Machine Learning
Machine learning is a variation on artificial intelligence (AI) technique that allows computers to acquire knowledge without manually programming them in their system. Machine learning algorithms are trained on data, and they use this data to make predictions or decisions.
1. Data Science
- Introduction to Data Science
- Need for Business Analytics
- Data Science Life Cycle
- Different tools available for Data Science
- Pre-requisites of Data Science
2. R-Programming
- Introduction to R
- Installation of R
- Windows Installation
- Linux Installation
- Installation of R-Studio
2.1 Types of Variables
- Types of Operators
- Arithmetic Operators
- Logical Operators
- Relational Operators
- Membership Operators
- Special Operators
- If-else Flow Control
- Loops in R (While, For, Break, Next)
- Switch-Case
2.2 Types of Datatype
- Vectors
- Arrays
- List
- Matrices
- Factors
- Data Frames
2.3 Types of Loops
- For loop
- While Loop
- Nested Loops
2.4 Functions in R
- Function declaration with parameters
- Function declaration without parameters
2.5 R Data Interface
- Reading CSV files
- Reading XML files
- JSON files
- Scraping data from the Web
- SQL with R
- Databases with R
2.6 Data Visualization of R
- Pie Chart
- Bar graph
- Line Graph
- Scatter plot
- Stack Plot
- Box-Plot
2.7 Statistics in R
- Terminologies of Statistics
- Normal Distribution
- Binomial Distribution
- Regression Analysis
- Poisson Distribution
- Time-Series Analysis
- Chi-square Test Analysis
- Non-linear square analysis
2.8 Machine Learning in R
- What is Machine Learning ?
- Supervised Machine learning
- Unsupervised Machine learning
- Application of Machine Learning.
- AI vs Machine Learning
- Supervised Learning
- Classification algorithms
- Decision Tree
- Random Forest
- Naive-Bayes
- SVM Classifier
- Regression Learning
- Linear Regression
- Multiple Regression
- Logistic Regression
- Clustering
- K-means clustering
- K-nearest neighbour
Duration: 4 Months
Eligibility: 10+2+3 Any Computer Graduate (BE/BTech/BCA/BCS)