Chatgpt For Python Data Science And Machine Learning

Master Data Analysis, Regression, Classification, Clustering and Pandas Coding with ChatGTP! A Project-based Course.

What you'll learn

Use ChatGPT for real-life Data Science and Machine Learning Projects

Let ChatGPT write do the Coding work (Python, Pandas, scikit-learn etc.)

Use ChatGPT to select the most suitable Machine Learning Model

Use ChatGPT to analyse and interpret the outcomes of Machine Learning & Statistical Models

Perform an Explanatory Data Analysis with ChatGPT and Python

Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas Coding & more

Use ChatGPT to fit and evaluate Regression and Classification Models

Use ChatGPT for Multiple Regression Analysis and Hypothesis Testing

Use ChatGPT for Error Handling and Troubleshooting

Master Clustering and Unsupervised Learning with ChatGPT

Requirements

An internet connection capable of streaming HD videos.

Some Data Science or Machine Learning related background (not required but it helps)

First Experience with Python and the Python Data Science Ecosystem (not required but it helps)

Description

Welcome to the first Data Science and Machine Learning course with ChatGPT. Learn how to use ChatGPT to master complex Data Science and Machine Learning real-life projects in no time! Why is this a game-changing course?Real-world Data Science and Machine Learning projects require a solid background in advanced statistics and Data Analytics. And it would be best if you were a proficient Python Coder. Do you want to learn how to master complex Data Science projects without the need to study and master all the required basics (which takes dozens if not hundreds of hours)? Then this is the perfect course for you!  What you can do at the end of the course:At the end of this course, you will know and understand all strategies and techniques to master complex Data Science and Machine Learning projects with the help of ChatGPT! And you don´t have to be a Data Science or Python Coding expert! Use ChatGPT as your assistant and let ChatGPT do the hard work for you! Use ChatGPT forthe theoretical part Python codingevaluating and interpreting coding and ML resultsThis course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts toload, initially inspect, and understand unknown datasets clean and process raw datasets with Pandasmanipulate, aggregate, and visualize datasets with Pandas and matplotlibperform an extensive Explanatory Data Analysis (EDA) for complex datasetsuse advanced statistics, multiple regression analysis, and hypothesis testing to gain further insightsselect the most suitable Machine Learning Model for your prediction tasks (Model Selection)evaluate and interpret the performance of your Machine Learning models (Performance Evaluation)optimize your models via handling Class Imbalance, Hyperparameter Tuning & more.evaluate and interpret the results and findings of your predictions to solve real-world business problemsmaster regression, classification, and unsupervised learning/clustering projectsWe´ll cover prompting strategies and tactics for GPT 3.5 (free) and GPT 4 (paid subscription). Know the differences and master both!The course is organized into Do-it-yourself projects with detailed project assignments and supporting materials. At the end, you will find a video sample solution. All solutions and sample prompts are available for simple download or copy/paste! Who is this Course for?Data Science Beginners who have no time to learn everything from scratchSkilled Data Scientists seeking to outsource the most time-consuming parts of their work to save time    Are you ready to be at the forefront of AI in Data Science? Enroll now and start transforming your professional landscape with AI and ChatGPT!

Overview

Section 1: Getting Started

Lecture 1 Welcome and Introduction

Lecture 2 Sneak Preview: Data Science with ChatGPT

Lecture 3 How to get the most out of this course

Lecture 4 Course Overview

Lecture 5 Course Materials /Downloads

Section 2: Introduction to ChatGPT

Lecture 6 What is ChatGPT and how does it work?

Lecture 7 ChatGPT vs. Search Engines

Lecture 8 Artificial Intelligence vs. Human Intelligence

Lecture 9 Creating a ChatGPT account and getting started

Lecture 10 **Design Update November 2023**

Lecture 11 Features, Options and Products around GPT models

Lecture 12 Navigating the OpenAI Website

Lecture 13 What is a Token and how do Tokens work?

Lecture 14 Prompt Engineering Techniques (Part 1)

Lecture 15 Prompt(s) used in previous Lecture

Lecture 16 Prompt Engineering Techniques (Part 2)

Lecture 17 Prompt(s) used in previous Lecture

Lecture 18 Prompt Engineering Techniques (Part 3)

Lecture 19 Prompt(s) used in previous Lecture

Section 3: Installing and working with Python, Anaconda and Jupyter Notebooks

Lecture 20 Download and Install Anaconda

Lecture 21 How to open Jupyter Notebooks

Lecture 22 How to work with Jupyter Notebooks

Section 4: Introduction Project: Explore an unknown Dataset with ChatGPT and Pandas

