Machine Learning

Why Machine Learning?

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It's a science that's not new – but one that has gained fresh momentum. Machine learning is an software of artificial intelligence (ai) that gives structures the capability to robotically examine and improve.

Syllabus

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,
2.7 Statistics in R
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