Artificial Intelligence Training
This Artificial Intelligence Training will first take you through Introduction to Data Science and Statistical Analytics, Introduction to R Programming, Data Exploration, Data Wrangling and R Data Structure. It will teach you Data Visualization like Bar Graph, Histogram, Correlogram etc.
This AI Training will also explain Predictive Modeling (Linear Regression, Logistic Regression) using AI. It will also explian you concepts like Decision Tree, Random Forest etc. This AI Training will introduce you to Neural Networks and Time Series.
The main highlight of this AI training will be the projects and case studies. In this course you will able to do different projects and case studies which will clear all the concepts you will learn during this Artificial Intelligence Training.
Few of the clients we have served across industries are:
DHL | PWC | ATOS | TCS | KPMG | Momentive | Tech Mahindra | Kellogg's | Bestseller | ESSAR | Ashok Leyland | NTT Data | HP | SABIC | Lamprell | TSPL | Neovia | NISUM and many more.
MaxMunus has successfully conducted 1000+ corporate training in India, Qatar, Saudi Arabia, Oman, Bangladesh, Bahrain, UAE, Egypt, Jordan, Kuwait, Srilanka, Thailand, HongKong, Germany, France, Australia and USA.
AI Course Duration: 35-40 Hours
AI Training Timings: Week days 1-2 Hours per day (or) Weekends: 2-3 Hours per day
AI Training Method: Online/Classroom Training
AI Study Material: Soft Copy
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
- 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
- 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, What is Naive Bayes?
- 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.
- 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.
- 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.
- 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.
- 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
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.