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