Twitter Sentiment Analysis Conceptual Infographic Notes | Getting Started with Sentiment Analysis based NLP Project with its main objectives , considerations , challenges , Approach and Steps

 

An Introduction to Classification Algorithms with 10 fundamental questions | An Infographic Note with questions and answers

 

Questions Covered :

Q1) What are Naive bayes Classifiers ?

Q2) What do you mean by Probabilistic Classification ?

Q3) What do you mean by Statistical Classification ?

Q4) Give an example of Statistical Classification .

Q5) What is a Classifier Algorithm ?

Q6) Give some examples of various forms of classification .

Q7) Which is the most used Classification algorithm in Statistics ?

Q8) What are some of the characteristics of a Classification problem ?

Q9) What are the popular acronyms for samples , independent and dependent variables in Machine learning (ML)

Q10) What is the difference between Binary Classification and Multiclass Classification ?

Machine Learning Algorithms used in solving some of the popular real world problems

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Machine Learning Algorithms used in solving some of the popular

real world problems

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 *  The majority of all the real world problems in machine learning are classification and regression problems which are to be performed over structured data ( that means it doesn't take into account much of unstructured data into the account )  

*  In the real world machine learning environment , deep learning also plays a very shallow and limited role in the field of machine learning and therefore it is considered as one of the smallest factors that go into building of applications over machine learning

 *  One of the the mostly used algorithm to augment the model performance is XGBoost algorithm .This was created by developer and machine learning engineer Tianqi Chen who changed the face of applied machine learning which as already mentioned is the best case use of machine learning algorithms and the way augmentation of built model performances is done which come under the category of gradient boosting algorithms which also happens to work over and on top of structured data .

  

*  Therefore , gradient boosters like XGBoost are considered as the gold standard for modelling over structured data problems used in data competitions .Again it needs to be re-iterated and kept in memory eternally that almost all of the data problems use XGBoost and other gradient boosters for working over any data problem .

 

 *  It is also believed that the top cloud based companies like Google , Microsoft  and Amazon also use gradient boosters over their cloud paltform for analysis into structured data problems

 *  Google's Cloud based Machine Learning Platform is called as AutoML . In this platform and application , the AutoML tables use gradient boosters and deep learning models for hyperparameter tuning

Last modified: 14:20

Infographic Notes on most commonly used Machine Learning Algorithms - Revision Short Notes on each topic , working principle , goals , application areas

 

* supervised

* unsupervised

* linear regression

* logistic regression

*decision tree

* knn

* k-means

* naive bayes

* svm - support vector machines