MACHINE LEARNING USING R PROGRAMMING free videos and free material uploaded by ducatittrainingschool staff .
Syllabus / What will i learn?
FUNDAMENTAL OF STATISTICS
- Population and sample
- Descriptive and Inferential Statistics
- Statistical data analysis
- Variables
- Sample and Population Distributions
- Interquartile range
- Central Tendency
- Normal Distribution
- Skewness
- Boxplot
- Five Number Summary
- Standard deviation
- Standard Error
- Emperical Formula
- central limit theorem
- Estimation
- Confidence interval
- Hypothesis testing
- p-value
- Scatterplot and correlation coefficient
- Standard Error
- Scales of Measurements and Data Types
- Data Summarization
- Visual Summarization
- Numerical Summarization
- Outliers & Summary
Module 1- Introduction to Data Analytics
- Objectives:
- This module introduces you to some of the important keywords in R like Business
- Intelligence, Business
- Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems
- This module tells you what is R and how it is used by the giants like Google, Facebook, etc
- Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software
- in analytics, install R and its packages
- Topics:
- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
Module 2- Introduction to R programming
- Starting and quitting R
- Recording your work
- Basic features of R.
- Calculating with R
- Named storage
- Functions
- R is case-sensitive
- Listing the objects in the workspace
- Vectors
- Extracting elements from vectors
- Vector arithmetic
- Simple patterned vectors
- Missing values and other special values
- Character vectors Factors
- More on extracting elements from vectors
- Matrices and arrays
- Data frames
- Dates and times
- NOTE:-
- Assignments with Datasets
Import and Export data in R
- Importing data in to R
- CSV File
- Excel File
- Import data from text table
- Topics
- Variables in R
- Scalars
- Vectors
- R Matrices
- List
- R � Data Frames
- Using c, Cbind, Rbind, attach and detach functions in R
- R � Factors
- R � CSV Files
- R � Excel File
- NOTE-:
- Assignments
- Business Scenerio/Group Discussion
- R Nuts and Bolts-:
Module 3- Managing Data Frames with the dplyr package
- The dplyr Package
- Installing the dplyr package
- select()
- filter()
- arrange()
- rename()
- mutate()
- group_by()
- %>%
- NOTE:-
- Assignments
- Business Scenerio/Group Discussion
Module 4- Loop Functions
- Looping on the Command Line
- lapply()
- sapply()
- tapply()
- apply()
- NOTE-:
- Assignments
- Business Scenerio/Group Discussion
Module 5- Data Manipulation in R Objectives
- In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis
- Thus using and exploring the popular functions required to clean data in R
- Topics
- Data sorting
- Find and remove duplicates record
- Cleaning data
- Merging data
- Statistical Plotting-:
- Bar charts and dot charts
- Pie charts
- Histograms
- Box plots
- Scatterplots
- QQ plots
- NOTE:-
- Assignments with Datasets
Objectives
- Control Structure Programming with R
- The for() loop
- The if() statement
- The while() loop
- The repeat loop, and the break and next statements
- Apply
- Sapply
- Lapply
- NOTE:-
- Assignments with Datasets
Factors
- Using Factors
- Manipulating Factors
- Numeric Factors
- Creating Factors from Continuous Variables
- Convert the variables in factors or in others
Reshaping
- Data Modifying
- Data Frame Variables
- Recoding Variables
- The recode Function
- Reshaping Data Frames
- The reshape Package
- NOTE:-
- Assignments with Datasets
Explore many algorithms and models
- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction
- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine
Module 6 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
- Machine Learning Languages, Types, and Examples
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
Module 7 - Supervised Learning I
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
Module 8 - Supervised Learning II
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
Module 9 - Unsupervised Learning
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters - Single Linkage Clustering
- Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
- Density-Based Clustering
Module 10 - Dimensionality Reduction & Collaborative Filtering
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
Module 11 - Classification
- An Overview of Classification
- Why Not Linear Regression?
- Logistic Regression
- The Logistic Model
- Estimating the Regression Coefficients
- Making Predictions
- Logistic Regression for >2 Response Classes
- Lab: Logistic Regression
- The Stock Market Data
- Logistic Regression
- NOTE-:
- Assignments with Different Datasets
- Business Scenerio/Group Discussion
Module 12 - Tree-Based Methods
- The Basics of Decision Trees
- Regression Trees
- Classification Trees
- Trees Versus Linear Models
- Advantages and Disadvantages of Trees
- Bagging, Random Forests, Boosting
- Bagging
- Random Forests
- Lab: Decision Trees
- Fitting Classification Trees
- Fitting Regression Trees
- NOTE-:
- Assignments with Different Datasets
- Business Scenerio/Group Discussion
Module 13 - Time Series & Forcasting
- Time series
- Estimating and Eliminating the Deterministic Components if they are present in the Model
- Estimating and Eliminating Seasonality if it is present in the Model
- Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
- Identify 'order' of the ARMA model
- 'Forecast' or Predict for Future Values
- Practise on R
- NOTE-:
- Assignments with Different Datasets
- Business Scenerio/Group Discussion
Module 14 - Support Vector Machines � Outline
- Understand when the Support Vector family of methods are an appropriate method of analysis
- Understand what a hyperplane is and how they are used with the Support Vector methods
- Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
- Vector Machines
- Know how each of the algorithms determines the best separating hyperplane
- Distinguish between hard and soft margins and when each is to be used
- Know how to extend the method for nonlinear cases
- NOTE-:
- Assignments with Different Datasets
- Business Scenerio/Group Discussion
Module 15 - Principal Component Analysis �Outline
- Understand what principal components are and when principal component analysis is appropriate
- Describe eigenvalues and eigenvectors and how they are used to calculate principal components
- Understand loading and loading vectors
- Know how to decide how many principal components to use in the analysis
- Be able to use principal component analysis for regression
- NOTE-:
- Assignments with Different Datasets
- Business Scenerio/Group Discussion
Curriculum for this course
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