1. Defining Python
2. History of Python and its Growing Popularity
   Features of Python and its Wide Functionality
3. Python 2 vs Python 3
4. Setting Up Python
5. Environment for Development
6. What and How of Python Installation?
7. IDEs: IDLE, Pycharm, and Jupyter
8. Writing First Python Program
9. Python Scripts on UNIX and Windows
10. Installation on Ubuntu-based Machines
11. Programming on Interactive Shell
12. Python Identifiers and Keywords
13. Indentation in Python
14. Comments and Writing to the Screen
15. Command Line Arguments and Flow Control
16. User Input
17. Python Core Objects
18. Defining Built-in Functions
19. Objectives
20. Variables and their types
21. Variables – String Variables
22. Variables – Numeric Types
23. Variables – Boolean Variables
24. Boolean Object and None Object
25. Tuple Object and Operations
26. Dictionary Object and Operations
27. Types of Variables – Dictionary
28. Comparison of Variables
29. Dictionary Methods and Manipulations
30. Operators and Logical Operators
31. Data Structures and Data Processing
32. Arithmetic Operations on Numeric Values
33. Operators and Keywords for Sequences
34. Understanding Conditional Statements
35. Break Statements and Continue Statements
36. Using Indentations for defining if & else block
37. Loops in Python
38. While, Nested, Demo-Create
39. How to Control Loops?
40. Sequence and Iterable Objects

1. Objectives of Functions
2. Types of Functions
3. Creating UDF Functions
4. Function Parameters
5. Unnamed and Named Parameters
6. Creating and Calling Functions
7. Python user Defined Functions
8. Python packages Functions
9. Anonymous Lambda Function
10. Understanding String Object Functions
11. List and Tuple Object Functions
12. Studying Dictionary Object Functions
13. Defining Python Inbuilt Modules
14. Studying Types of Modules
15. os, sys, time, random, datetime, zip modules
16. How to Create Python User Defined Modules?
17. Understanding Pythonpath
18. Creating Python Packages
19. init File and Package Initialization
20. What and How of File Handling with Python?
21. How to Process Text Files using Python?
22. Read/Write and Append File Object
23. Test Operations: os.path
24. Overview of Object Oriented Programming
25. Defining Classes, Objects, and Initializers
26. Attributes – Built-In Class
27. Destroying Objects
28. Methods – Instance, Class, Static, Private methods, and Inheritance
29. Data Hiding
30. Module Aliases and reloading modules
31. Python Exceptions Handling
32. Standard Exception Hierarchy
33. .. except…else
34. .. finally…clause
35. Creating Self-Exception Class
36. User-defined Exceptions
37. Debugging Errors – Unit Tests
38. Project Skeleton
39. Creating and Using the Skeleton
40. How to use pdb debugger?
41. Using Pycharm Debugger
42. Asserting Statement for Debugging
43. Using UnitTest Framework for Testing
44. Understanding Regular Expressions
45. Match Function, Search Function, and the Comparision
46. Compile and Match, Match and Search
47. Search and Replace
48. What and How of Extended Regular Expressions?
49. Wildcard Characters

1. Objectives of Functions
2. Types of Functions
3. Creating UDF Functions
4. Function Parameters
5. Unnamed and Named Parameters
6. Creating and Calling Functions
7. Python user Defined Functions
8. Python packages Functions
9. Anonymous Lambda Function
10. Understanding String Object Functions
11. List and Tuple Object Functions
12. Studying Dictionary Object Functions
13. Defining Python Inbuilt Modules
14. Studying Types of Modules
15. os, sys, time, random, datetime, zip modules
16. How to Create Python User Defined Modules?
17. Understanding Pythonpath
18. Creating Python Packages
19. init File and Package Initialization
20. What and How of File Handling with Python?
21. How to Process Text Files using Python?
22. Read/Write and Append File Object
23. Test Operations: os.path
24. Overview of Object Oriented Programming
25. Defining Classes, Objects, and Initializers
26. Attributes – Built-In Class
27. Destroying Objects
28. Methods – Instance, Class, Static, Private methods, and Inheritance
29. Data Hiding
30. Module Aliases and reloading modules
31. Python Exceptions Handling
32. Standard Exception Hierarchy
33. .. except…else
34. .. finally…clause
35. Creating Self-Exception Class
36. User-defined Exceptions
37. Debugging Errors – Unit Tests
38. Project Skeleton
39. Creating and Using the Skeleton
40. How to use pdb debugger?
41. Using Pycharm Debugger
42. Asserting Statement for Debugging
43. Using UnitTest Framework for Testing
44. Understanding Regular Expressions
45. Match Function, Search Function, and the Comparision
46. Compile and Match, Match and Search
47. Search and Replace
48. What and How of Extended Regular Expressions?
49. Wildcard Characters

