Welcome to the Zero to Mastery Data Science and Machine Learning Bootcamp!
This webpage contains all of the materials for the Zero to Mastery Data Science and Machine Learning Bootcamp.
Quick Links
- 📚 Get the course materials on the course GitHub
- 🎥 Watch the first 10 hours of the course on YouTube
- 🤓 Read more on the course page
- 💻 Sign up to the course on Zero to Mastery and start coding
Contents
| Resource | Description |
|---|---|
| A 6 step framework for approaching machine learning projects | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. |
| Introduction to NumPy | NumPy stands for Numerical Python. It's one of the most used Python libraries for numerical processing (which is what much of data science and machine learning is). |
| Introduction to pandas | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. |
| Introduction to Matplotlib | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. |
| Introduction to Scikit-Learn | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. |
| Introduction to TensorFlow/Keras and Deep Learning | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision 🐶👁️, a neural network to identify dog breeds in images. |
| Communicating your work | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks ons how you can communicate your work to others. |
If you have any questions, leave an issue/discussion on the course GitHub.
Author
Last update: 26 April 2024