualities is dealing with society, meaning that you are not a shy person because you will not be able at this time to deal with people or specifically the seller and buyer, and there are other qualities. There are many that you should search for before starting work, as for how to find a property and promote it on social media, there are many ways you can learn and use it, as there are free real estate ads on many sites and you can access these sites and you can advertise your property for free like Craiglist, MarketPlace in Facebook and many more. The field of real estate marketing or profit by marketing real estate is a very profitable field and you can start it now.
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How to earn from real estate marketing: The question here is can anyone work in the field of real estate marketing? The answer will definitely be no !! Because the real estate marketer must have some qualities or features in order to start working in this field, one of the most important of these qualities is dealing with society, meaning that you are not a shy person because you will not be able at this time to deal with people or specifically the seller and buyer, and there are other qualities. There are many that you should search for before starting work, as for how to find a property and promote it on social media, there are many ways you can learn and use it, as there are free real estate ads on many sites and you can access these sites and you can advertise your property for free like Craiglist, MarketPlace in Facebook and many more. The field of real estate marketing or profit by marketing real estate is a very profitable field and you can start it now.
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