When confronted with the full collection of End-to-End Machine Learning courses, it's natural to wonder where to start. Here are a few course sequences to choose from, depending on your interests, but they are written to be able to stand alone so feel free to follow where your curiosity leads.

Introduction to data science

000. Foundational skills

101. Data science concepts

121. Navigating a data science career

131. Data munging tips and tricks


111. Getting ready to learn Python, Mac edition

112. Getting ready to learn Python, Windows edition

201. Intro to Python

135. Python cookbooks and concepts II


133. Navigating Matplotlib

137. Signal processing techniques

211. Neural network visualization

Applied machine learning

171. How to choose a model

173. How optimization works

191. How selected models and methods work

211. Decision trees with Python and Pandas

212. Time-series analysis

213. Nonlinear modeling and optimization

209. Cottonwood examples

Neural networks

193. How neural networks work

312. Build a neural network framework

313. Advanced neural network methods

314. Neural network optimization

321. One dimensional convolutional neural networks

322. Two dimensional convolutional neural networks