This foundation of the AI & ML module is designed to help the learners decrypt Artificial Intelligence and Machine Learning Uncertainty. This module provides an outline of Artificial Intelligence, Machine Learning Concepts, and R programming.  

• Introduction to Data Science, AI&ML
• R Essentials
• Statistical Analysis 

Learning Outcomes: By the end of this module, you will get a basic knowledge of  Artificial Intelligence, Machine Learning, and Data Science Fields. Along with this, you will be familiar with R programming and Statistical Analysis.

In this module, you will learn the Python programming fundamentals like Python Environment Setup, Python Data Types, Object-Oriented Programming Concepts, Python Operators, Python Control, and Looping Statements.

• Python Essentials
• Python Environment Setup
• Python Data Types
• Python Looping and Control Statements
• Object-Oriented Programming Concepts
• Database Connection

Learning Outcomes: By the end of this module, you will be able to configure the Python Environment and perform different logical and mathematical calculations through Python Programs.
 

This Module discusses different Python Libraries like Numpy, MatPlotlib, Pandas, and Scipy.

• Numpy 
• Scipy
• Pandas 
• MatPlot

Learning Outcomes: At the end of this module, you will acquire real-time experience of working with Python libraries to perform different tasks like mathematical computing, Data Visualisation, Data analytics, and Scientific Computing.
 

This module deals with the fundamentals of probability and statistics. It also covers concepts like Data acquisition, Data Quality, Data Transformation, Big Data Frameworks, etc.

• Data Acquisition
• Data Pre-processing and Preparation
• Data Transformation and Quality
• Handling Text Data
• Big Data Fundamentals
• Big Data Frameworks(Spark, Hadoop, NoSQL)

Learning Outcomes: At the end of this module, you will acquire real-time experience of how to gather information from other systems, how to handle the text data, how to do the Data Transformation and Big Data.
 

The SAS Module gives you a detailed look at  SAS Installation, SAS Functions, SAS Data Sets, SAS Procedures, SAS Graphs, SAS Macros, SAS Format, etc. 

• SAS Introduction
• SAS Functions
• SAS Operators
• SAS Procedures
• SAS Graphs
• SAS Macros
• SAS Format

Learning Outcomes: By the end of this module, you will acquire hands-on knowledge of SAS Installation, Performing different operations through SAS Functions, Format a Variable. Along with this, you will get practical knowledge of Creating SAS Data Sets and Plotting Graphs through PROC GPLOT.
 

This module deals with implementing and designing verbal and visual representations of patterns, Different Visualisation Types, Sampling, Test for Hypotheses, etc.

• Data Visualisation
• Sampling and Estimation
• Inferential Statistics

Learning Outcomes:  By the end of this module, you will achieve hands-on knowledge of Data Visualisation, Sample versus Population, and Inferential Statistics.
 

In this module, you will learn concepts like Regression Basics, Hypothesis of testing, Relationship between Multiple Variables, Polynomial regression, Logit function, and interpretation, Forecasting Models, etc.

• Linear Regression
• Multiple Linear Regression
• Non-Linear Regression
• Forecasting Models

Learning Outcomes: At the end of this module, you will get a real-time experience of predicting behavior patterns, trends, and outcomes. Along with this, you will gain an in-depth understanding of Non-Linear and Multiple Linear Regression.
 

This module discusses topics like Machine Learning Techniques, Different Clustering Methods, Probability estimation, K-Nearest Neighbour Algorithm, Support Vector Machines,  Decision Trees, Association Rule Mining, etc.

• ML Foundations
• Clustering
• Classification(Naive Bayes Classifier, K-Nearest Neighbors)
• Association Rule Mining

Learning Outcomes: By the end of this module, you will acquire hands-on knowledge of Machine Learning Techniques, how to construct a hierarchical cluster, how to deal with categorical, continuous values in K-Means, Mathematical Model for Association Analysis.
 

In this module, we will learn about famous NN Architectures for getting different cognitive functions like Natural Language Processing, Object recognition. Later, you will study and implement different Artificial Intelligence Applications.

• Foundations of AI
•  Convolution Neural Networks
• Recurrent Neural Networks

Learning Outcomes:  At the end of this module, you will achieve practical knowledge of Neural Networks, Image Classification, Building Recurrent Neural Networks. 
 

This Module discusses topics like Deep Learning Libraries, Keras, Tensorflow, Deep Learning Algorithms, etc.

• Deep Learning Libraries
• Keras API
• TensorFlow
• Deep Learning Algorithms

Learning Outcomes: By the end of this module, you will get real-time experience on Deep Learning Models through Keras and Tensorflow Frameworks. Also, you will be able to implement Deep Learning Algorithms.
 

This Module discusses Advanced Deep Learning Concepts like Generating Images, Parallel Computing, Reinforcement Learning, Deploying Deep Learning Models.

• Distributed and Parallel Computing
• Deploying Deep Learning Models
• Reinforcement Learning
• Generating Images with Neural Style
• Object Detection through Convolutional Neural Networks

Learning Outcomes: At the end of this module, you will get hands-on knowledge of Parallel Computing in Tensorflow, Architecture of Deep Neural Networks, Deploying Deep Learning Models in Serverless environments. Also, you will acquire an in-depth understanding of Tensorflow Serving.

This module deals with concepts like Cloud Computing, AWS Basics, AWS Architecture, AWS S3, EC2, ELB, EBS, AMI, VPC.

• Introduction to Cloud Computing and AWS
• Storage Volumes and Elastic Compute
• Virtual Private Cloud
• Simple Storage Services
• AWS Lambda and Amazon Machine Learning

Learning Outcomes: By the end of this module, you will achieve real-time experience of creating AWS Account, launching EC2 Instance, Creating AMI for Launched Instance, AWS Storage, Creating VPC. Along with this, you will get extensive knowledge of AWS Lambda, and Amazon ML.

In this Module, you will explore the concepts like Introduction to Data Visualisation, Tableau Installation, Tableau Architecture, Tableau Interface, Creation of Sets, Tableau Filters, etc.

• Introduction to Data Visualisation
• Tableau Architecture
• Working with Data Blending
• Creation of Sets
• Calculations, Expression, and Parameters.
• Dashboards, Stories, and Filters
• Tableau Prep

Learning Outcomes: At the end of this module, you will get a real-time Tableau Installation experience, sharing the existing work, Creating the Sets, Working with the Filters, etc.