Complete course of Statistical and Machine Learning Data Mining Free. In this course you will learn different methods for better predictive Modelling and analysis for big Data. By this course you are able to solve specific method most commonly problem in predictive modelling.  In this course you will learn about machine learning, statistics and datamining. In this course you will learn brief introduction of statistics and data analysis. This course teach you that how to solve problems and make a better predictive model. This course is very useful and helpful for researchers, developers and programmers.

There is a difference between statistical and machine learning that is machine learning models are designed to make the most precise divination practicable and the statistical models are made for deduction about the connections between variables. Statistical models are use of statistics to make a representation of the data and then its have a way of behaving analysis to deduce any connection between to discover insights. We use Machine Learning in mathematical and statistical models to acquire a common apprehension of data to make divination.

You Learn These Topics From This Course:

  • The Personal Computer and Statistics
  • Statistics and Data Analysis
  • EDA
  • The EDA Paradigm
  • Data Mining Paradigm
  • Statistics and Machine Learning
  • Statistical Data Mining
  • Science Dealing with Data: Statistics and Data Science
  • Two Basic Data Mining Methods for Variable Assessment
  • Scatterplots
  • Smoothed Scatterplot
  • The Scatterplot
  • The Smooth Scatterplot
  • CHAID-Based Data Mining for a Smoother Scatterplot
  • The Importance of Straight Data Simplicity and Desirability for Good
  • Model-Building Practice
  • Straightness and Symmetry in Data
  • Data Mining Is a High Concept
  • The Correlation Coefficient
  • Straightening a Handful of Variables and a Baker’s Dozen of Variables
  • Principal Component Analysis
  • A Statistical Data Mining Method for
  • Market Share Estimation: Data Mining for an Exceptional Case
  • Data Mining for an Exceptional Case
  • Dummy Variables
  • Basics of the Correlation Coefficient
  • Calculation of the Correlation Coefficient
  • Logistic Regression
  • Scoring an LRM
  • Candidate Predictor and Dependent Variables
  • Logits and Logit Plots

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