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Master Python using ChatGPT
Size :2.0 GB
Peers : Seeders : 0      Leechers : 0
Added : 1 year ago » by tutsnode » in Other
Language : English
Last Updated :6 months ago
Info_Hash :22D37731C23B91059C37C5306143E6FE8BEE1AE0

Torrent File Contents

Master Python using ChatGPT
  [TutsNode.net] - 8. Projects/1. Car Price Prediction.mp4
  -  142.94 MB

  TutsNode.net.txt
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  [TutsNode.net] - 7. Machine Learning/8.2 KNN Practical.ipynb
  -  60.8 KB

  [TutsNode.net] - 3. Numpy/5.1 Array Iterating Practical.ipynb
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  .pad/0
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  [TutsNode.net] - 8. Projects/2. Customer Segmentation.mp4
  -  119.9 MB

  [TutsNode.net] - 8. Projects/3.1 Wine Quality Prediction.ipynb
  -  90.3 KB

  [TutsNode.net] - 7. Machine Learning/11.2 Random Forest Practical.ipynb
  -  6.66 KB

  [TGx]Downloaded from torrentgalaxy.buzz .txt
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  [TutsNode.net] - 7. Machine Learning/6.3 Salary_Data.csv
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  .pad/1
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  [TutsNode.net] - 8. Projects/3. Wine Quality Prediction.mp4
  -  95.46 MB

  [TutsNode.net] - 3. Numpy/1.1 Numpy Introduction and Installation.ipynb
  -  1.65 KB

  [TutsNode.net] - 5. Data Visualization/2.1 Different types of plots in Matplotlib.ipynb
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  [TutsNode.net] - 7. Machine Learning/10.2 Decision Tree Practical.ipynb
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  .pad/2
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  [TutsNode.net] - 7. Machine Learning/8. KNN.mp4
  -  79.02 MB

  [TutsNode.net] - 7. Machine Learning/13.2 GridSearchCV.pptx
  -  487.17 KB

  [TutsNode.net] - 4. Pandas/1.1 Pandas Intro and Installation.ipynb
  -  1.97 KB

  .pad/3
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  [TutsNode.net] - 7. Machine Learning/7. Logistic Regression.mp4
  -  74.71 MB

  [TutsNode.net] - 6. Data Preprocessing/3.1 Feature Scaling.ipynb
  -  117.33 KB

  [TutsNode.net] - 8. Projects/3.3 winequality-red.csv
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  [TutsNode.net] - 5. Data Visualization/3.1 Seaborn.ipynb
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  [TutsNode.net] - 7. Machine Learning/7.3 User_Data.csv
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  [TutsNode.net] - 4. Pandas/2.1 Pandas Series.ipynb
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  .pad/4
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  [TutsNode.net] - 7. Machine Learning/13. Grid Search CV.mp4
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  [TutsNode.net] - 8. Projects/3.2 Wine Quality Prediction.pptx
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  .pad/5
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  [TutsNode.net] - 7. Machine Learning/10. Decision Tree.mp4
  -  73.76 MB

  [TutsNode.net] - 7. Machine Learning/12.1 K Means Practical.ipynb
  -  60.72 KB

  [TutsNode.net] - 8. Projects/2.2 Customer Segmentation.ipynb
  -  60.3 KB

  [TutsNode.net] - 4. Pandas/5.2 Analyzing DataFrames.ipynb
  -  60.11 KB

  [TutsNode.net] - 6. Data Preprocessing/2.1 airport.csv
  -  47.24 KB

  [TutsNode.net] - 7. Machine Learning/8.3 User_Data.csv
  -  10.67 KB

  [TutsNode.net] - 4. Pandas/3.1 Pandas DataFrame Practical.ipynb
  -  3.19 KB

  .pad/6
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  [TutsNode.net] - 6. Data Preprocessing/2. Feature Encoding.mp4
  -  67.7 MB

  [TutsNode.net] - 4. Pandas/5.1 airport.csv
  -  47.24 KB

  [TutsNode.net] - 4. Pandas/4.1 airport.csv
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  [TutsNode.net] - 8. Projects/1.2 Car Price Prediction.ipynb
  -  41.92 KB

  [TutsNode.net] - 6. Data Preprocessing/2.2 Feature Encoding.ipynb
  -  36.83 KB

  [TutsNode.net] - 7. Machine Learning/7.1 Logistic Regression Practical.ipynb
  -  25.33 KB

