edX
MOOC / Non-credit
0
Implementing AI Algorithms from Scratch
About this course
Build core artificial intelligence and machine learning algorithms from scratch to understand how they work beneath high-level libraries. This advanced path covers neural networks, clustering, ensembles, optimization, classification, and regression.
C
59/100
CourseAsk score
- What the provider tells you
- 23/45
- Who stands behind it
- 20/35
- 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 basic neural network with forward and backward propagation
- Code clustering algorithms such as k‑means and hierarchical clustering
- Construct ensemble methods including random forests and boosting
- Apply gradient‑based optimization techniques to train models
- Develop classification and regression models without relying on pre‑built libraries
Course objectives
- Understand the mathematical foundations of key AI algorithms
- Translate algorithmic equations into working Python code
- Analyze the behavior and performance of hand‑written models
- Compare custom implementations with library equivalents
Artificial Intelligence
#regression
#classification
#artificial intelligence
#machine learning
#programming
#neural networks
#ensemble methods
#optimization techniques
#data clustering
#algorithm development
#backpropagation
#gradient descent
#k-means
#hierarchical clustering
#random forest
#boosting
#linear regression
#logistic regression
#optimization
#loss functions
#python
#algorithm implementation
$160.00
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