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. 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

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:

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