ML Maths Basics
Understand linear algebra, probability, and calculus concepts used in ML.
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After this skill you can…
- Manipulate vectors and matrices
- Understand gradient descent intuitively
- Apply Bayes' theorem and basic probability
Watch (10 videos)
Coding PCA from Scratch : Data Science Code
→ Implement PCA from scratch→ Apply dimensionality reduction techniques
ROC and AUC in R
→ Draw ROC curves in R→ Calculate AUC for model evaluation
NumPy Crash Course - Complete Tutorial
→ Apply NumPy to scientific computing→ Use NumPy for data science tasks
L3.3 Vectorization in Python
→ Implement vectorization in Python→ Optimize machine learning models with NumPy
Naive Bayes from Scratch - Machine Learning Python
→ Implement Naive Bayes from scratch→ Understand Gaussian Naive Bayes
predict.m - Programming Assignment 2 Machine Learning
→ Implement predictive models in MATLAB→ Solve machine learning problems
Logistic Regression in Python - Machine Learning From Scratch 03 - Python Tutorial
→ Implement Logistic Regression in Python→ Use NumPy for ML algorithms
Implement 1D convolution, part 6: Multi-channel, multi-kernel convolutions
→ Implement 1D convolution with Cottonwood→ Apply multi-channel convolutions to heartbeat classification
Mean Shift Dynamic Bandwidth - Practical Machine Learning Tutorial with Python p.42
→ Implement Mean Shift clustering→ Use dynamic bandwidth in clustering
I completed Andrej Karpathy’s AI challenge (advanced)
→ Implement micrograd from scratch→ Train models with Google Colab
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