ChatGPT for Data Science and Machine Learning


Learn to build Data Science and Machine Learning Projects by Leveraging the Power of ChatGPT.

What you will learn

Learn about Fundamentals of Data Science and Machine Learning.

Learn to leverage the power of ChatGPT and add a powerful tool in your Tech Stack.

Learn about Matplotlib and Seaborn – Two important Data Visualization libraries in Python.

Build 3 complete Data Science and Machine Learning Projects in a qucik and efficient way by using concepts covered in the course and ChatGPT.

Description

WELCOME TO THE COURSE – ChatGPT for DATA SCIENCE AND MACHINE LEARNING

ChatGPT is an AI-powered conversational agent based on the GPT-3.5 architecture developed by OpenAI. As a language model, ChatGPT is capable of understanding and generating human-like responses to a wide variety of topics, making it a versatile tool for chatbot development, customer service, and content creation.Furthermore, ChatGPT is designed to be highly scalable and customizable, allowing developers to fine-tune its responses and integrate it into various applications and platforms. This flexibility makes ChatGPT a valuable asset for businesses seeking to enhance customer engagement and streamline their operations.


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By leveraging ChatGPT’s advanced natural language processing capabilities, data scientists can improve their workflows and achieve better results in their projects.

ChatGPT can be a useful tool for Programmers and Data Scientists in various ways.

  1. Code Generation: ChatGPT can generate code snippets based on natural language prompts, which can be useful for programmers who need to quickly prototype ideas or generate boilerplate code. By training ChatGPT on a corpus of code examples, programmers can create a language model that can generate syntactically correct code snippets for a variety of programming languages.
  2. Documentation Generation: ChatGPT can also be used to generate documentation for code. By training ChatGPT on a corpus of code comments and documentation, programmers can create a language model that can generate documentation for code snippets or entire codebases automatically.
  3. Code Optimization: ChatGPT can be used to optimize code by suggesting ways to simplify or optimize code snippets. By training ChatGPT on a corpus of optimized code examples, programmers can create a language model that can suggest improvements to existing code, which can help to reduce code complexity, improve performance, and increase maintainability.
  4. Error Handling: ChatGPT can also be used to improve error handling by suggesting solutions to common coding errors. By training ChatGPT on a corpus of code examples that contain errors and their solutions, programmers can create a language model that can suggest solutions to common coding errors automatically.

SO THIS IS ONE COMPLETE COURSE THAT WILL TEACH YOU ABOUT DATA SCIENCE AND MACHINE LEARNING AND HOW YOU CAN LEVERAGE THE POWER OF ChatGPT FOR A FASTER AND MORE EFFICIENT PROJECT DEVELOPMENT.

English
language

Content

Introduction

Introduction and Key Learning Outcomes

Machine Learning Fundamentals

Introduction to Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Machine Learning Life Cycle
Train Test Split
Regression Analysis
Linear Regression
Logistic Regression
KNN
SVM
Decision Tree
Random Forest
K – Means Clustering
GridSearch CV
Machine Learning Model Evaluation Metrics

Data Visualization

Introduction to Matplotlib
Different type of plots in Matplotlib
Seaborn

Introduction to ChatGPT

Introduction to ChatGPT
Introduction to ChatGPT Practical

Car Price Prediction

Understanding the Problem Statement
Coding Implementation

Wine Quality Prediction

Understanding the Problem Statement
Coding Implementation

Customer Segmentation using K-Means Clustering

Understanding the Problem Statement
Coding Implementation