Python Mastery With Generative Ai: Coding To Ai Integration

Learn Python, AI Applications, Data Analysis & Code Optimization

What you'll learn

Master Python syntax and basic programming constructs.

Utilize AI tools like ChatGPT and GitHub Copilot for code enhancement.

Optimize and refactor Python code using AI technologies.

Implement advanced error debugging and code review techniques.

Develop skills in asynchronous programming and threading.

Apply design patterns and best coding practices in Python.

Enhance data manipulation skills using Pandas and visualization libraries.

Explore object-oriented programming and dynamic attributes.

Build AI-driven Python applications for real-world scenarios.

Gain proficiency in deep learning and NLP with Python frameworks.

Requirements

Basic understanding of programming concepts.

Access to a computer with internet connectivity.

Installation of Python and relevant development tools.

Eagerness to learn about AI's role in programming.

No prior experience required; just a passion for technology!

Access to a computer and internet for software installation.

Description

Dive into the world of programming with our comprehensive course "Python Mastery with Generative AI: Coding to AI Integration". This course is meticulously designed for both beginners and experienced developers who aspire to master Python while integrating cutting-edge Generative AI technologies. Starting with the basics of Python programming, you'll quickly progress to understanding how AI tools like ChatGPT, Bard, and GitHub Copilot can elevate your coding skills.Explore advanced programming topics such as lambda functions, multiprocessing, and asynchronous programming tailored with the aid of AI. Delve into data manipulation using Pandas, create visually appealing data visualizations, and optimize data performance. Learn the principles of object-oriented programming enhanced by AI insights, and harness Python's vast library ecosystem for tasks ranging from web development to machine learning.The course also prepares you for real-world applications by teaching you how to implement AI in data analysis and business intelligence, culminating in building a practical To-Do List app. By joining, you'll gain access to a plethora of resources for continuing education, participate in Python open source projects, and stay updated with the latest trends and best practices in Python programming. Prepare to transform your coding skills and embrace the future of Python and AI with us!

Overview

Section 1: Basic Python Programming Concepts and Generative AI

Lecture 1 Introduction

Lecture 2 What you Should know before Starting Python?

Lecture 3 Understanding of Python and installations

Lecture 4 Basics of Python

Lecture 5 How is AI beneficial in programming?

Lecture 6 Generative AI (Chatgpt, Bard, Copilot, Git&Github copilot)

Section 2: Leveraging Generative AI for Enhancing Python Skills

Lecture 7 Advanced Use of AI Code Completion Tools for Python

Lecture 8 Integrating AI for Code Optimization and Refactoring

Lecture 9 Utilizing AI for Error Debugging

Lecture 10 AI-Powered Code Review and Quality Analysis

Section 3: Efficient Development Environment Setup

Lecture 11 Advanced Python Environment Management

Lecture 12 Profiling and Optimizing Python Code with Generative AI

Lecture 13 Debugging Techniques

Section 4: Intermediate to Advanced Syntax and Concepts (Using chatGPT-4 and Gemini)

Lecture 14 Lambda Functions and Functional Programming Techniques

Lecture 15 Threading, Multiprocessing, and Asynchronous Programming

Lecture 16 Advanced Error and Exception Handling Techniques

Lecture 17 Python Memory Management and Optimization

Section 5: Design Patterns and Best Practices

Lecture 18 Implementing Design Patterns in Python

Lecture 19 Writing Pythonic Code: Best Practices and Conventions using ChatGPT

Lecture 20 Unit Testing and Test-Driven Development (TDD) in Python with Gemini

Section 6: Data Manipulation and Analysis Generative AI

Lecture 21 Advanced Data Manipulation with Pandas

Lecture 22 Data Visualization Techniques

Lecture 23 Working with Data Sets and Performance Tuning

Section 7: Advanced Object-Oriented Programming in Python

Lecture 24 Design Principles with Copilot

Lecture 25 Advanced Techniques in OOP

Lecture 26 Working with Dynamic Attributes and Methods

Section 8: Utilizing Python Libraries and Frameworks for Specialized Tasks

Lecture 27 Data Science and Machine Learning

Lecture 28 Network Programming

Lecture 29 Advanced Web Development

Section 9: AI and Deep Learning with Python

Lecture 30 Deep Learning Concepts and Frameworks

Lecture 31 Natural Language Processing (NLP) with Python

Section 10: 10 Project

Lecture 32 Building a Todo List App with Flask: Leveraging Python and JavaScript for an Enh

Section 11: Practical AI Applications for Python Developers

Lecture 33 Case Studies: Real-World AI Solutions with Python

Lecture 34 AI in Data Analysis and Business Intelligence

Section 12: Advanced Resources and Continuing Education

Lecture 35 Participating in Python Open Source Projects

Lecture 36 Advanced Python Books and Online Resources

Lecture 37 Joining Python Developer Communities and Conferences

Section 13: Conclusion: Mastering Python

Lecture 38 The Evolving Landscape of Python Programming

Lecture 39 . Future Trends in Python and AI

Lecture 40 Conclusion

Aspiring Developers: Beginners who want to start a career in programming with Python.,Experienced Programmers: Those looking to enhance their skills with AI integrations.,Data Analysts: Professionals aiming to leverage Python for data manipulation and visualization.,Tech Enthusiasts: Individuals curious about the intersection of AI and software development.,Project Managers: Managers seeking to understand the technical aspects of Python projects.,Students: College or university students studying computer science or related fields.