Lazy Programmer - Vote for Upcoming Courses
Vote on future course topics. Don't be shy!
Note: the same list of course topics appear in all 3 questions. Only the question itself is different.
* Required
Which of the following future courses would you purchase? (note: you can choose multiple)
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Time Series Analysis
Neural ODEs
XGBoost
A super massive (20+ hours) ML course like my Tensorflow 2 course but focused on all of ML and data science (not just deep learning). Application-focused instead of theory-focused.
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
Kalman Filters (more Bayesian Machine Learning)
Gaussian Processes (Bayesian Machine Learning for Regression)
More Advanced Deep Reinforcement Learning
More Advanced GANs
Machine Learning for Biology / Medicine / Genomics
Bioinformatics
Bayesian Machine Learning: Variational Inference
Transformers (BERT, NLP, Attention, etc.)
Bayesian Networks (Bayes Nets)
Markov Chain Monte Carlo (MCMC)
NLP Libraries (SpaCy, Gensim)
Required
If you had to pick only ONE course, which would you choose?
*
Kalman Filters (more Bayesian Machine Learning)
Gaussian Processes (Bayesian Machine Learning for Regression)
Time Series Analysis
XGBoost
Machine Learning for Biology / Medicine / Genomics
A super massive (20+ hours) ML course like my Tensorflow 2 course but focused on all of ML and data science (not just deep learning). Application-focused instead of theory-focused.
Markov Chain Monte Carlo (MCMC)
Neural ODEs
More Advanced GANs
Bayesian Networks (Bayes Nets)
Bioinformatics
NLP Libraries (SpaCy, Gensim)
Transformers (BERT, NLP, Attention, etc.)
More Advanced Deep Reinforcement Learning
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
Bayesian Machine Learning: Variational Inference
If you could only pick TWO courses, what would your 2nd choice be?
*
Bayesian Networks (Bayes Nets)
Gaussian Processes (Bayesian Machine Learning for Regression)
Bioinformatics
XGBoost
Machine Learning for Biology / Medicine / Genomics
A super massive (20+ hours) ML course like my Tensorflow 2 course but focused on all of ML and data science (not just deep learning). Application-focused instead of theory-focused.
Markov Chain Monte Carlo (MCMC)
Time Series Analysis
Bayesian Machine Learning: Variational Inference
Neural ODEs
Transformers (BERT, NLP, Attention, etc.)
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
Kalman Filters (more Bayesian Machine Learning)
More Advanced Deep Reinforcement Learning
NLP Libraries (SpaCy, Gensim)
More Advanced GANs
If you have other suggestions, please write them here. Try to be as detailed as possible, the longer the better!
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