Syllabus – What you will learn from this course
1.Introduction
2.Linear Regression with One Variable
3.Linear Algebra Review
4.Linear Regression with Multiple Variables
5.Octave/Matlab Tutorial
6.Logistic Regression
7.Regularization
8.Neural Networks: Representation
9.Neural Networks: Learning
10.Advice for Applying Machine Learning
11.Machine Learning System Design
12.Support Vector Machines
13.Unsupervised Learning
14.Dimensionality Reduction
15.Anomaly Detection
16.Recommender Systems
17.Large Scale Machine Learning
18.Application Example: Photo OCR
Skills you will gain
Logistic Regression
Artificial Neural Network
Machine Learning (ML) Algorithms
Machine Learning
Machine Learning?
- Sign Up on coursera.org
- Subscribe Here(Machine Learning): Click Here
- Explore 100+ CSS Cubes Examples from CodePen
- CSS Popup/Modal Design: 35+ Examples for Inspiration
- 35+ stunning css paper effect examples for your next design project
- 50+ CSS Cards Layouts with Code and Demos
- 40+ Best CSS Liquid / Water Effects Codepen Collection
- 20+ Examples of How to Create a Stunning 3D Slider Effect
- 15+ Examples of Animating CSS Grid Layout
- 30+ Best CSS Text Stroke Effects For Your Inspiration
- Get Inspired with 30+ CSS Motion Path Examples
- 65+ Excellent CSS Image Galleries to Get Inspired From
Join Us(Mandatory to Join):
| Follow us on Facebook: | Click Here |
| Follow us on Dev.io: | Click Here |
| Follow us on Twitter: | Click Here |
| Follow us on Quora space | Click Here |
| Follow us on Pinterest: | Click Here |
