Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

ChatGPT for Deep Learning with Python Keras and Tensorflow

Name: ChatGPT for Deep Learning with Python Keras and Tensorflow
Rating: 4.4 (21 reviews)

Master Image Recognition, Time Series Prediction, Regression and Classification with ChatGPT! A Project-based Course.

Created byAlexander Hagmann

Last updated 12/2025

English

What you'll learn

Utilize ChatGPT for real-life Data Science and Deep Learning projects
Let ChatGPT do the coding work for you (Python, Pandas, Keras etc.)
Use ChatGPT to select the most suitable Neural Network for your task
Utilize ChatGPT to analyse and interpret the outcomes of your Deep Learning models
Ask ChatGPT to critically assess and improve your Neural Networks
Perform an Explanatory Data Analysis with ChatGPT and Python
Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas coding & more
Utilize ChatGPT to fit, evaluate and optimize FNN, CNN, RNN and LSTM models
Utilize ChatGPT for Regression and Classification tasks using Keras & Tensorflow
Utilize ChatGPT for Image Recognition
Utilize ChatGPT for Time Series Prediction
Use ChatGPT for Error Handling and Troubleshooting

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

11 sections • 193 lectures • 14h 53m total length

Welcome and Introduction2:34
Sneak Preview: Deep Learning with ChatGPT6:13
How to get the most out of this course10:22
Course Overview5:45
Download Materials / Downloads2:41

What is ChatGPT and how does it work?3:56
ChatGPT vs. Search Engines5:23
Artificial Intelligence vs. Human Intelligence4:45
Creating a ChatGPT account and getting started7:44
**Update July 2024**6:42
Features, Options and Products around GPT models6:57
Update (July 2024): Products and Availability (FREE vs. PLUS)0:19
Navigating the OpenAI Website7:38
Navigate the OpenAI website to explore ChatGPT, DALL-E, and API usage, while learning about safety, data privacy, tokens, and pricing for model use.
What is a Token and how do Tokens work?7:33
Prompt Engineering Techniques (Part 1)12:33
Prompt(s) used in previous Lecture0:26
Prompt Engineering Techniques (Part 2)5:03
Prompt(s) used in previous Lecture0:11
Prompt Engineering Techniques (Part 3)8:01
Prompt(s) used in previous Lecture0:12

Deep Learning vs. traditional Machine Learning7:01
Prompt(s) used in previous Lecture0:14
Neural Network Types - Overview7:32
Prompt(s) used in previous Lecture0:08
The Feedforward Neural Network (FNN) explained8:36
Prompt(s) used in previous Lecture0:15
Neural Network Types - CNN and RNN at a glance13:30
Prompt(s) used in previous Lecture0:17
Pre-trained GPT models vs. customized Neural Networks - What to use when8:02
Prompt(s) used in previous Lecture0:18
Test your Deep Learning / Neural Networks Knowledge

Project Introduction3:21
GPT Model Upgrades (July 24)3:17
Project Assignment5:44
Providing the Dataset to GPT-3.5 / GPT-4o mini3:20
Prompt(s) used in previous Lecture0:11
Task 1: Inspecting the Dataset with GPT-3.5 / GPT-4o mini10:44
Inspect the census data sample with pandas to understand the dataset structure, viewing each row as an observation and each column as a property for machine learning and predictive analysis.
Prompt(s) used in previous Lecture0:21
Task 2: Brainstorming with GPT-3.5 / GPT-4o mini5:19
Prompt(s) used in the previous Lecture0:29
Task 3: Data Cleaning with GPT-3.5 / GPT-4o mini11:37
Prompt(s) used in previous Lecture0:33
Task 4: Identifying and Creating new Features with GPT-3.5 / GPT-4o mini6:35
Prompt(s) used in previous Lecture0:15
Task 5: Saving the cleaned Dataset4:02
Prompt(s) used in previous Lecture0:05
Loading the Dataset with GPT-4 / GPT-4o2:10
Prompt(s) used in previous Lecture0:09
Initial Data Inspection and Brainstorming with GPT-4 / GPT-4o7:11
Prompt(s) used in previous Lecture0:35
Data Cleaning with GPT-4 / GPT-4o6:45
Prompt(s) used in previous Lecture0:26
Troubleshooting5:03
Identifying and Creating new Features with GPT-4 / GPT-4o6:46
Prompt(s) used in previous Lecture0:15
How to download and save the cleaned Dataset from GPT-4 / GPT-4o2:30
Prompt(s) used in previous Lecture0:06
Conclusion, Final Remarks and Troubleshooting6:37

