Zero to Mastery Machine Learning
The "Zero to Mastery Machine Learning" project is a comprehensive educational resource for those seeking to delve into the world of machine learning. It encompasses a range of materials, including code, notebooks, images, and other learning aids, all of which complement the Zero to Mastery Machine Learning Course available on Udemy and zerotomastery.io.
Quick Access Resources
- Watch Online: The initial ten hours of the course are accessible on YouTube, providing a solid start for new learners.
- Read Online: Course materials are available as a beautifully designed online book, offering a structured reading experience.
- Engage with the Community: If you find any issues with the code, you can open an issue, or if you have questions, participate in discussions and use the provided question templates to connect with instructors and peers.
Recent Updates
- 30 October 2024: Released an updated version for 2025, featuring the milestone project "Bulldozer Price Regression."
- 12 September 2024: Ongoing updates in preparation for 2025.
- 12 October 2023: Online book version of course materials was created for ease of access and study.
Course Contents
The course content is organized in a suggested chronological order, but flexibility is encouraged. All datasets utilized in the course can be found in the data/ folder of the repository.
- Framework for Machine Learning Projects: A 6-step guide for tackling machine learning projects by breaking them into manageable parts.
- Introduction to Key Tools:
- NumPy: Essential for numerical processing in Python.
- Pandas: A tool for data manipulation and analysis.
- Matplotlib: For creating data visualizations.
- Scikit-Learn: Offers machine learning algorithms and data processing techniques.
- Milestone Projects:
- Heart Disease Classification: An end-to-end project to classify health data.
- Bulldozer Price Prediction: Building a regression model to predict sales prices.
- Dog Vision with TensorFlow/Keras: An introduction to deep learning frameworks for image classification.
- Communicating Your Work: Tips on presenting and sharing your findings from machine learning projects.
Course Focus
The course is built around a three-pronged focus:
- Develop a problem-solving framework specific to machine learning.
- Equip learners with the best tools fitting this framework.
- Foster practical skills through targeted practice on real-world projects.
Structuring the Course
The course is thoughtfully structured into sections, each targeting specific learning goals:
- Preparing learners and their computing environments for machine learning.
- Introducing essential data science tools (pandas, NumPy, Matplotlib, Scikit-Learn).
- Practicing with structured data projects focused on classification and regression.
- Exploring neural networks and deep learning with TensorFlow 2.0.
- Emphasizing the communication of analysis and insights.
Student Contributions
Students are encouraged to share their notes and insights from the course. Here are examples of such contributions:
- Chester's Notes: Comprehensive notes available on GitHub.
- Sophia's Notes: Detailed write-ups hosted on her blog.
This well-rounded approach ensures that students not only learn theoretical concepts but also gain hands-on experience, preparing them for a career in machine learning.