Python- Machine Learning
Introduction to Machine Learning using Python. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data.
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What You Will Learn

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Requirements

 

  • Statistics.
  • Linear Algebra.
  • Calculus.
  • Probability.
  • Programming Languages.
Description

Course Details:

Module 1- Introduction to Data Analytics 

  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using the command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Module 2- Introduction to R programming

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R factors

Module 3- Data Manipulation in R 

  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions

Module 4- Data Import techniques in R

  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping

Module 5- Exploratory data Analysis

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing Graphs

Module 6- Basics of Statistics & Linear & Logistic Regression 

  • Basics of Statistics
  • Inferential statistics
  • Probability
  • Hypothesis
  • Standard deviation
  • Outliers
  • Correlation
  • Linear & Logistic Regression

Module 7- Data Mining: Clustering techniques, Regression & Classification

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K- means clustering

Module 8- Anova & Sentiment Analysis

  • Anova
  • Sentiment Analysis

Module 9- Data Mining: Decision Trees and Random Forest 

  • Decision Tree
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest

 

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