Project Introduction2:53
Explore explanatory data analysis with ChatGPT and the Python data science stack using a cleaned census dataset. Analyze univariate, bivariate, and multivariate features and discuss implications for deep learning projects.
Project Assignment4:00
Perform an end-to-end exploratory data analysis on census data with Python and ChatGPT, focusing on income, univariate and bivariate analyses, and deriving features for ML/DL projects in a notebook.
Task 1: (Up-) Loading the Dataset and first Inspection3:57
Prompt(s) used in the previous Lecture0:11
Excursus: Behind the Scenes2:48
Task 2: Brainstorming: Goals and Objectives of an EDA3:29
Prompt(s) used in the previous Lecture0:11
Task 3: Univariate Data Analysis8:27
Prompt(s) used in the previous Lecture0:07
Task 4: Multivariate Data Analysis: Correlations7:17
Prompt(s) used in the previous Lecture0:06
Task 5: Exploring Factors influencing Income7:15
Prompt(s) used in the previous Lecture0:13
Task 6: Implications & Outlook3:29
Prompt(s) used in the previous Lecture0:13
The Code reviewed & Troubleshooting9:03

Project Introduction2:57
Project Assignment5:43
Task 1: (Up-) Loading the Dataset and first Inspection2:24
Load the census data from the course materials and inspect it in a Jupyter notebook with pandas, verifying nearly 49,000 observations and 19 columns via df.info.
Prompt(s) used in previous Lecture0:25
Task 2: Brainstorming: How to best tackle a FNN Classification Project8:57
Prompt(s) used in previous Lecture0:26
Task 3: Data Pre-processing and Feature Engineering (Theory)10:00
Explore data pre-processing and feature engineering for deep learning with keras and tensorflow, covering missing values, feature scaling, one-hot encoding, binary representations, and handling imbalanced data.
Prompt(s) used in previous Lecture0:33
Feature-specific questions and considerations6:10
Prompt(s) used in previous Lecture0:30
Actions derived from Brainstorming2:48
Task 4: Data Pre-Processing and Feature Engineering (Code)7:38
Prompt(s) used in previous Lecture0:50
Task 5: Defining and Fitting an FNN Baseline Model9:58
Prompt(s) used in previous Lecture0:33
Task 6: Evaluation of Baseline Model on the Test Set8:06
Prompt(s) used in previous Lecture0:21
Task 7: Model Optimization - Theory5:46
Prompt(s) used in previous Lecture0:37
Task 7: Model Optimization - Code8:32
Prompt(s) used in the previous Lecture0:20
Performance Evaluation and Model Architecture10:16
Compare model performance with and without class weighting, noting higher recall but lower precision for the minority class, and discuss hyperparameter tuning and architecture changes to optimize accuracy and F1.
Prompt(s) used in the previous Lecture0:25
Modifying the number of Hidden Layers3:34
Task 8: Decision Thresholds (Precision vs. Recall)7:54
Prompt(s) used in the previous Lecture0:06
The full project using GPT4 (Part 1)7:57
The full project using GPT4 (Part 2)6:22
Compute precision, accuracy, recall, F1, and ROC curve on the test set, while addressing class imbalance and guiding hyperparameter tuning with cross-validation, class weights, and dropout.
Bonus Task: Feature Importance and Outlook (Part 1)8:01
Prompt(s) used in the previous Lecture0:21
Bonus Task: Feature Importance and Outlook (Part 2)8:39
Apply scikit-learn permutation importance to a trained pipeline, visualize results with a box plot, and interpret top features such as capital changes, education num, and occupation.
Prompt(s) used in the previous Lecture0:19

Project Introduction3:51
Explore image recognition with convolutional neural networks using Keras and TensorFlow, starting from loading CIFAR-10, preprocessing, training a CNN with data augmentation, validation, and checkpointing, plus batch normalization and dropout.
Project Assignment4:22
Task 1: Downloading the Dataset2:40
Task 2: Loading the Dataset with Python and first Data Inspection11:06
Prompt(s) used in the previous Lecture0:16
Task 3: Displaying the images with Python3:41
Create a Python function with parameter x to display first x images from data batch one in a Jupyter notebook, handling unpickle and label names, reshaping and transposing for matplotlib.
Prompt(s) used in the previous Lecture0:07
Task 4: Loading, Merging, formatting and storing the full dataset4:36
Prompt(s) used in the previous Lecture0:13
Task 5: Data Preprocessing4:45
Normalize image pixel values to 0-1 and convert labels to one-hot encoding for ten classes, preparing loaded data and labels for training a neural network, while preserving image appearance.
Prompt(s) used in the previous Lecture0:12
Task 6: Brainstorming3:49
Prompt(s) used in the previous Lecture0:16
Task 7: Creating and Training a Baseline CNN model8:18
Build and train a baseline CNN on cifar ten dataset with TensorFlow, featuring three conv layers, max pooling, flatten, dense layers, softmax for categories, and early stopping, 20% validation split.
Prompt(s) used in the previous Lecture0:15
Task 8: Evaluating the Baseline Model13:00
Prompt(s) used in the previous Lecture0:08
Task 9: Data Augmentation & Model Checkpointing12:02
Prompt(s) used in the previous Lecture0:22
Model Checkpointing4:17
Advanced Data Augmentation & Fine Tuning5:29
Prompt(s) used in the previous Lecture0:14
Task 10: Increasing Model Architecture Complexity & Dropout8:49
Prompt(s) used in the previous Lecture0:11
Adding Dropout4:13
Prompt(s) used in the previous Lecture0:11

