This project serves as your gateway to mastering machine learning through practical application in Python. It provides comprehensive example code and solutions to exercises featured in the acclaimed book, “Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd Edition” – your essential guide to hands-on machine learning with scikit learn keras and tensorflow.
Note: For those using the first edition, notebooks are available at ageron/handson-ml. If you’re seeking the most current content, explore the notebooks for the 3rd edition at ageron/handson-ml3, ensuring you have the most up-to-date resources for Hands On Machine Learning With Scikit Learn Keras And Tensorflow.
Quick Start Guide to Machine Learning Notebooks
Eager to dive into machine learning notebooks without installation hassles? We offer several accessible online platforms to get you started immediately.
Launch Interactive Notebooks Online (No Installation Required)
To immediately begin experimenting with these notebooks and practice hands on machine learning with scikit learn keras and tensorflow, we highly recommend these platforms, especially Colab and Kaggle, which provide free GPU and TPU resources, significantly accelerating your learning experience with hands-on machine learning with scikit learn keras and tensorflow.
Important Reminder: Be mindful that these cloud environments are temporary. Your work will be erased after each session concludes. Download any important data to your local machine to prevent data loss.
Preview Notebooks Directly Online
Want to quickly review the notebooks without executing the code? Simply browse the repository on GitHub. You can effortlessly view each notebook to understand the structure and content, making it an excellent way to get a feel for the practical exercises in hands on machine learning with scikit learn keras and tensorflow.
Run Locally with Docker
For users preferring a containerized environment, Docker instructions are available. Visit the Docker instructions for detailed steps to set up and run the project using Docker, ideal for a consistent and reproducible environment for hands on machine learning with scikit learn keras and tensorflow.
Local Installation Guide
To install this project on your personal computer and fully engage in hands on machine learning with scikit learn keras and tensorflow, follow these steps:
- Install Anaconda or Miniconda: Begin by installing Anaconda or Miniconda. Anaconda is a comprehensive distribution, while Miniconda is a minimal installation, both excellent for managing Python environments needed for hands on machine learning with scikit learn keras and tensorflow.
- Install Git: Ensure git is installed on your system. Git is essential for cloning the project repository and keeping your notebooks updated.
- GPU Driver (Optional but Recommended): If your system has a compatible NVIDIA GPU and you plan to use TensorFlow for GPU-accelerated computing in hands on machine learning with scikit learn keras and tensorflow, install the GPU driver. Also, install the correct versions of CUDA and cuDNN as required by TensorFlow. Refer to TensorFlow’s official documentation for detailed compatibility and installation instructions to optimize your experience with hands on machine learning with scikit learn keras and tensorflow.
Once these prerequisites are installed, clone the repository by opening your terminal and executing the following commands:
$ git clone https://github.com/ageron/handson-ml2.git
$ cd handson-ml2Next, set up your Python environment using Anaconda or Miniconda:
$ conda env create -f environment.yml
$ conda activate tf2
$ python -m ipykernel install --user --name=python3Finally, launch Jupyter Notebook to start exploring the notebooks for hands on machine learning with scikit learn keras and tensorflow:
$ jupyter notebookFor more detailed guidance, consult the detailed installation instructions.
Frequently Asked Questions (FAQ)
Which Python Version is Recommended?
Python 3.8 is the recommended version for optimal compatibility. Following the installation steps ensures you will be using Python 3.8. While much of the code may function with newer Python 3 versions, some libraries might not yet fully support Python 3.9 or 3.10. Therefore, Python 3.8 is advised for the best experience with hands on machine learning with scikit learn keras and tensorflow.
Troubleshooting load_housing_data() Errors
If you encounter errors with load_housing_data(), ensure you have executed fetch_housing_data() beforehand. For HTTP errors, meticulously verify that your code exactly matches the notebook examples. Copying and pasting code can prevent subtle errors. If issues persist, examine your network settings.
Resolving SSL Errors on MacOSX
SSL errors on MacOSX often require installing SSL certificates. See this helpful StackOverflow question for solutions. If Python was installed from the official website, run /Applications/Python 3.8/Install Certificates.command in your terminal, adjusting 3.8 to your installed version. MacPorts users should execute sudo port install curl-ca-bundle.
Updating a Local Installation
Instructions for updating your locally installed project to the latest version are detailed in INSTALL.md. Regularly updating ensures you have the newest materials for hands on machine learning with scikit learn keras and tensorflow.
Updating Python Libraries with Anaconda
Guidance on updating your Python libraries using Anaconda to keep your environment current for hands on machine learning with scikit learn keras and tensorflow is available in INSTALL.md.
Contributors
We extend our sincere gratitude to all contributors who have enhanced this project. Your feedback, issue reporting, and pull requests are invaluable. Special acknowledgments are due to Haesun Park and Ian Beauregard for their thorough reviews of every notebook and numerous PR contributions, including assistance with exercise solutions. We also thank Steven Bunkley and Ziembla for creating the docker directory, and GitHub user SuperYorio for contributions to exercise solutions, all collectively enriching this resource for hands on machine learning with scikit learn keras and tensorflow.