  [TutsNode.net] - 7. Machine Learning/6.1 Linear Regression.ipynb
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  [TutsNode.net] - 4. Pandas/4.2 Read CSV.ipynb
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  [TutsNode.net] - 6. Data Preprocessing/1.3 Placement_Data_Full_Class.csv
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  [TutsNode.net] - 8. Projects/1.1 car data.csv
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  [TutsNode.net] - 5. Data Visualization/1.1 Matplotlib Intro and Getting started.ipynb
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  [TutsNode.net] - 6. Data Preprocessing/1.2 Missing Values.ipynb
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  [TutsNode.net] - 7. Machine Learning/13.1 GridSearch CV.ipynb
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  .pad/7
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  [TutsNode.net] - 7. Machine Learning/12. K Means Clustering.mp4
  -  66.02 MB

  [TutsNode.net] - 7. Machine Learning/10.1 Decision Tree Algorithm.pptx
  -  463.37 KB

  [TutsNode.net] - 7. Machine Learning/9.3 User_Data.csv
  -  10.67 KB

  [TutsNode.net] - 7. Machine Learning/10.3 User_Data.csv
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  [TutsNode.net] - 6. Data Preprocessing/2.4 Iris.csv
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  .pad/8
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  [TutsNode.net] - 6. Data Preprocessing/1. Handling Missing Values.mp4
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  [TutsNode.net] - 8. Projects/1.3 Car Price Prediction.pptx
  -  456.16 KB

  [TutsNode.net] - 7. Machine Learning/11.3 User_Data.csv
  -  10.67 KB

  .pad/9
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  [TutsNode.net] - 5. Data Visualization/3. Seaborn.mp4
  -  63.92 MB

  [TutsNode.net] - 7. Machine Learning/14.2 ML Pipeline.ipynb
  -  10.54 KB

  [TutsNode.net] - 7. Machine Learning/9.2 SVM Practical.ipynb
  -  8.68 KB

  [TutsNode.net] - 7. Machine Learning/12.3 Mall_Customers.csv
  -  4.67 KB

  [TutsNode.net] - 3. Numpy/2.1 Creating Arrays Numpy.ipynb
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  [TutsNode.net] - 3. Numpy/6.1 Array Slicing.ipynb
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  [TutsNode.net] - 3. Numpy/4.1 Array Indexing.ipynb
  -  3.96 KB

  [TutsNode.net] - 3. Numpy/7.1 Searching and Sorting numpy array prac.ipynb
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  [TutsNode.net] - 8. Projects/2.3 Mall_Customers.csv
  -  3.89 KB

  .pad/10
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  [TutsNode.net] - 7. Machine Learning/9. SVM.mp4
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  .pad/11
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  [TutsNode.net] - 6. Data Preprocessing/3. Feature Scaling.mp4
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  .pad/12
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  [TutsNode.net] - 7. Machine Learning/14. Machine Learning Pipeline.mp4
  -  56.82 MB

  .pad/13
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  [TutsNode.net] - 7. Machine Learning/5. Regression Analysis.mp4
  -  55.43 MB

  .pad/14
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  [TutsNode.net] - 3. Numpy/2. Create a Numpy Array.mp4
  -  52.99 MB

  .pad/15
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  [TutsNode.net] - 7. Machine Learning/6. Linear Regression.mp4
  -  52.24 MB

  .pad/16
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  [TutsNode.net] - 3. Numpy/3. Shape and Reshape.mp4
  -  50.07 MB

  [TutsNode.net] - 7. Machine Learning/14.1 Machine learning Pipeline.pptx
  -  415.53 KB

  .pad/17
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  [TutsNode.net] - 4. Pandas/5. Analyze Pandas DataFrames.mp4
  -  48.42 MB

  .pad/18
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  [TutsNode.net] - 7. Machine Learning/11. Random Forest.mp4
  -  48.35 MB

  .pad/19
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  [TutsNode.net] - 5. Data Visualization/2. Type of Plots in Matplotlib.mp4
  -  47.18 MB

  .pad/20
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  [TutsNode.net] - 3. Numpy/6. Slicing.mp4
  -  43.16 MB

  .pad/21
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  [TutsNode.net] - 2. Python/1. Data Types and Variables.mp4
  -  38.23 MB

  .pad/22
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  [TutsNode.net] - 7. Machine Learning/1. Introduction to Machine Learning.mp4
  -  36.68 MB

  .pad/23
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  [TutsNode.net] - 2. Python/3. Lists.mp4
  -  36.43 MB

  .pad/24
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  [TutsNode.net] - 3. Numpy/1. Introduction and Installation.mp4
  -  35.96 MB

