For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.
Source: KdnuggetA beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
Source: KdnuggetSo what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.
Source: KduggetWhat common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
Source: MediumFor a myriad of data scientists, linear regression is the starting point of many statistical modeling and predictive analysis projects. The importance of fitting, both accurately and quickly, a linear model to a large data set cannot be overstated.
Source: TDSI started my way in the Data Science world a few years back. I was a Software Engineer back then and I started to learn online first (before starting my Master's degree).
Source: HOBMachine Learning has become increasingly visible important because of the digital revolution of companies leading to the production of massive data of different forms and types, at ever increasing rates. Due to the advancements in computing technologies and exposure to huge amounts of data, the applicability of machine learning has dramatically increased.
Source: HOBLogistic Algorithms as the name suggest it comes under regression algorithms but with logistics regression the answer which comes is categorical as in the answer is either yes or no, either true or false so it is classified the values of fixed values are categorical the dependent variable the output, this is what we getting like this answers this will also categorize under the classification algorithms.
Source: HOBDeep Learning is not very interpretable, and this makes it undesirable in cases where it is important to understand why a deep learning model is making certain predictions. Deep Learning will not replace traditional Machine Learning, they will live side by side. Deep Learning only adds the capability to bring low quality data into the fold, it self-learns rich features, and turns low quality data, like pixels and sound samples, into high quality features, which it then feeds into traditional machine learning. In fact, Deep Learning actually has normal machine learning as part of its pipeline.
Source: HOBDecision Tree Algorithm is a part of supervised Machine Learning Algorithm. This algorithm is used for solving regression and classification problems just like other algorithms. This algorithm is basically used to predict the value from the passed trained data by applying learning decision rules. This is the most powerful algorithm among all as this algorithm can be easily visualized and understandable by the humans.
Source: HOBSome algorithms are better at learning with small data while others are preferable for large data. This fact can be understood rigorously through statistical learning theory. Intuitively, algorithm that chooses from a large or complex collection of models needs a larger data set to converge to a model that generalizes well to new data. Thus there is a trade-off between how complex model one wants to be able to learn and how much data and therefore also compute resources that one can provide.
Source: HOBLearn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
Source: HOBThe truth is that there are innumerable forms of regressions, which can be performed. Each form has its own importance and a specific condition where they are best suited to apply.
Source: HOBMachine Learning Algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory.
Source: HOBOnce the Business Intelligence reports and dashboards have been prepared and insights which are extracted from them, this information becomes the basis for predicting future values. And the accuracy of these predictions lies in the methods used.
Source: HOBThere are many different types of analysis to retrieve information from big data. Each type of analysis will have a different impact or result. The data mining technique you should use, depends on the kind of business problem that you are trying to solve.
Source: HOBWith only heavy statistical background professional have proper experience in this domain and recruiters should hire data scientists, who have a rich and strong background and capable to do their job. You can do data science without statistics, Data science without statistics is possible, even desirable. Data science is about automating these boring tasks. And automating much more advanced tasks.
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