1. Data Visualization and Matplotlib, seaborn
2. Python Libraries
3. Features of Matplotlib
4. Line Properties Plot with (x, y)
5. Set Axis, Labels, and Legend Properties
6. Alpha and Annotation
7. Univariate plots
8. Bivariate plots
9. Multivariate plots
10. Interpretations

• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations(filtering, sorting, grouping, aggregation, merging) and Data Standardization
• Pandas: File Read and Write Support
• SQL Operations(pandasql)

1. Creating Database, using Database
2. Creating Tables, inserting Values in the Table
3. Select Query where clauses
4. And, Or, Not Operators
5. In Operators, Not In Operators
6. Between & Not Between Operators
7. Like Operators orders distinct Command
8. Distinct Clauses
9. Limit Clauses with where Clauses

1. Offset Arithmetic expression (SUM, AVG, MAX, MIN, COUNT) Update Functions
2. Inner, Left, Right, Outer, Fuller, Cross Join
3. Group By, Having clauses, Sub Query
4. Advanced Sub query using with Functions
5. Union And Union All
6. Advanced Union & Union all with Functions
7. Advanced Union & Union all using Where Clauses
8. Advanced Union & Union all using Joins
9. If And Case Statements
10. STRING FN & manipulation of STRINGS VALUE using String fn
11. Date Function
12. Date Format Functions
13. Delete And Truncate
14. Alter
15. Creating View Advanced concept
16. Procedure.

1. Power BI Introduction
2. Introduction to Power BI
3. Desktop, Getting Data (Excel and RDBMS, Web, SharePoint), Naming for Q&A,
4. Direct Query vs Import Data
5. Introduction to Modelling
6. Set Up and Manager
7. Relationships Cardinality and Cross Filtering
8. Creating Hierarchy in the Model
9. Introduction to Modelling
10. Default Summarization and Sort by Creating Calculated Columns
11. Creating Measures & Quick Measures
12. Creating Visuals Colour and Conditional Formatting
13. Setting Sort Order
14. Scatter and Bubble
15. Charts and Play Axis
16. Tool Tips, Slicers
17. Timeline Slicers and Sync Slicers
18. Cross Filtering and Highlighting
19. Creating Visuals Visual
20. Page & Report Level Filters
21. Drill Down/Up Hierarchies
22. Constant Lines Tables, Matrix and Table
23. Conditional Formatting KPI’s, Cards and Gauges
24. Map
25. Visualizations
26. Custom Visuals
27. Creating Visuals
28. Managing and Arranging
29. Drill Through
30. Custom Report Themes
31. Grouping and Binning
32. Bookmark & Buttons
33. DAX Expressions
34. Introduction to Modelling
35. Set Up and Manager
36. Relationships Cardinality and Cross Filtering
37. Creating Hierarchy in the Model
38. Introduction to Modelling
39. Default Summarization and Sort by
40. Creating Calculated Columns
41. Creating Measures & Quick Measures
42. Publishing and Sharing
43. Sharing Options
44. Publish from Power BI
45. Desktop Publish Reports to Web
46. Sharing Reports and Dashboards Workspaces
47. Apps. Sharing Options
48. Printing PDF’s and
49. Exports Row Level Security
50. Exporting Data from Visualization Refreshing
51. Datasets Understanding
52. Data Refresh Gateways

1. Excel and basic formula
2. Introduction to excel and its interface
3. Entering and editing data into cells & autofill
4. Basic Formating using shortcuts keys
5. Basic DAX – sum, Average,Count,
6. Sorting and filtering the data with accending & Decending Inserting
7. deleting and renaming ther worksheet
8. Ranges selection using range name for easier references
9. Conditional Formating