  .pad/25
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  [TutsNode.net] - 3. Numpy/4. Indexing.mp4
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  .pad/26
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  [TutsNode.net] - 3. Numpy/7. Searching and Sorting.mp4
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  .pad/27
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  [TutsNode.net] - 5. Data Visualization/1. Introduction to Matplotlib.mp4
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  [TutsNode.net] - 7. Machine Learning/1.1 Machine Learning Introduction.pptx
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  .pad/28
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  [TutsNode.net] - 2. Python/5. Loops.mp4
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  .pad/29
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  [TutsNode.net] - 7. Machine Learning/2. Supervised Machine Learning.mp4
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  .pad/30
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  [TutsNode.net] - 4. Pandas/3. DataFrame.mp4
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  [TutsNode.net] - 7. Machine Learning/7.2 Logistic Regression.pptx
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  .pad/31
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  [TutsNode.net] - 1. Introduction/2. ChatGPT Introduction.mp4
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  .pad/32
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  [TutsNode.net] - 4. Pandas/1. Pandas Introduction and Installation.mp4
  -  26.11 MB

  [TutsNode.net] - 1. Introduction/1.1 Introduction and key learning outcomes.pptx
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  .pad/33
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  [TutsNode.net] - 3. Numpy/5. Iterating.mp4
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  .pad/34
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  [TutsNode.net] - 7. Machine Learning/3. Unsupervised Machine Learning.mp4
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  [TutsNode.net] - 7. Machine Learning/11.1 Random Forest Algorithm.pptx
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  .pad/35
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  [TutsNode.net] - 2. Python/4. Conditional Statements.mp4
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  .pad/36
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  [TutsNode.net] - 4. Pandas/4. Read_CSV.mp4
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  .pad/37
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  [TutsNode.net] - 4. Pandas/2. Series.mp4
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  .pad/38
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  [TutsNode.net] - 1. Introduction/3. ChatGPT Practical.mp4
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  .pad/39
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  [TutsNode.net] - 7. Machine Learning/4. Train Test Split.mp4
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  [TutsNode.net] - 7. Machine Learning/2.1 Supervised Machine Learning.pptx
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  .pad/40
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  [TutsNode.net] - 2. Python/2. User Input.mp4
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  .pad/41
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  [TutsNode.net] - 1. Introduction/1. Course Introduction and Key Learning Outcomes.mp4
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  .pad/42
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  [TutsNode.net] - 6. Data Preprocessing/1.1 Handling Missing Values (1).pptx
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  .pad/43
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  [TutsNode.net] - 7. Machine Learning/12.2 K-Means Clustering Algorithm.pptx
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  [TutsNode.net] - 7. Machine Learning/6.2 Linear Regression.pptx
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  .pad/44
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  [TutsNode.net] - 7. Machine Learning/9.1 Support Vector Machine (SVM).pptx
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  .pad/45
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  [TutsNode.net] - 7. Machine Learning/5.1 Regression Analysis.pptx
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  .pad/46
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  [TutsNode.net] - 8. Projects/2.1 Customer Segmentation using K-means Clustering.pptx
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  .pad/47
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  [TutsNode.net] - 7. Machine Learning/8.1 K Nearest Neighbors(KNN).pptx
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  [TutsNode.net] - 6. Data Preprocessing/2.3 Feature Encoding.pptx
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Torrent Description


Description

WELCOME TO THE COURSE – MASTER PYTHON USING CHATGPT

Python is a high-level, interpreted programming language that has gained immense popularity in recent years. It is known for its simplicity, ease of use, and versatility, making it a top choice for a wide range of applications, from web development to data analysis.

As a language model developed by OpenAI, ChatGPT has a wide range of applications in programming, from natural language processing to machine learning.

Code Generation – ChatGPT can be used for code generation tasks like generating code snippets or completing code blocks. It can be trained on large code repositories to understand the patterns in code and to generate code that is similar to human-written code. This makes it a valuable tool for developers who want to automate certain coding tasks or generate code more quickly and efficiently.

Machine Learning – ChatGPT can be used for machine learning tasks like language modeling, text generation, and machine translation. It can be fine-tuned on specific tasks or datasets to improve its performance on those tasks. This makes it a powerful tool for developers who need to work with natural language data and want to improve the accuracy and effectiveness of their models.

In conclusion, ChatGPT is a versatile tool that can be used in many different ways in programming, from natural language processing to machine learning to code generation and debugging. It can save developers time and improve the quality of their work by automating certain tasks and providing more accurate and effective solutions. As the technology continues to improve, it is likely that we will see even more applications for ChatGPT in programming in the future.

By the end of the course, you’ll be able to write code with lightning speed and save countless hours that you can spend on other things. With ChatGPT, the sky is the limit, and you’ll be able to make any app you can imagine.

SO THIS IS ONE COMPLETE COURSE THAT WILL TEACH YOU ABOUT PYTHON, 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:

Anyone who wants to get started with Python Programming and Learn about Data Science and Machine Learning with a very short learning curve as this course uses ChatGPT to make things fast and more efficient.

Requirements

A desire to learn about latest technologies like ChatGPT, Python, Data Science and Machine Learning.

Last Updated 5/2023