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)
*
More Advanced GANs
Machine Learning for Biology / Medicine / Genomics
Transformers (BERT, NLP, Attention, etc.)
More Advanced Deep Reinforcement Learning
XGBoost
Markov Chain Monte Carlo (MCMC)
Neural ODEs
Gaussian Processes (Bayesian Machine Learning for Regression)
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
Bayesian Machine Learning: Variational Inference
Time Series Analysis
Bioinformatics
Bayesian Networks (Bayes Nets)
Kalman Filters (more Bayesian Machine Learning)
NLP Libraries (SpaCy, Gensim)
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.
Required
If you had to pick only ONE course, which would you choose?
*
Bayesian Networks (Bayes Nets)
XGBoost
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
Bayesian Machine Learning: Variational Inference
Bioinformatics
Neural ODEs
Machine Learning for Biology / Medicine / Genomics
NLP Libraries (SpaCy, Gensim)
More Advanced GANs
Time Series Analysis
Transformers (BERT, NLP, Attention, etc.)
Markov Chain Monte Carlo (MCMC)
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.
Kalman Filters (more Bayesian Machine Learning)
More Advanced Deep Reinforcement Learning
Gaussian Processes (Bayesian Machine Learning for Regression)
If you could only pick TWO courses, what would your 2nd choice be?
*
Transformers (BERT, NLP, Attention, etc.)
NLP Libraries (SpaCy, Gensim)
Gaussian Processes (Bayesian Machine Learning for Regression)
Bioinformatics
Bayesian Networks (Bayes Nets)
More Advanced Deep Reinforcement Learning
XGBoost
Neural ODEs
Bayesian Machine Learning (Bayesian Linear Regression, Classification)
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.
Kalman Filters (more Bayesian Machine Learning)
Markov Chain Monte Carlo (MCMC)
Time Series Analysis
More Advanced GANs
Machine Learning for Biology / Medicine / Genomics
Bayesian Machine Learning: Variational Inference
If you have other suggestions, please write them here. Try to be as detailed as possible, the longer the better!
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