ChatGPT for Data Science and Machine Learning

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ChatGPT for Data Science and Machine Learning [TutsNode.net] - ChatGPT for Data Science and Machine Learning 5. Car Price Prediction
  • 2. Coding Implementation.mp4 (137.4 MB)
  • 2.1 car data.csv (16.8 KB)
  • 1.1 Car Price Prediction.pptx (456.2 KB)
  • 2.2 Car Price Prediction.ipynb (41.9 KB)
  • 1. Understanding the Problem Statement.mp4 (9.1 MB)
6. Wine Quality Prediction
  • 2.1 Wine Quality Prediction.ipynb (90.3 KB)
  • 2. Coding Implementation.mp4 (93.4 MB)
  • 2.2 winequality-red.csv (98.6 KB)
  • 1.1 Wine Quality Prediction.pptx (480.5 KB)
  • 1. Understanding the Problem Statement.mp4 (14.3 MB)
7. Customer Segmentation using K-Means Clustering
  • 2.2 Mall_Customers.csv (3.9 KB)
  • 2. Coding Implementation.mp4 (116.6 MB)
  • 2.1 Customer Segmentation.ipynb (60.3 KB)
  • 1. Understanding the Problem Statement.mp4 (13.8 MB)
  • 1.1 Customer Segmentation using K-means Clustering.pptx (521.4 KB)
2. Machine Learning Fundamentals
  • 8.1 Logistic Regression.pptx (405.2 KB)
  • 8.2 User_Data.csv (10.7 KB)
  • 13.2 Mall_Customers.csv (4.7 KB)
  • 7.2 Salary_Data.csv (0.4 KB)
  • 9. KNN.mp4 (79.0 MB)
  • 8. Logistic Regression.mp4 (74.8 MB)
  • 9.2 User_Data.csv (10.7 KB)
  • 10.2 User_Data.csv (10.7 KB)
  • 11.2 User_Data.csv (10.7 KB)
  • 12.2 User_Data.csv (10.7 KB)
  • 14. GridSearch CV.mp4 (74.0 MB)
  • 11. Decision Tree.mp4 (73.8 MB)
  • 13. K - Means Clustering.mp4 (66.1 MB)
  • 5.1 Train Test Split.pptx (443.0 KB)
  • 10. SVM.mp4 (63.7 MB)
  • 6. Regression Analysis.mp4 (55.4 MB)
  • 7. Linear Regression.mp4 (52.2 MB)
  • 12. Random Forest.mp4 (48.4 MB)
  • 15. Machine Learning Model Evaluation Metrics.mp4 (45.1 MB)
  • 12.1 Random Forest Algorithm.pptx (400.8 KB)
  • 4. Machine Learning Life Cycle.mp4 (36.8 MB)
  • 1. Introduction to Machine Learning.mp4 (36.7 MB)
  • 2. Supervised Machine Learning.mp4 (27.4 MB)
  • 3. Unsupervised Machine Learning.mp4 (22.6 MB)
  • 3.1 Unsupervised Machine Learning.pptx (400.2 KB)
  • 2.1 Supervised Machine Learning.pptx (364.9 KB)
  • 5. Train Test Split.mp4 (14.6 MB)
  • 7.1 Linear Regression.pptx (360.1 KB)
  • 13.1 K-Means Clustering Algorithm.pptx (588.5 KB)
  • 10.1 Support Vector Machine (SVM).pptx (554.8 KB)
  • 6.1 Regression Analysis.pptx (541.0 KB)
  • 11.1 Decision Tree Algorithm.pptx (463.4 KB)
  • 9.1 K Nearest Neighbors(KNN).pptx (519.8 KB)
  • 14.1 GridSearchCV.pptx (487.2 KB)
  • 1.1 Machine Learning Introduction.pptx (506.7 KB)
3. Data Visualization
  • 3. Seaborn.mp4 (63.9 MB)
  • 2. Different type of plots in Matplotlib.mp4 (47.2 MB)
  • 1. Introduction to Matplotlib.mp4 (29.5 MB)
4. Introduction to ChatGPT
  • 1. Introduction to ChatGPT.mp4 (27.4 MB)
  • 2. Introduction to ChatGPT Practical.mp4 (18.1 MB)
  • 1.1 Introduction to ChatGPT.pptx (1.2 MB)
1. Introduction
  • 1.1 Introduction and key learning outcomes.pptx (372.5 KB)
  • 1. Introduction and Key Learning Outcomes.mp4 (13.5 MB)
  • TutsNode.net.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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Description


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.

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.

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.
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.
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.
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.
Who this course is for:

Anybody with an interest in Data Science and Machine Learning.
Anybody who wants to learn to develop Data Science and Machine Learning Projects in a quick and efficient manner by leveraging the power of ChatGPT .
Python developers who are curious about Data Science, Machine Learning and ChatGPT.

Requirements

Basic understanding of python programming language.

Last Updated 3/2023



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ChatGPT for Data Science and Machine Learning

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1.3 GB
seeders:46
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ChatGPT for Data Science and Machine Learning

Torrent hash: ADFE3C00A547B7809F93C40E8A4ED4859FC63815