Software Development

No Black Box Machine Learning Course – Learn Without Libraries



On this No Black Box Machine Learning Course in JavaScript, you’ll achieve a deep understanding of machine studying methods by coding with out counting on libraries. This distinctive strategy not solely demystifies the interior workings of machine studying but additionally considerably enhances software program improvement abilities.

Thank you for reading this post, don't forget to subscribe!

✏️ Course created by @Radu (PhD in Laptop Science)

🎥 Watch half two:

HOMEWORK
🏠 1st task spreadsheet:
🏠 Submit all different assignments to Radu’s Discord Server:

GITHUB LINKS
💻 Drawing App:
💻 Information:
💻 Customized Chart Part:
💻 Full Course Code (In Elements):

PREREQUISITES
🎥 Interpolation:
🎥 Linear Algebra:
🎥 Trigonometry:

LINKS
🔗 Take a look at the Recognizer we’ll construct on this course:
🔗 Draw for Radu, Name for assist video:
🔗 Draw for Radu, Information assortment software:
🔗 Radu’s Self-driving Automobile Course:
🔗 Radu’s older Machine Learning video:
🔗 CHART TUTORIAL (talked about at 01:45:27):
🔗 CHART CODE:

TOOLS
🔧 Visible Studio Code:
🔧 Google Chrome:
🔧 Node JS:
(ensure you add ‘node’ and ‘npm’ to the PATH atmosphere variables when requested!)

TIMESTAMPS
⌨️(0:00:00) Introduction
⌨️(0:05:04) Drawing App
⌨️(0:46:46) Homework 1
⌨️(0:47:05) Working with Information
⌨️(1:08:54) Information Visualizer
⌨️(1:29:52) Homework 2
⌨️(1:30:05) Characteristic Extraction
⌨️(1:38:07) Scatter Plot
⌨️(1:46:12) Customized Chart
⌨️(2:01:03) Homework 3
⌨️(2:01:35) Nearest Neighbor Classifier
⌨️(2:43:21) Homework 4 (higher field)
⌨️(2:43:53) Information Scaling
⌨️(2:54:45) Homework 5
⌨️(2:55:23) Okay Nearest Neighbors Classifier
⌨️(3:04:18) Homework 6
⌨️(3:04:49) Mannequin Analysis
⌨️(3:21:29) Homework 7
⌨️(3:22:01) Choice Boundaries
⌨️(3:39:26) Homework 8
⌨️(3:39:59) Python & SkLearn
⌨️(3:50:35) Homework 9

source

Comments are closed.