1. Logical Dax: If statements nested ifs, And & or operators
2. Lookup Functions: Vlookup Dax & Hlookup
3. AX for searching and retreiving data
4. Text DAX: Concatenation, LEFT, RIGHT,MID DATE & Time Function: Working with Date
using DAX
5. Introduction Pivot Creating PIVOT to summarise and analyse large dataset
6. Pivot table filtering and formating
7. Calculated fields in pivot table pivot charts Creating
8. Charts in excel
9. Chart formatting chart type and customization
10. Data analysis using chart Data
11. Validation:Setting data validation rules
12. Scenario Manager:Creating and managing scenarios
13. Text to columns: Splitting text into multiple columns using delimeters
14. Removing Duplicates Data
15. Consolidation Text
16. Functions for data using Trim , proper, upper, lower
17. What if Analysis:using tables for scenarios
18. Power Query: Importing transforming bigdata
19. Macros:Recording and running macros to automate task
20. Customizing Ribbon: Modifying the ribbon
21. Introduction to VBA
22. Enablind Developer tab
23. VBA Editor : Opening and Navigating

• Regression Methods for Forecasting
• Numeric Data
  Understanding Neural Networks
• From Biological to Artificial Neurons
• Activation Functions
• Deep Learning – Neural Networks and
  Support Vector Machines
• What is Regression?
• Model Selection
• Generalized Regression
• Simple Linear
• Regression
• Correlation between X and Y
• Multiple Linear Regression
• Interaction Regres-
  sion Model
  Network Topology
• The Number of Layers
• Decision
• Trees and Data Mining

• Understanding K-means Clustering
• K-means and PseudoCode
• K-means Clustering using R
• TF-IDF and Cosine Similarity
• Application to VectorSpace Model
• Hierarchical Clustering Algorithm
• Understanding Agglomerative Clustering
• DBSCAN Clustering
• Association Rule Strength Measures
• Ordering Items
• Dimensionality reduction

• Introduction to Artificial Intelligence
• History of Arificial Intelligence
• Future and Market Trends in Artificial Intelligence
• Neural Network and Perceptron
• Understanding Feedforward
• Networks Exploring
• Backpropagation
• Understanding Sensor Processing
• Studying Neural Elements

• Natural LanguageProcessing in Python 
• Studying Deep Learning 
• Artificial Neural Networks
• ANN Intuition
• Plan of Attack 
• Studying the Neuron
• The Activation Function
• Exploring Gradient Descent

Session 1: Introduction to ChatGPT

• Overview of natural language processing (NLP) and chatbots
• Introduction to ChatGPT and its capabilities
• Setting up the Python environment for ChatGPT
• Basic usage of OpenAI’s API for ChatGPT
• Session 2: Working with OpenAI API
• Understanding the OpenAI API key and authentication
• Exploring API endpoints for ChatGPT
• Sending requests to the API and handling responses
• Limitations and usage guidelines for the API
• Session 3: Text Generation Basics
• Basic text generation using ChatGPT
• Input formats and techniques for generating coherent responses
• Handling different prompt styles for varied outputs
• Experimenting with temperature and max tokens foroutput control
• Session 4: Advanced Text Generation
• Fine-tuning prompts for specific tasks or contexts
• Customizing responses with user-provided context
• Controlling verbosity and length of generated text
• Handling multiple turns in a conversation
• Session 5: Error Handling and Model Behavior
• Understanding and handling model biases
• Dealing with inappropriate or biased outputs
• Implementing error handling for API requests
• Managing user interactions and providing feedback
• Session 6: Integrating ChatGPT into Applications
• Building asimple chatbot application with ChatGPT
• Integrating ChatGPT into web applications or scripts
• Handling user input and managing conversations
• Exploring real-world use cases for ChatGPT
• Session 7: Optimization and Performance
• Strategies for optimizing API usage and cost
• Caching and storing responses for efficiency
• Implementing rate limiting and error handling
• Discussing best practices for production use
• Session 8: Project and Recap
• Developing a small project using ChatGPT
• Presenting and discussing projects within the group
• Recap of key concepts and best practices
• Q&A session and exploring further resources