Project Introduction2:55
Project Assignment6:19
Task 1: (Up-) Loading the Dataset and first Inspection5:00
Prompt(s) used in the previous Lecture0:12
Task 2: Explanatory Data Analysis (EDA)5:27
Prompt(s) used in previous Lecture0:06
Task 3: Brainstorming: How to best tackle an RNN Time Series Project8:49
Prompt(s) used in the previous Lecture0:35
Task 4: Covariance Stationarity and other Time Series specific aspects10:27
Explore covariance stationarity in time series for deep learning, learn when LSTMs benefit from stationary data, and apply the augmented Dickey-Fuller test to electricity data to assess stationarity.
Prompt(s) used in the previous Lecture0:14
Task 5: Feature Creation - adding temporal features11:27
Prompt(s) used in the previous Lecture0:33
Task 6: Creating and fitting a Baseline Model11:25
Prompt(s) used in the previous Lecture0:28
Performance Evaluation on the Test Set5:59
Prompt(s) used in the previous Lecture0:12
Finding the optimal look-back period (Lags)5:21
Task 7: Adding more Features to the model (Part 1)7:17
Add features to the LSTM model by incorporating a second feature with the same 24 lags, adjust data preparation, and evaluate with root mean squared error and R-squared.
Prompt(s) used in the previous Lecture0:14
Task 7: Adding more Features to the model (Part 2)3:51
Task 8: Adding Temporal Features to the model8:25
Prompt(s) used in the previous Lecture0:43
Task 9: Increase the complexity of the LSTM Architecture6:42
Prompt(s) used in the previous Lecture0:19
Task 10: Adding Early Stopping, Validation & more3:13
Final Assessment and potential Improvements4:32
Prompt(s) used in the previous Lecture0:19

Introduction2:24
Intro to Tabular Data / Pandas4:19
Create your very first Pandas DataFrame (from csv)6:58
Loading a CSV-file into Pandas
How to read CSV-files from other Locations3:36
Pandas Display Options and the methods head() & tail()6:41
First Data Inspection11:25
Summary Statistics
Built-in Functions, Attributes and Methods with Pandas12:06
Explore how Python built-ins and pandas dataframes work, using Titanic to show attributes like shape, size, index, and columns, and methods such as head, min (numeric_only), and mean with chaining.
Make it easy: TAB Completion and Tooltip8:57
Selecting Columns6:05
Selecting one Column with the "dot notation"2:16
Selecting Columns
Zero-based Indexing and Negative Indexing3:04
Selecting Rows with iloc (position-based indexing)10:07
Slicing Rows and Columns with iloc (position-based indexing)4:39
Position-based Indexing Cheat Sheets0:02
Position-based Indexing 1
Position-based Indexing 2
Selecting Rows with loc (label-based indexing)3:14
Slicing Rows and Columns with loc (label-based indexing)10:21
Label-based Indexing Cheat Sheets0:02
Label-based Indexing 1
Label-based Indexing 2
First Steps with Pandas Series3:53
Analyzing Numerical Series with unique(), nunique() and value_counts()13:50
Analyzing non-numerical Series with unique(), nunique(), value_counts()7:17
First Steps with Pandas Index Objects5:57
Learn to work with the pandas index object, inspect row and column indices, slice indices, customize with read_csv, set an athlete column as row index, and use get_loc for positions.
Filtering DataFrames by one Condition10:20
Learn to filter data frames with boolean masks in pandas, selecting rows by column conditions (such as male Titanic passengers) and filtering numeric columns using the log notation.
Filtering DataFrames by many Conditions4:45
Sorting DataFrames with sort_index() and sort_values()9:09
Visualizing Data with the plot() method8:48
Plot Titanic data using pandas' plot with matplotlib, explore subplots, fixed size, and share settings, and visualize the age distribution as a line plot.
Creating Histograms4:34
Create histograms of Titanic age data using pandas, matplotlib, and seaborn to visualize frequency distributions, use value counts, and explore bins from 0 to 80 with a ten-bin default.
Creating Scatterplots7:18
Understanding GroupBy objects8:05
Splitting with many Keys6:49
split-apply-combine explained9:36

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 a game-changing learning experience with "ChatGPT for Deep Learning using Python Keras and TensorFlow".

