Artificial Intelligence Training

Course Content

Introduction to Data Science and Statistical Analytics

• Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science.

Introduction to R

• Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case

Data Exploration, Data Wrangling and R Data Structure

• Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions

Data Visualization

• Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram

Introduction to Statistics

• Terminologies of Statistics ,Measures of Centres, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA

Predictive Modeling – 1 ( Linear Regression) Using AI

• Supervised Learning – Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation ( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories

Predictive Modeling – 2 (Logistic Regression) Using AI 

• Logistic Regression

Decision Trees

• What is Classification and its use cases,? What is Decision Tree,?Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix

Random Forest

• Random Forest, What is Naive Bayes?

Unsupervised learning

• What is Clustering & it’s Use Cases,? What is K-means Clustering?, What is Canopy Clustering?, What is Hierarchical Clustering?

Association Analysis and Recommendation engine

• Market Basket Analysis (MBA), Association Rules, Apriori Algorithm for MBA, Introduction of Recommendation Engine, Types of Recommendation – User-Based and Item-Based, Recommendation Use-case

Introduction to Neural Networks

• Fundamentals of Artificial Neural Network (ANN), various building blocks of ANN, what is Deep Learning, the process flow of reinforcement learning, how biological neural networks work, important terminologies in Artificial Neural Networks, relevant use cases of ANN, deep learning and reinforcement learning.

Time Series

• What is Time Series data?, Time Series variables, Different components of Time Series data, Visualize the data to identify Time Series Components, Implement ARIMA model for forecasting, Exponential smoothing models, Identifying different time series scenario based on which different Exponential Smoothing model can be applied, Implement respective ETS model for forecasting

Data Science Projects

Project 1 : Augmenting retail sales with Data Science

Industry :  Retail

Problem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.

Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.

Highlights:

• Association rules for transaction data
• Association mining with Apriori algorithm
• Generating rules and identifying patterns.

Project 2 : Analyzing pre-paid model of stock broking

Industry : Finance

Problem Statement : Finding out the deciding factor for people to opt for the pre-paid model of stock broking.

Topics : In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.

Highlights :

• Deploying the rules of statistical analysis
• Implementing data visualization
• Linear regression for predictive modeling.

Project 3 : Cold Start Problem in Data Science

Industry : Ecommerce

Problem Statement :  how to build a recommender system without the historical data available

Topics : This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.

Highlight :

• Algorithms for Recommender
• Ways of Recommendation
• Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
• Complete mastery in working with the Cold Start Problem.

Project 4 : Recommendation for Movie, Summary

Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

• Recommendation for movie
• Two Types of Predictions – Rating Prediction, Item Prediction
• Important Approaches: Memory Based and Model-Based
• Knowing User Based Methods in K-Nearest Neighbor
• Understanding Item Based Method
• Matrix Factorization
• Decomposition of Singular Value
• Data Science Project discussion
• Collaboration Filtering
• Business Variables Overview

Case Study

The Market Basket Analysis (MBA) case study

• This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

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