Artificial Intelligence Certification Program

Module 1: Introduction to Artificial Intelligence

Goal: Build foundational understanding of AI, history, and real-world relevance.

• What is AI? Definition & Evolution
• History & Milestones in AI (Turing, ELIZA to GPT-4)
• Types of AI:
  • Narrow AI vs General AI vs Super AI
  • Reactive Machines vs Self-Aware AI
  • Myths and Facts about AI
  • Current Trends in AI (Generative AI, Automation, Robotics, AI in Analytics)
• Applications of AI in:
  • Healthcare, Finance, Retail, HR, Marketing, Education, Manufacturing

Module 2: AI vs Machine Learning vs Deep Learning

Goal: Understand the relationship between AI, ML, and DL.

• What is Machine Learning (ML)?
• What is Deep Learning (DL)?
• Differences: AI vs ML vs DL
• Real-world Examples:
  • AI = Alexa, ChatGPT
  • ML = Fraud detection, Recommendation Systems
  • DL = Facial Recognition, Autonomous Vehicles

Module 3: Machine Learning Essentials

Goal: Understand how machines learn from data.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Features, Labels, and Models
• Train-Test-Split
• Algorithms (Intro):
  • Regression, Decision Trees, Clustering, Classification
• Overfitting vs Underfitting
• Optional Hands-on (if learners are tech-savvy):
  • Use Python + Scikit-learn or Excel ML plug-ins
  • Load a dataset and apply a basic model

Module 4: Data, AI & the Role of Big Data

Goal: Show how data fuels AI and the need for clean, structured inputs.

• Structured vs Unstructured Data
• Importance of Data Quality
• Data Cleaning Basics
• Introduction to Data Sources (CSV, APIs, Web, Sensors)
• Role of Big Data in AI
• Data Pipelines (ETL Conceptual Flow)

 Module 5: Natural Language Processing (NLP)

Goal: Understand how AI interacts with human language.

• What is NLP? Scope & Applications
• Text Processing (Tokenization, Stop Words, Lemmatization)
• NLP Use Cases: Chatbots, Sentiment Analysis, Translators, Resume Parsing
• Tools: NLTK, spaCy, Hugging Face (brief overview)
• Concept of Word Embeddings (word2vec, BERT – simplified)

Module 6: Computer Vision (CV)

Goal: Understand how AI “sees” the world.

• What is Computer Vision?
• Use Cases: Object Detection, OCR, Surveillance, Medical Imaging
• Image vs Video Analysis
• Introduction to OpenCV and CV tools
• Brief on Face Recognition & Emotion Detection

Module 7: Generative AI & LLMs

Goal: Explore ChatGPT, CoPilot, Bard, and their underlying models.

• What is Generative AI?
• Use Cases: Content Generation, Code Writing, Summarization
• Large Language Models (LLMs) – GPT, LLaMA, PaLM
• How ChatGPT works (Simplified with architecture and training examples)
• Prompt Engineering:
  • Crafting good prompts for productive responses
  • Role of temperature, max tokens, creativity
• Demo: Use ChatGPT or Gemini for content, analysis, code

Module 8: AI in Business and Industries

Goal: Discover how AI is transforming industries.

AI in:

• Finance: Credit Scoring, Fraud Detection
• Healthcare: Predictive Diagnosis, AI-Assisted Surgery
• Retail: Inventory Prediction, Personalized Shopping
• HR: Resume Screening, Employee Engagement
• Marketing: Recommendation Systems, Dynamic Ads

Case Studies:

Netflix, Amazon, Tesla, Zomato

Module 9: Tools & Platforms in AI

Goal: Introduce top tools and platforms available for AI use.

• No-code/Low-code Tools:
  • Microsoft Azure AI Studio
  • Power BI with AI visuals
  • MonkeyLearn, Peltarion
• Programming-Based Tools:
  • Python, TensorFlow, Keras, PyTorch
  • Chatbot Platforms: Dialogflow, IBM Watson, Rasa
  • Data Visualization + AI (Power BI, Tableau with GPT)

Module 10: Ethics, Challenges & Future of AI

Goal: Foster responsible understanding of AI in society.

• Ethical Concerns:
  • Bias in AI
  • Deepfakes
  • Job Displacement
  • Explainable AI (XAI)
  • Legal Implications & AI Governance
• Future of AI:
  • AGI (Artificial General Intelligence)
  • AI + Quantum Computing
  • Autonomous Economy

Module 11: Capstone Project / Case Study (Optional for Certification Track)

Learners choose from:

• Build a Smart Resume Screener
• Predict Sales Using AI Tools
• Create an AI-Powered Chatbot
• Use ChatGPT to generate business insights

Add-Ons

• Pre/Post Assessments with MCQs
• Prompt Engineering Templates
• AI Tool Cheat Sheets (for Excel, ChatGPT, No-Code ML, etc.)
• Completion Certificate

Module 1:- Installing & Running Python

• Python 2.7 vs Python 3
• Local Environment Setup
• Installing Python on different platforms(Windows and Linux)
• Python Interpreter and Python Interactive Shell
• Python IDE(Pydev, Pycharm,VIM)

Module 2:- Python Introduction

• Python Overview
• History Of Python
• CPython, Jython, PyPy
• Python Features
• Areas Of Application of Python
• Understanding More About Python
• Writing your First Python Program
• Interactive Mode Programming
• Script Mode Programming
• Dir and help: Getting help from the Python interpreter.

Module 3:- Python Syntax ,Keywords and Operators

• Python Identifiers
• Various Operators and Operators Precedence
• Reserved Words,Lines and Indentation
• Multi-Line Statements,Quotation in Python
• Comments in Python,Using Blank Lines
• Command Line Arguments
• Python Input/Output:Using the Print Function
• Getting Input from User
• Python Basic Data Types And Variables
• Binary, octal and hexadecimal numbers
• Convert one data type to another

Module 4:- Expressions, Statements, Variables, Strings

• Working With Numbers
• Working With Booleans
• Math library and its various operations
• Working with Strings
• String types and formatting
• String Operations and Task
• Program to find duplicate characters in a String.
• Program to reverse a string
• Program to check if String is Palindrome
• Program to remove a newline in Python

Module 5:- Python Data Types: List,Tuples,Dictionaries

• Python Lists, Tuples, Dictionaries
• Accessing Values
• Basic Operations
• Indexing, Slicing, and Matrixes
• Built-in Functions & Methods
• Exercises on List, Tuples And Dictionary
• Remove Duplicate from Lists
• Program to find the index of an item of a tuple
• Python program to convert a list to a tuple
• Python program to reverse a tuple
• Program to convert a tuple to a dictionary

Module 6:- Making Decisions – if Statements

• The Relational Operators
• The Logical Operators
• Simple if Statement,if-else Statement
• If-elif Statement
• More Advanced If, ElIf & Else Processing

Module 7:- Loop Control

• Introduction To while Loops
• Count-Controlled while Loops
• Event-Controlled while Loops
• Using continuE,Using break
• Introduction To for Loops
• For loops with files,list,tuples and dictionaries

Module 8:- Iterators

• Understanding Iterators
• Using iter And next
• Iterators And Dictionaries
• Other Iterators

Module 9:- Functions And Scopes

• Introduction To Functions – Why
• Defining Functions
• Calling Functions
• Functions With Multiple Arguments
• Predicate Functions,Recursive Functions
• Function Objects,Generators,Decorators
• Anonymous Functions, Higher-Order Functions
• Scope , Global Scope, Local Scope , Nested Scope

Module 10:- Modules and Packages

• Using Built-In Modules
• User-Defined Modules
• Module Namespaces
• Installing and Uninstalling a package
• Package vs Library vs Module

Module 11:- File I/O

• Printing to the Screen
• Reading Keyboard Input
• Opening and Closing Files
• Open Function,file Object Attributes
• close() Method ,Read,write,seek
• Rename,remove,
• Mkdir,chdir,rmdir

