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https://brohrer.github.io/blog.html

 


End-to-end Machine Learning courses

Hands-on, project-driven courses for machine learning students and data scientists, offered through Udemy.

  1. Decision Trees: Build your own transit time predictor
  2. Time-series prediction: Build your own weather predictor
  3. Polynomial regression: Build your own down dog breed selector [Under development]
  4. Autoencoders: Build your own photo gallery compressor [Under development]

And some excellent crowdsourced recommendations for online learning

How machine learning works

    1. How neural networks work [post]  
    2. How convolutional neural networks work [post]  
    3. How convolutional neural networks work, in depth

        [slides]

    4. How recurrent neural networks and long short-term memory work [post]  
    5. How backpropagation works
    6. How optimization for machine learning works
    7. How deep learning works [post]  
    8. How support vector machines work [post]  
    9. How decision trees work [post]  
    10. How linear regression works [post]  

 

Using machine learning

    1. How to choose a machine learning model
    2. How to choose a machine learning algorithm [post]  
    3. What questions can machine learning answer [post]  
    4. How to find the right machine learning algorithm [post]  
    5. What machine learning can't do

 

Using data

    1. How data science works [post]  
    2. Data science for beginners [post]  
    3. There is more to data science than machine learning [post]  
    4. What is data
    5. How to organize data for machine learning [post]  
    6. How to clean data [post]  
    7. How to handle missing values [post]  
    8. How feature engineering works [post]  
    9. How to get good quality data [post]  
    10. Why visualize data [post]  
    11. How to slice and index pandas DataFrames [post]  
    12. How to use datetime [post]  
    13. How to convert images to video and back [post]

 

Statistics

    1. How Bayesian inference works [post]  
    2. How conditional probabilities work [post]  
    3. How joint probabilities work [post]  
    4. How marginal probabilities work [post]  
    5. How probability distributions work [post]  
    6. How autocorrelation works

 

Professional advice

Just remember what you paid for it.

Artificial intelligence editorial

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