Lecture 23 Project Introduction

Lecture 24 Project Assignment

Lecture 25 Providing the Dataset to GPT3.5

Lecture 26 Prompt(s) used in previous Lecture

Lecture 27 Inspecting the Dataset with GPT3.5

Lecture 28 Prompt(s) used in previous Lecture

Lecture 29 Brainstorming with GPT3.5

Lecture 30 Prompt(s) used in previous Lecture

Lecture 31 Data Cleaning with GPT3.5

Lecture 32 Prompt(s) used in previous Lecture

Lecture 33 Data Transformation and Feature Engineering with GPT3.5

Lecture 34 Prompt(s) used in previous Lecture

Lecture 35 Loading the Dataset with GPT4

Lecture 36 Prompt(s) used in previous Lecture

Lecture 37 Initial Data Inspection and Brainstorming with GPT4

Lecture 38 Prompt(s) used in previous Lecture

Lecture 39 Data Cleaning with GPT4

Lecture 40 Prompt(s) used in previous Lecture

Lecture 41 Data Transformation and Feature Engineering with GPT4

Lecture 42 Prompt(s) used in previous Lecture

Lecture 43 How to download and save the cleaned Dataset from GPT4

Lecture 44 Prompt(s) used in previous Lecture

Lecture 45 Conclusion, Final Remarks and Troubleshooting

Section 5: Using ChatGPT for complex Data Wrangling and Manipulation Tasks

Lecture 46 Project Introduction

Lecture 47 Project Assignment

Lecture 48 Task 1 - Loading and Sorting

Lecture 49 Prompt(s) used in the previous Lecture

Lecture 50 Task 2 - Data Type Conversion

Lecture 51 Prompt(s) used in the previous Lecture

Lecture 52 Task 3 - Mapping

Lecture 53 Prompt(s) used in the previous Lecture

Lecture 54 Task 4 - Reversing One-Hot-Encoding

Lecture 55 Prompt(s) used in the previous Lecture

Lecture 56 Excursus: Saving Intermediate Results

Lecture 57 Task 5: Selecting Columns and their sequence

Lecture 58 Prompt(s) used in the previous Lecture

Lecture 59 Task 6: Unique and most frequent values

Lecture 60 Prompt(s) used in the previous Lecture

Lecture 61 Task 7: Grouping and Aggregating DataFrames

Lecture 62 Prompt(s) used in the previous Lecture

Lecture 63 Task 8: Advanced Filtering

Lecture 64 Prompt(s) used in the previous Lecture

Lecture 65 Task 9: Adding group-specific Features

Lecture 66 Prompt(s) used in the previous Lecture

Lecture 67 Task 10: Identifying and fixing erroneous or non-intuitive Data

Lecture 68 Prompt(s) used in the previous Lecture

Lecture 69 Task 11: Index Operations

Lecture 70 Prompt(s) used in the previous Lecture

Lecture 71 Excursus: Understanding and Handling Warnings

Lecture 72 Data Wrangling and Manipulation with GPT 4

Lecture 73 Prompt(s) used in the previous Lecture

Section 6: Using ChatGPT for Explanatory Data Analysis (EDA)

Lecture 74 Project Introduction

Lecture 75 Project Assignment

Lecture 76 Task 1: (Up-) Loading the Dataset and first Inspection

Lecture 77 Prompt(s) used in the previous Lecture

Lecture 78 Task 2: Brainstorming: Goals and Objectives of an EDA

Lecture 79 Prompt(s) used in the previous Lecture

Lecture 80 Task 3: Feature Engineering and Creation

Lecture 81 Prompt(s) used in the previous Lecture

Lecture 82 Task 4: Univariate Data Analysis

Lecture 83 Prompt(s) used in the previous Lecture

Lecture 84 Excursus: Troubleshooting

Lecture 85 Task 5: Multivariate Data Analysis: Correlations

Lecture 86 Prompt(s) used in the previous Lecture

Lecture 87 Task 6: Exploring Factors influencing Appointment No-Shows (Part 1)

Lecture 88 Prompt(s) used in the previous Lecture

Lecture 89 Task 6: Exploring Factors Influencing Appointment No-Shows (Part 2)