Module 12:- Error And Exceptional Handling

• Exception Handling, Assertions: The assert Statement
• What is Exception, Handling an exception
• The except Clause with No Exceptions, the try-finally Clause
• The argument of an Exception, Raising an Exceptions
• User-Defined Exceptions 

Module 13:- Regular Expression

• Matching and Searching- match() and search() Functions
• Search and Replace
• Regular Expression Modifiers
• Regular Expression Patterns
• Regular Expression Quantifiers

Module 14:- Introduction to Data Science and Machine Learning

• Matching and Searching- match() and search() Functions
• Search and Replace
• Regular Expression Modifiers
• Regular Expression Patterns
• Regular Expression Quantifiers

Module 15:- Tools & Languages available

• Python .R
• Python & R Differences
• Python Distribution
• Python tools for Data Science
• Anaconda Installation
• Jupiter Notebook Usage and Examples

Module 16:- Numpy

• Introduction to Numpy. Array
• Creation,Printing Arrays
• Basic Operations- Indexing, Slicing and Iterating
• Shape Manipulation – Changing
• Shape,stacking and spliting of array
• Random number

Module 17:- Pandas, Matplotlib and Seaborn

Pandas

• Introduction to Pandas
• Importing data into Python
• Pandas Data Frames,Indexing Data
• Frames ,Basic Operations With Data
• frame,Renaming Columns,Subletting and filtering a data frame.

Matpolib

• Introduction,plot(),Controlling Line
• Properties,Working with Multiple
• scatter, hist, bar, piechart
• subplot, titles, axis, colormap
• Figures,Histograms

Seaborn

• Plot for categorical and numerical data
• Plot for categorical vs numerical, numerical vs numerical, categorical vs categorical
• distplot, jointplot, boxplot, barplot, countplot, violinplot, swarmplot

Module 18:- Exploratory Data Analysis

• Data Manipulations and Wrangling
• Drawing Insights and Completing analysis
• Imputing NA values

Module 1:- Getting Started with Tableau

• Introduction to Data Visualization
• Leading Data Visualization Tools
• Introduction to Tableau
• Exploring Interface and Important Key Components
• Navigating Through Tableau Menu Tabs
• Exploring Each Menu Tab i.e. File, Data, Worksheet, Dashboard, Story, Analysis, Map, Format, Server, etc.*
• Tableau – Design Flow
• File Types
• Tableau Data Types
• Show Me
• Data Terminology

Module 2:- Connecting to Data with Tableau Desktop

• Introduction to Data Connection
• Data Source Interface
• Types of Data Connections
• Extracting Data
• Custom Data View
• Joins and Unions
• Data Blending
• Live Connection Vs Extract
• Field Operations
• Basic Project Activity

Module 3:- Examining & Filtering

• The Sheet Interface
• Dimensions & Measures
• Hierarchies
• Data Granularity
• Highlighting
• Data Sorting
• Grouping Data
• Data Filtering
• Data Source Filters
• The Filter Shelf
• Dimension Filters & Card Modes
• Context Filters
• Measure Filters
• Creating Sets

Module 4:- Field Types & Charts

• Utilize Auto-Generated Fields
• Use Titles, Captions and Tooltips Effectively
• Creating Bins
• ToolTip
• Basic Charts

Module 5:- Calculations in Tableau

• What are Calculations
• Methods to Create Calculated Field
• Introduction to Tableau Functions
• Operator and Syntax Conventions
• Introduction to Table Calculations

Module 6:- Level of Detail (LOD) Expression

• Level of Detail (LOD) Calculations
• Live Use Cases of LOD
• Introduction to Parameters
• Parameters Data Type Options

Module 7:- Geographical Visualization

• Introduction to Geographic Visualizations
• Assigning Geographical Locations
• Spatial Files
• Map Types
• Custom Geocoding
• Background Image