This unique course combines the power of ChatGPT with the technical depth of Python, Keras, and TensorFlow to offer you an innovative approach to tackling complex Deep Learning projects. Whether you're a beginner or a seasoned Data Scientist, this course will significantly enhance your skill set, making you more proficient and efficient in your work.

Why This Course?

Deep learning and Artificial Intelligence are revolutionizing industries across the globe, but mastering these technologies often requires a significant time investment (for theory and coding). This course cuts through the complexity, leveraging ChatGPT to simplify the learning curve and expedite your project execution. You'll learn how to harness the capabilities of AI to streamline tasks from data processing to complex model training, all without needing exhaustive prior knowledge of the underlying mathematics and Python code.

Comprehensive Learning Objectives

By the end of this course, you will be able to apply the most promising ChatGPT prompting strategies and techniques in real-world scenarios:

ChatGPT Integration: Utilize ChatGPT effectively to automate and enhance various stages of your Data Science projects, including coding, model development, and result analysis.
Data Management: Master techniques for loading, cleaning, and visualizing data using Python libraries like Pandas, Matplotlib, and Seaborn.
Deep Learning Modeling: Gain hands-on experience in constructing and fine-tuning Neural Networks for tasks such as Image Recognition with CNNs, Time Series prediction with RNNs and LSTMs, and classification and regression with Feedforward Neural Networks (FNN), using ChatGPT as your assistant.
Advanced Techniques: Learn how to best utilize ChatGPT to select the best Neural Network architecture for your projects. Optimize your models with techniques like Hyperparameter Tuning and Regularization, and enhance your models' performance with strategies like Data Augmentation.
Theoretical Foundations: While the course emphasizes practical skills, you'll also gain a clear understanding of the theoretical underpinnings of the models you're using, helping you make informed decisions about your approach to each project.

Course Structure

This course is structured around interactive, project-based learning. Each module is designed as a "Do-It-Yourself" project that challenges you to apply what you've learned in real-time. You’ll receive:

Detailed Project Assignments: These assignments mimic real-world problems and are designed to test your application of the course material.
Supporting Materials: Access to a wealth of resources, including sample prompts for ChatGPT, code snippets, and datasets.
Video Solutions: At the end of each project, a detailed video solution will guide you through the expected outcomes and provide additional insights.
Prompting Strategies: Exclusive content on effective prompting for both GPT-3.5 / GPT-4o mini (free) and GPT-4 / GPT-4o (Plus), helping you maximize your use of these powerful tools.

Who Should Enroll?

Data Science Beginners: If you are new to Data Science and Deep Learning, this course offers a friendly introduction to complex concepts and applications, significantly reducing your learning time.
Experienced Data Scientists and Analysts: For those looking to enhance their productivity and incorporate cutting-edge AI tools into their workflows, this course provides advanced strategies and techniques to streamline and optimize your projects.

Are You Ready to Revolutionize Your Data Science Capabilities?

Enroll now to begin your journey at the forefront of artificial intelligence and deep learning innovation. Transform your professional capabilities and embrace the future of AI with confidence!

Who this course is for:

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 and Neural Networks
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 / Deep Learning Wizards needing help and assistance for their models from ChatGPT.

ChatGPT for Deep Learning with Python Keras and Tensorflow

What you'll learn

Explore related topics

Coding Exercises

Course content

Getting started5 lectures • 28min

ChatGPT Introduction15 lectures • 1hr 17min

Python Installation4 lectures • 45min

Understanding Deep Learning and Neural Networks - with ChatGPT10 lectures • 46min

Introduction Project: Explore an unknown Dataset with ChatGPT and Pandas27 lectures • 1hr 34min

Using ChatGPT for Explanatory Data Analysis (EDA)16 lectures • 54min

Using ChatGPT for Binary Classification with Feedforward Neural Networks (FNN)32 lectures • 2hr 17min

Using ChatGPT for Image Recognition with Convolutional Neural Networks (CNN)26 lectures • 1hr 37min

Using ChatGPT for Time Series Prediction with Recurrent Neural Networks (RNN)27 lectures • 1hr 51min

Appendix: Pandas Crash Course30 lectures • 3hr 17min

Requirements

Description

Who this course is for: