xav data science replies
please read below student posts and reply each in 150 words.
chait – In Supervised learning, you train the machine using data which is well “labelled.” It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.
A supervised learning algorithm learns from labelled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.
Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.
Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning deep learning and reinforcement learning methods.
Why Supervised Learning?
- Supervised learning allows you to collect data or produce a data output from the previous experience.
- Helps you to optimize performance criteria using experience
- Supervised machine learning helps you to solve various types of real-world computation problems.
Why Unsupervised Learning?
Here, are prime reasons for using Unsupervised Learning:
- Unsupervised machine learning finds all kind of unknown patterns in data.
- Unsupervised methods help you to find features which can be useful for categorization.
- It is taken place in real time, so all the input data to be analysed and labelled in the presence of learners.
- It is easier to get unlabelled data from a computer than labelled data, which needs manual intervention.
hima – Discussion
Statistical Learning is a Framework is used to Understand and to model the vast data sets to solve Big-Data related issues. Nowadays, Statistical Learning is becoming popular. Statistical Learning is nothing but understand the statistics and predict the output data.
Statistical Learning is finding out the relation between predictors independent variables(X), and response-dependent variable Y) and develop a Statistical model that can predict the values of Y based on X values.
There Two types in Statistical Learning
1, Supervised
2. Un supervised
Supervised Statistical Learning:
Building statistics on output data based on one more input value
xi, i = 1,n
For each observation of Predictors measures, there should be an associate Variable port, so that we can Predict the output Model for that. Linear and logistic Regression comes under Supervised Statically Learning.
Linear Regression: There is a direct relationship between predator and response.
Coming to Regression they are two types Quantitative and Qualitative.
Linear Regression is use full for predicting a quantitative response
Logistic Regression: Logistic Regression will be used when the Dependent variable is Binary
Unsupervised: You have only input values. Unlike Supervised, there is no Output for the predictors. You can use it to discover the structure and modeling input values.
Based on the clustering you can subgroup the data, based on subgroups and main group difference we can calculate the statistics. K-means clustering and hierarchical clustering.
Ravi – What Is Statistical Learning?
Statistical learning is a context of indulgent data grounded on figures, which can be categorized as overseen or unverified. Overseen statistical learning comprises of building a statistical model for envisaging, or appraising, an output based on one or more inputs, while in unverified statistical learning, there are inputs but no supervising output; but we can learn relationships and structure from such data. One of the simple way to comprehend statistical learning is to regulate connotation between predictors & response and evolving a precise model that can envisage response variable (Y) on foundation of predictor variables (X). In circumstances where a set of inputs X are gladly obtainable, but the output Y is not known, we often treat (f) as black box as long as it produces accurate likelihoods for Y.
There are circumstances where we are concerned in acknowledging the way that Y is pretentious as (X) change. In this condition we wish to appraisal (f), but our goal is not necessarily to make predictions for (Y). We are more engrossed in accepting relationship between X and Y. Now (f) cannot be pickled as a black box, because we need to know its exact form. This is the extrapolation some of the many approaches that we use for statistical learning, some are less supple, or more obstructive. When corollary is the goal, there are clear merits to using modest and moderately unbending statistical learning methodology. When we are only concerned in likelihood, we use supple models available. When we make a postulation about the functional form of (f) and try to guess (f) by estimating the set of strictures, these methods are called parametric methods. Non-parametric systems do not make explicit expectations about the form of (f), instead they seek an estimate of (f) that gets as close to the data points as conceivable.
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