Module 8:- Advanced Charts in Tableau

• Introduction to Advanced Charts
• Bar in Bar Chart
• Bullet Chart
• Pareto Chart
• Gantt Chart
• Hierarchy and Tree Maps
• Box and Whisker’s Plot
• Waterfall Chart
• Step and Jump Lines
• Maps on a Scatter Plot
• Bubble Chart
• Control Chart
• Funnel Chart
• Packaged Bubbles
• Word Cloud
• Donut Chart
• Trendlines
• Reference Line, Bands, and Distributions

Module 9:- Dashboard & Stories

• Introduction to Dashboards
• The Dashboard Interface
• Important Dashboard Objects
• Adding Objects to the Dashboard
• Building a Dashboard
• Dashboard Design and Formatting
• Types of Actions
• Designing Dashboard for Tablets & Mobile-Phones
• Story Points
• Sharing Workbook
• Wrapping up Tableau Program

Module 1:- Getting Started with My SQL

• An Introduction and Overview of MySQL
• Installation and GUI Tools
• An Overview

Module 2:- My SQL Fundamentals

• Introducing SELECT statement
• Introducing WHERE clause
• Sort result with ORDER BY
• Using FROM to specify the source tables
• Importance of Clause Orders
• Data Modification tricks

Module 3:- Creating Database & Tables

• Creating a database
• Creating a table
• Creating Indexes
• Controlling column behavior with constraints
• Using foreign key constraints
• Creating an ID column
• Changing a schema with ALTER
• Introducing NULL and NOT_NULL
• Introduction to MySQL Data Types
• Setting up default values
• MySQL Warnings
• Alerting a table

Module 4:- My SQL Functions & Clause

• Introduction to MySQL Functions
• String Functions – CONCAT, SUBSTRING, REPLACE, REVERSE, LENGTH, UPPER, LCASE etc.
• Aggregate Functions – COUNT, MIN, MAX, SUM, AVG, ROUND etc
• Date/Time Functions – CURDATE, CURTIME, CURRENT_DATE, LOCALTIME etc.
• Control Flow Functions – IF, IFNULL, NULLIF etc.

Module 5:- Multiple Tables & Joins

• Introduction to JOINS
• Different types of JOINS
• JOINS and Aliases
• Multiple Table Joins
• Creating a simple Subselect
• Understanding of Primary keys and Foreign keys

Module 6:- Transactions, Stored Routines & Triggers

• Transactions & Stored Routines
• Triggers

Bonus Modules

• Creating a New User Login
• Granting access to new users
• Backup and Restore databases
• Important Keyboard Shortcuts Guide
• Session Study Material
• Situational Case Studies for Best Practice and Getting Ready for Corporate World
• 6 Months Live Support via Phone/Email/Messages

Module 1:- Getting Started With R

• Introduction To R
• Installation Setup
• A quick guide to RStudio User Interface
• RStudio’s GUI3
• Changing the appearance in RStudio
• Installing packages in R and using the library
• Development Environment Overview
• Introduction to R basics
• Building blocks of R
• Core programming principles
• Fundamentals of R

Module 2:- Programming with R

• Creating an object
• Data types in R
• Coercion rules in R
• Functions and arguments
• Conditional Statements and Loops
• if else, for, while, repeat, break, next

Module 3:- Objects in R

• Vectors and Vector operation
• List and Operations
• Factor and Operations
• Matrices
• Data Frame
• Applications of R objects
• Data Inputs and Outputs with R
• Advanced Visualization
• Using the script vs. using the console

Module 4:- Manipulating Data

• Data transformation with R
• Dplyr package
• Sampling data with the Dplyr package
• Select, filter, arrange, rename
• Mutate, pipeline
• Group by, summarize

Module 5:- Start Visualizing Data

• Intro To Data Visualization
• Introduction To Ggplot2
• Coloring, Filling Color, Axis, Legend, Labelling
• Histogram, Density
• Bar Chart, Point Plot
• Box And Whiskers Plot, Outliers
• Scatterplot
• Pie Chart

Module 6:- Projects & Assignments

• 2 Assignments
• 1 Projects
• Dataset Analysis