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 ?

Infographic Descriptive Note on Input and Output Streaming Functions in Python

 

Infographic Note on Road Map to become an expert in Python - Important for Data Science Pursuing Students and Job-Seekers

 

Concepts related to Array datatype object handling and manipulation using Numpy and Scipy Libraries of Python - A Descriptive Article

 

Numpy and SciPy Libraries Basic Functionality in Python

 

AWS ( Amazon ) Web Services - Predominant Characteristics of any Cloud Computing Application and Application Server

 

AWS ( Amazon ) Web Services - Disadvantages of using AWS

 

Amazon Web Storage Services

 

Some usages and applications of Naïve Bayes Algorithms over Data Science - Data Science Interview Questions

 

Question on Data Ssience - What are the disadvantages of using Naive Bayes Classifier Algorithm

 

Questions on Data Science - What are the advantages of working with the Naive Bayes Classifier Algorithm

 

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