Lecture 90 Task 7: Exploring Factors influencing SMS reminders

Lecture 91 Prompt(s) used in the previous Lecture

Lecture 92 The Code reviewed

Lecture 93 Bonus Task: The impact of Neighbourhoods

Lecture 94 Final remarks: Missing Data and Features

Section 7: Using ChatGPT for Multiple Regression Analysis and Hypothesis Testing

Lecture 95 Project Introduction

Lecture 96 Project Assignment

Lecture 97 Task 1: Loading the Dataset and feeding ChatGPT

Lecture 98 Prompt(s) used in the previous Lecture

Lecture 99 Task 2: Brainstorming and Theoretical Background

Lecture 100 Prompt(s) used in the previous Lecture

Lecture 101 Task 3: Logistic Regression and Hypothesis Testing: Data Preparation

Lecture 102 Prompt(s) used in the previous Lecture

Lecture 103 Task 4: Fitting the Model

Lecture 104 Prompt(s) used in the previous Lecture

Lecture 105 Task 5: Exploring the Regression and Testing Results

Lecture 106 Prompt(s) used in the previous Lecture

Lecture 107 Task 6: Test and correct for Multicollinearity

Lecture 108 Prompt(s) used in the previous Lecture

Lecture 109 Task 7: Exploring and interpreting the Results and outlook

Lecture 110 Prompt(s) used in the previous Lecture

Lecture 111 Task 8: Comparison with Bivariate Analysis

Lecture 112 Prompt(s) used in the previous Lecture

Section 8: Using ChatGPT for Machine Learning & Classification

Lecture 113 Project Introduction

Lecture 114 Project Assignment

Lecture 115 Task 1: Loading the Dataset and feeding ChatGPT

Lecture 116 Prompt(s) used in the previous Lecture

Lecture 117 Task 2: Brainstorming / Model Comparison and Selection

Lecture 118 Prompt(s) used in the previous Lecture

Lecture 119 Task 3: Data Proprocessing

Lecture 120 Prompt(s) used in the previous Lecture

Lecture 121 Task 4: Fitting a Baseline Model (Part 1)

Lecture 122 Prompt(s) used in the previous Lecture

Lecture 123 Task 4: Fitting a Baseline Model (Part 2)

Lecture 124 Prompt(s) used in the previous Lecture

Lecture 125 Task 5: Evaluating the Baseline Model

Lecture 126 Prompt(s) used in the previous Lecture

Lecture 127 Task 6: Handling Class Imbalance

Lecture 128 Prompt(s) used in the previous Lecture

Lecture 129 Task 7: Hyperparameter Tuning (Theory)

Lecture 130 Prompt(s) used in the previous Lecture

Lecture 131 Task 8: Hyperparameter Tuning (Code)

Lecture 132 Prompt(s) used in the previous Lecture

Lecture 133 Final Considerations

Lecture 134 Prompt(s) used in the previous Lecture

Lecture 135 Bonus Task

Lecture 136 Prompt(s) used in the previous Lecture

Lecture 137 Feature Importance

Lecture 138 Prompt(s) used in the previous Lecture

Section 9: Using ChatGPT for Unsupervised Learning and Clustering

Lecture 139 Project Introduction

Lecture 140 Project Assignment

Lecture 141 Task 1: Loading the Dataset and feeding ChatGPT

Lecture 142 Prompt(s) used in the previous Lecture

Lecture 143 Task 2: Brainstorming / Model Comparison and Selection

Lecture 144 Prompt(s) used in the previous Lecture

Lecture 145 Task 3: Data Proprocessing

Lecture 146 Prompt(s) used in the previous Lecture

Lecture 147 Task 4: Fitting the Clustering Model

Lecture 148 Prompt(s) used in the previous Lecture

Lecture 149 Task 5: Results Evaluation

Lecture 150 Prompt(s) used in the previous Lecture

Lecture 151 Task 6: Revisiting the Number of Clusters

Lecture 152 Prompt(s) used in the previous Lecture

Lecture 153 Task 7: Analysing and Interpreting the final Clusters

Lecture 154 Prompt(s) used in the previous Lecture

Section 10: Using ChatGPT for a full ML Regression Project (XGBoost)

Lecture 155 Project Introduction

Lecture 156 Project Scenario & Assignment

Lecture 157 Solution (Overview)

Section 11: Appendix: Pandas Crash Course

Lecture 158 Introduction

Lecture 159 Intro to Tabular Data / Pandas

Lecture 160 Create your very first Pandas DataFrame (from csv)

Lecture 161 How to read CSV-files from other Locations

Lecture 162 Pandas Display Options and the methods head() & tail()

Lecture 163 First Data Inspection

Lecture 164 Built-in Functions, Attributes and Methods with Pandas

Lecture 165 Make it easy: TAB Completion and Tooltip

Lecture 166 Selecting Columns

Lecture 167 Selecting one Column with the "dot notation"

Lecture 168 Zero-based Indexing and Negative Indexing

Lecture 169 Selecting Rows with iloc (position-based indexing)

Lecture 170 Slicing Rows and Columns with iloc (position-based indexing)

Lecture 171 Position-based Indexing Cheat Sheets

Lecture 172 Selecting Rows with loc (label-based indexing)

Lecture 173 Slicing Rows and Columns with loc (label-based indexing)

Lecture 174 Label-based Indexing Cheat Sheets

Lecture 175 First Steps with Pandas Series

Lecture 176 Analyzing Numerical Series with unique(), nunique() and value_counts()

Lecture 177 Analyzing non-numerical Series with unique(), nunique(), value_counts()

Lecture 178 First Steps with Pandas Index Objects

Lecture 179 Filtering DataFrames by one Condition

Lecture 180 Filtering DataFrames by many Conditions

Lecture 181 Sorting DataFrames with sort_index() and sort_values()

Lecture 182 Visualizing Data with the plot() method

Lecture 183 Creating Histograms

Lecture 184 Creating Scatterplots

Lecture 185 Understanding GroupBy objects

Lecture 186 Splitting with many Keys

Lecture 187 split-apply-combine explained

Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch.,Data Scientists interested in boosting their work with Artificial Intelligence.,Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work.,Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT.,Machine Learning Wizards needing help and assistance for their models from ChatGPT.