AI/ML course content

Overview : Data Analytics is very upcoming with more jobs in this stream, we ensure that a participant will learn basics of statistics, data reading and application of Machine learning using R statistical software package. This will ensure the participant learns basics and is able to interpret the data after it is coded.

Pre-requisites : No Prior knowledge needed.

Who is an Ideal student for AI?ML training that we intend to start on 16th August.
We encourage an enthusiast who is willing to learn and will do every bit to ensure the participant completes the training successfully. We have no target groups as such and encourage an enthusiast willing to take up the programme as the profession is in demand and there is presence of skill shortage..

Chapter 01: Analytics – demo

  • 1.1 Introduction to Analytics
  • 1.2 Market & Use cases
  • 1.3 Analytics Tools

Chapter 02: Data Processing

  • 2.1 Introduction to Data & Data Types
  • 2.2 Analytics Methodology
  • 2.3 Case Study

Chapter 03: Statistical Tools I

  • 3.1 Role of Statistics & Mathematics
  • 3.2 Descriptive Statistics
  • 3.3 Concepts of Sample, Population, Probability
  • 3.4 Probability Distributions

Chapter 04: Statistical Tools II

  • 4.1 Data Visualization
  • 4.2 Correlation Analysis
  • 4.3 Regression Analysis

Chapter 05: Introduction to 'R'

  • 5.1 What is "R"& importance of R
  • 5.2 R installation, Basic Function in R
  • 5.3 Loading Data File, Saving 
  • 5.4 Descriptive statistics using R

Chapter 06: Data Treatment

  • 6.1 Data Visualization
  • 6.2 Data Cleaning 
  • 6.3 Data Transformation

Chapter 07: Exploratory Data Analysis

  • 7.1 Correlation & Regression Analysis
  • 7.2 Multicollinearity
  • 7.3 Introduction to Factory Analysis 
  • 7.4 Principal Component Analysis 

Chapter 08: Cluster Analysis

  • 8.1 Supervised & unsupervised Techniques
  • 8.2 Hierarchical Clustering
  • 8.3 K-Means Clustering
  • 8.4 Case study uisng R

Chapter 09: Association rules

  • 9.1 Introduction
  • 9.2 Apriori algorithm
  • 9.3 Case study using R

Chapter 10: Predictive Modeling

  • 10.1 Linear Regresion 
  • 10.2 Logistic Regression
  • 10.3 Case study using R

Chapter 11: Classification Modeling

  • 11.1 Introduction to Classification
  • 11.2 Decision Trees
  • 11.3 Decision Tree Modeling
  • 11.4 Case study using R

Chapter 12: Assessment

  • 12.1 Modeling Development using "R"

Leave a Reply

Your email address will not be published. Required fields are marked *