For the future i'd advise to either not use forced black color with transparent background (just use a white one then) or do not use a forced font color, sadly i cant really read it, as i am using nightmode.

Hopefully some lightmode users can help you :)

In my experience many of software engineers forgot most of linear algebra and calculus if they knew them from the start. Some also forgot probailty/statistics. If there was no preliminary requirements for participants course should start from refreshing those areas.

If each of this week has practical project with real-life data then it sounds very interesting. I did a MSc in Applied math so this course would suit me well. Not sure about CS people with no or little math/statistics background.

Where’s this course being offered?

You messed up the text in box 12, it's a duplicate of box 11.

I’d take it!

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A typical machine learning curriculum should cover the following topics:

Introduction to machine learning

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Naive Bayes

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVMs)

Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Clustering (K-means, Hierarchical)

Dimensionality Reduction (PCA, t-SNE)

Ensemble Methods

Model evaluation and selection

Hyperparameter tuning

Regularization

Bias-Variance Trade-off

Overfitting and Underfitting

Model interpretability and explainability

I’d add lecture(s) talking about MAP/MLE, bias-variance trade off, and model interpretability, common pitfalls (Eg. Concept drift), and (maybe) building ml systems.

I’d skip the lectures on reinforcement learning and gans and maybe add a lecture on recommender systems. I’d say you need quite a bit of knowledge on both of these topics before you can actually solve real/practical problems.

Honestly, 16 weeks isn’t a lot of time to learn/digest all of this material in depth. I’d focus a lot more on the practical.