Udemy
Certificate
0
Deep Learning Foundation : Linear Regression and Statistics
About this course
The course walks through the mathematics and code behind linear regression, covering hypothesis testing, unbiased estimators, statistical tests, and gradient descent. It shows how each concept builds a solid statistical foundation for later machine‑learning and deep‑learning work. By the end you’ll have written a regression algorithm from scratch, ready to plug into larger models.
C
55/100
CourseAsk score
- What the provider tells you
- 31/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Implement a simple linear regression algorithm from first principles
- Perform hypothesis testing on regression results
- Explain and calculate unbiased estimators
- Apply gradient descent to optimize regression parameters
- Interpret basic statistical tests used in model evaluation
Course objectives
- Teach core statistical concepts needed for machine‑learning pipelines
- Demonstrate how to translate theory into working code
- Prepare learners for deeper study of ML and deep‑learning algorithms
Machine Learning
Statistics & Probability
#machine learning
#hypothesis testing
#data analysis
#data science
#statistics
#coding
#linear regression
#gradient descent
#statistical tests
#unbiased estimators
#unbiased estimator
#statistical test
#regression algorithm
#machine learning basics
#statistical fundamentals
#data science foundation
#model evaluation
#cost function
#parameter estimation
$19.99
Price shown by Udemy — confirm on their site.
Enroll on UdemyYou'll be redirected to Udemy to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
Coursera
![[마이크임팩트 GMC] 결핍 없이 욕망할 수 있는가](https://i.udemycdn.com/course/480x270/4490290_0e54_3.jpg)
Compared on these lists
Where this course ranks against the alternatives.