English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 187 lectures (13h 27m) | 5.96 GB
Master Data Analysis, Regression, Classification, Clustering and Pandas Coding with ChatGTP! A Project-based Course.
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 for
- the theoretical part
- Python coding
- evaluating and interpreting coding and ML results
This course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts to
- load, initially inspect, and understand unknown datasets
- clean and process raw datasets with Pandas
- manipulate, aggregate, and visualize datasets with Pandas and matplotlib
- perform an extensive Explanatory Data Analysis (EDA) for complex datasets
- use advanced statistics, multiple regression analysis, and hypothesis testing to gain further insights
- select 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 problems
- master regression, classification, and unsupervised learning/clustering projects
We´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!
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
Table of Contents
Getting Started
1 Welcome and Introduction
2 Sneak Preview Data Science with ChatGPT
3 How to get the most out of this course
4 Course Overview
5 Course Materials Downloads
Introduction to ChatGPT
6 What is ChatGPT and how does it work
7 ChatGPT vs. Search Engines
8 Artificial Intelligence vs. Human Intelligence
9 Creating a ChatGPT account and getting started
10 Design Update November 2023
11 Features, Options and Products around GPT models
12 Navigating the OpenAI Website
13 What is a Token and how do Tokens work
14 Prompt Engineering Techniques (Part 1)
15 Prompt(s) used in previous Lecture
16 Prompt Engineering Techniques (Part 2)
17 Prompt(s) used in previous Lecture
18 Prompt Engineering Techniques (Part 3)
19 Prompt(s) used in previous Lecture
Installing and working with Python, Anaconda and Jupyter Notebooks
20 Download and Install Anaconda
21 How to open Jupyter Notebooks
22 How to work with Jupyter Notebooks
Introduction Project Explore an unknown Dataset with ChatGPT and Pandas
23 Project Introduction
24 Project Assignment
25 Providing the Dataset to GPT3.5
26 Prompt(s) used in previous Lecture
27 Inspecting the Dataset with GPT3.5
28 Prompt(s) used in previous Lecture
29 Brainstorming with GPT3.5
30 Prompt(s) used in previous Lecture
31 Data Cleaning with GPT3.5
32 Prompt(s) used in previous Lecture
33 Data Transformation and Feature Engineering with GPT3.5
34 Prompt(s) used in previous Lecture
35 Loading the Dataset with GPT4
36 Prompt(s) used in previous Lecture
37 Initial Data Inspection and Brainstorming with GPT4
38 Prompt(s) used in previous Lecture
39 Data Cleaning with GPT4
40 Prompt(s) used in previous Lecture
41 Data Transformation and Feature Engineering with GPT4
42 Prompt(s) used in previous Lecture
43 How to download and save the cleaned Dataset from GPT4
44 Prompt(s) used in previous Lecture
45 Conclusion, Final Remarks and Troubleshooting
Using ChatGPT for complex Data Wrangling and Manipulation Tasks
46 Project Introduction
47 Project Assignment
48 Task 1 – Loading and Sorting
49 Prompt(s) used in the previous Lecture
50 Task 2 – Data Type Conversion
51 Prompt(s) used in the previous Lecture
52 Task 3 – Mapping
53 Prompt(s) used in the previous Lecture
54 Task 4 – Reversing One-Hot-Encoding
55 Prompt(s) used in the previous Lecture
56 Excursus Saving Intermediate Results
57 Task 5 Selecting Columns and their sequence
58 Prompt(s) used in the previous Lecture
59 Task 6 Unique and most frequent values
60 Prompt(s) used in the previous Lecture
61 Task 7 Grouping and Aggregating DataFrames
62 Prompt(s) used in the previous Lecture
63 Task 8 Advanced Filtering
64 Prompt(s) used in the previous Lecture
65 Task 9 Adding group-specific Features
66 Prompt(s) used in the previous Lecture
67 Task 10 Identifying and fixing erroneous or non-intuitive Data
68 Prompt(s) used in the previous Lecture
69 Task 11 Index Operations
70 Prompt(s) used in the previous Lecture
71 Excursus Understanding and Handling Warnings
72 Data Wrangling and Manipulation with GPT 4
73 Prompt(s) used in the previous Lecture
Using ChatGPT for Explanatory Data Analysis (EDA)
74 Project Introduction
75 Project Assignment
76 Task 1 (Up-) Loading the Dataset and first Inspection
77 Prompt(s) used in the previous Lecture
78 Task 2 Brainstorming Goals and Objectives of an EDA
79 Prompt(s) used in the previous Lecture
80 Task 3 Feature Engineering and Creation
81 Prompt(s) used in the previous Lecture
82 Task 4 Univariate Data Analysis
83 Prompt(s) used in the previous Lecture
84 Excursus Troubleshooting
85 Task 5 Multivariate Data Analysis Correlations
86 Prompt(s) used in the previous Lecture
