Watch the simple video and read the blog to build a machine-learning model in 5 minutes. No Coding is required. This is the Quickest and easiest method to make Machine Learning Model
Yes, this is the quickest and easiest way to build a machine-learning model. To learn more such easy tutorials you must follow me on Youtube:
To start you must download the data below.
Machine learning model without coding – The simple way to Data Science
Dataiku provides a variety of possibilities for Data enthusiasts to develop Machine learning models, create charts, data cleaning, and many other features.
If you want to quick guide to downloading Dataiku Software you must read my other blog:
Install Dataiku in simple steps
Who needs to watch this video?
For all Data scientists, Students, teachers, Data engineers, and other Data enthusiasts. The tool not only saves time to build the machine learning model but also is the easiest way to do it. The person does not need to code which makes it a unique selling point.
How to learn Dataiku?
The simplest way to learn from official Dataiku Website. You may also like to watch videos to learn on Bootcamp. Also, studing Computer science, hadoop, Data analyst could help to understand the topic deeply.
What is a Regression Analysis?
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome’ or ‘response’ variable, or a ‘label’ in machine learning parlance) and one or more independent variables (often called ‘predictors’, ‘covariates’, ‘explanatory variables’ or ‘features’). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared differences between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). Continue reading on Wikipedia.
What is Random Forest Analysis?
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees’ habit of overfitting to their training set.Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees. However, data characteristics can affect their performance. Continue Reading on wikipedia
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Quick and easiest method to make Machine Learning Model
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