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