87 Task 6 Exploring Factors influencing Appointment No-Shows (Part 1)
88 Prompt(s) used in the previous Lecture
89 Task 6 Exploring Factors Influencing Appointment No-Shows (Part 2)
90 Task 7 Exploring Factors influencing SMS reminders
91 Prompt(s) used in the previous Lecture
92 The Code reviewed
93 Bonus Task The impact of Neighbourhoods
94 Final remarks Missing Data and Features
Using ChatGPT for Multiple Regression Analysis and Hypothesis Testing
95 Project Introduction
96 Project Assignment
97 Task 1 Loading the Dataset and feeding ChatGPT
98 Prompt(s) used in the previous Lecture
99 Task 2 Brainstorming and Theoretical Background
100 Prompt(s) used in the previous Lecture
101 Task 3 Logistic Regression and Hypothesis Testing Data Preparation
102 Prompt(s) used in the previous Lecture
103 Task 4 Fitting the Model
104 Prompt(s) used in the previous Lecture
105 Task 5 Exploring the Regression and Testing Results
106 Prompt(s) used in the previous Lecture
107 Task 6 Test and correct for Multicollinearity
108 Prompt(s) used in the previous Lecture
109 Task 7 Exploring and interpreting the Results and outlook
110 Prompt(s) used in the previous Lecture
111 Task 8 Comparison with Bivariate Analysis
112 Prompt(s) used in the previous Lecture
Using ChatGPT for Machine Learning & Classification
113 Project Introduction
114 Project Assignment
115 Task 1 Loading the Dataset and feeding ChatGPT
116 Prompt(s) used in the previous Lecture
117 Task 2 Brainstorming Model Comparison and Selection
118 Prompt(s) used in the previous Lecture
119 Task 3 Data Proprocessing
120 Prompt(s) used in the previous Lecture
121 Task 4 Fitting a Baseline Model (Part 1)
122 Prompt(s) used in the previous Lecture
123 Task 4 Fitting a Baseline Model (Part 2)
124 Prompt(s) used in the previous Lecture
125 Task 5 Evaluating the Baseline Model
126 Prompt(s) used in the previous Lecture
127 Task 6 Handling Class Imbalance
128 Prompt(s) used in the previous Lecture
129 Task 7 Hyperparameter Tuning (Theory)
130 Prompt(s) used in the previous Lecture
131 Task 8 Hyperparameter Tuning (Code)
132 Prompt(s) used in the previous Lecture
133 Final Considerations
134 Prompt(s) used in the previous Lecture
135 Bonus Task
136 Prompt(s) used in the previous Lecture
137 Feature Importance
138 Prompt(s) used in the previous Lecture
Using ChatGPT for Unsupervised Learning and Clustering
139 Project Introduction
140 Project Assignment
141 Task 1 Loading the Dataset and feeding ChatGPT
142 Prompt(s) used in the previous Lecture
143 Task 2 Brainstorming Model Comparison and Selection
144 Prompt(s) used in the previous Lecture
145 Task 3 Data Proprocessing
146 Prompt(s) used in the previous Lecture
147 Task 4 Fitting the Clustering Model
148 Prompt(s) used in the previous Lecture
149 Task 5 Results Evaluation
150 Prompt(s) used in the previous Lecture
151 Task 6 Revisiting the Number of Clusters
152 Prompt(s) used in the previous Lecture
153 Task 7 Analysing and Interpreting the final Clusters
154 Prompt(s) used in the previous Lecture
Using ChatGPT for a full ML Regression Project (XGBoost)
155 Project Introduction
156 Project Scenario & Assignment
157 Solution (Overview)
Appendix Pandas Crash Course
158 Introduction
159 Intro to Tabular Data Pandas
160 Create your very first Pandas DataFrame (from csv)
161 Loading a CSV-file into Pandas
162 How to read CSV-files from other Locations
163 Pandas Display Options and the methods head() & tail()
164 First Data Inspection
165 Summary Statistics
166 Built-in Functions, Attributes and Methods with Pandas
167 Make it easy TAB Completion and Tooltip
168 Selecting Columns
169 Selecting one Column with the dot notation
170 Selecting Columns
171 Zero-based Indexing and Negative Indexing
172 Selecting Rows with iloc (position-based indexing)
173 Slicing Rows and Columns with iloc (position-based indexing)
174 Position-based Indexing Cheat Sheets
175 Position-based Indexing 1
176 Position-based Indexing 2
177 Selecting Rows with loc (label-based indexing)
178 Slicing Rows and Columns with loc (label-based indexing)
179 Label-based Indexing Cheat Sheets
180 Label-based Indexing 1
181 Label-based Indexing 2
182 First Steps with Pandas Series
183 Analyzing Numerical Series with unique(), nunique() and value_counts()
184 Analyzing non-numerical Series with unique(), nunique(), value_counts()
185 First Steps with Pandas Index Objects
186 Filtering DataFrames by one Condition
187 Filtering DataFrames by many Conditions
188 Sorting DataFrames with sort_index() and sort_values()
189 Visualizing Data with the plot() method
190 Creating Histograms
191 Creating Scatterplots
192 Understanding GroupBy objects
193 Splitting with many Keys
194 split-apply-combine explained
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