Python Training & Certification

1. What is an Application?

2. Types of Applications

3. Web Application Fundamentals

4. Web Technologies: (List key technologies and their roles)

• Frontend: HTML, CSS, JavaScript, React
• Backend: Python, Java, Node.js
• Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

5. Software Development Life Cycle (SDLC)

• Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

6. Application Development Methodologies

• Agile: Core principles, Scrum, Kanban
• Waterfall

1. What is Data

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

1. The Importance of Computing Power

2. Key Computing Technologies:

• CPU (Central Processing Unit)
• GPU (Graphics Processing Unit)

3. Cloud Computing:

• What is the Cloud?
• Cloud Service Models:
• IaaS (Infrastructure as a Service)
• PaaS (Platform as a Service)
• SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)?

2. How AI Works?

3. Key Concepts:

• Machine Learning (ML)
• Deep Learning (DL)

4. Generative AI:

• What is Generative AI?
• Examples: Large Language Models (LLMs), image generation models.

5. AI in Everyday Learning

1. Customer Relationship Management (CRM)

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

1. Python’s applicability across various domains

2. Installation, environment setup, and path configuration

3. Writing and executing the first Python script

1. Keywords, Identifiers, and basic syntax

2. Variables, Data Types, and Operators

3. Introduction to Input/Output operations

1. Conditional Statements: If, Elif, Else

2. Loops: For, While, and control flow mechanisms

3. Understanding and defining Functions in Python

1. String operations and manipulations

2. Lists and their operations

3. Introduction to Tuples and Sets

1. Detailed exploration of Dictionaries

2. Frozen Sets and their use-cases

3. Advanced list comprehensions

1. Advanced methods in Lists, Tuples, and Dictionaries

2. Sets, Frozen Sets, and operations

3. Comprehensive look into Python Collections

1. Exploring types of Functions and Arguments

2. Lambda functions and their applications

3. Map, Reduce, and Filter functions

1. File operations and handling different file formats

2. Working with Excel and CSV files in Python

3. Understanding and using Python Modules and Packages

1. Deep dive into Classes, Objects, and Methods

2. Constructors, Destructors, and Types of Methods

3. Inheritance, Polymorphism, and Encapsulation

1. Exception Handling: Try, Except, Finally

2. Creating and using Custom Exceptions

3. Utilizing Regular Expressions for pattern matching

1. Introduction to Django and its features

2. Setting up a Django project and understanding its structure

3. MVC Model, creating views, and URL mapping

1. Database models and migrations

2. Admin interface and deploying Django applications

3. Forms and handling user inputs

1. Advanced URL routing and views

2. Class-based views and middleware

3. Working with static and media files

1. Building RESTful APIs with Django REST Framework

2. Serializers and request handling

3. Authentication and permissions in APIs

1. Writing tests for Django applications

2. Deployment strategies and best practices

3. Configuring Django applications for production

1. Introduction to Data Science with Python

2. Data manipulation with Pandas

3. Data visualization with Matplotlib and Seaborn

• Basic concepts of application deployment on the cloud, including containerization basics with Docker and initial orchestration concepts.

1. Advanced Pandas techniques and operations

2. Time Series data analysis with Pandas

3. Combining, merging, and reshaping data frames

1. Advanced visualization with Matplotlib

2. Interactive visualizations with Plotly

3. Geospatial data visualization

1. Basics of machine learning with Python

2. Using Scikit-learn for machine learning models

3. Model evaluation and validation techniques

1. Introduction to Neural Networks and Deep Learning

2. Working with text data and Natural Language Processing (NLP)

3. Introduction to Big Data technologies with Python

Topics:

1. Cloud Computing Basics

• Understanding cloud computing: Definitions, service models (IaaS, PaaS, SaaS), and deployment models (public, private, hybrid, multicloud).

2. Cloud Service Providers Overview

• Introduction to major cloud platforms (e.g., AWS, Azure, Google Cloud), focusing on their core services relevant to developers.

3. Cloud-based Development Environments

• Setting up and utilizing cloud-based IDEs and development tools to streamline development workflows.

4. Deploying Applications on the Cloud

• Basic concepts of application deployment on the cloud, including containerization basics with Docker and initial orchestration concepts.

Topics:

1. Understanding DevOps

• The philosophy, practices, and benefits of DevOps in modern software development, emphasizing collaboration, automation, and integration.

2. Version Control with Git

• Deep dive into using Git for source code management, including best practices for branches, commits, merges, and pull requests.

3. Continuous Integration/Continuous Deployment (CI/CD)

• Introduction to CI/CD pipelines, overview of tools ( GitHub Actions), and setting up basic pipelines for automated testing and deployment.

4. Monitoring and Feedback

• Basics of application monitoring, log management, and utilizing feedback for continuous improvement.

Topics:

1. Containers and Docker

• Introduction to containers, Docker fundamentals, creating Docker images, and container management basics.

2. Managing Application Infrastructure

• Basic strategies for managing infrastructure as part of the application lifecycle, including introduction to infrastructure as code (IaC) principles.

Topics:

1. Scalable Application Design

• Principles of designing scalable applications that can grow with user demand, focusing on microservices architecture and stateless application design.

2. Cloud-native Services for Developers

• Leveraging cloud-native services (e.g., AWS Lambda, Azure Functions, Google Cloud Run) for building and deploying applications.

3. Databases in the Cloud

• Overview of cloud database services (SQL and NoSQL) and integrating them into web applications.

4. Security Basics in Cloud and DevOps

• Understanding security best practices in cloud environments and throughout the DevOps pipeline.

Topics:

1. Agile and Scrum Methodologies

• Incorporating Agile and Scrum practices into team collaboration for efficient project management.

2. Code Review and Collaboration Tools

• Utilizing code review processes and collaboration tools GitHub, to enhance code quality and team productivity.

3. Automation in Development

• Exploring automation beyond CI/CD, including automated testing frameworks, database migrations, and environment setup.

4. DevOps Culture and Best Practices

• Cultivating a DevOps culture within teams, embracing continuous learning, and adopting industry best practices for sustainable development.

Introduction to Generative AI

1. What is Generative AI?

2. Key Applications:

• Text (ChatGPT, Claude, LLaMA)
• Images (DALL·E, MidJourney, Stable Diffusion)
• Audio (Music Generation, Voice Cloning)
• Code (GitHub Copilot, Cursor)

3. Evolution of GenAI:

•  Rule-Based → Deep Learning → Transformers
• GANs vs. VAEs vs. LLMs

1. Effective Prompt Design

• Instruction-Based, Few-Shot, Zero-Shot

2. Advanced Techniques:

• Chain-of-Thought (CoT) Prompting
• Self-Consistency & Iterative Refinement
• Hands-on:
• Optimizing prompts for GPT-4, Claude, LLaMA

Transformer Architecture

1. Why Transformers? (Limitations of RNNs/LSTMs)

2. Key Components:

• Self-Attention & Multi-Head Attention
• Encoder-Decoder (BERT vs. GPT)

3. Evolution: BERT → GPT → T5 → Mixture of Experts

4. Large Language Models (LLMs)

5. Pre-training vs. Fine-tuning

6. Popular Architectures:

• GPT-4, Claude, Gemini, LLaMA 3
• BERT (Encoder-based) vs. T5 (Text-to-Text)

Introduction to AI Agents

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

• CrewAI + n8n: Automating Business Workflows
• Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

• Case Studies:
• AI Customer Support Agents