Comment

Comment by Fraser Doswell on August 28, 2019 at 5:06pm

Hi ThetaHat,

The link is working, and the code in colab gives me summary statistics and information for the Breast cancer wisconsin (diagnostic) dataset. The links to the cheatsheets and notes all appear to be working, as are the 2 links to the colab on the 2nd page of these comments.

Comment by ThetaHat on August 28, 2019 at 12:09pm

Hi Ajit-

Are these links working?

https://colab.research.google.com/drive/1qrj5B5

Comment by ThetaHat on August 28, 2019 at 11:58am

Hello everybody-

Glad to be here and hoping to learn a bunch.

Comment by Ernest Paul on July 18, 2019 at 11:46pm

I'm truly delighted and priviledged to belong to this group.

I hope to learn a lot.

Comment by ajit jaokar on June 8, 2019 at 12:30pm

The second book is out in the weekend series! https://www.datasciencecentral.com/profiles/blogs/free-book-azure-m...

Comment by ajit jaokar on June 7, 2019 at 9:20am

hello all the Azure book is coming by next week rgds ajit

Comment by ajit jaokar on May 28, 2019 at 8:39pm

coming next week - Azure machine learning concepts in a weekend - https://www.datasciencecentral.com/profiles/blogs/azure-machine-lea...

Comment by ajit jaokar on May 28, 2019 at 8:38pm

here is the final book - https://www.datasciencecentral.com/profiles/blogs/free-book-classif...

Comment by ajit jaokar on May 4, 2019 at 11:02am

for the Classification and Regression code and book - the steps are as below

These also mirror the diagram we posted before

*** Regression ****

Load and describe the data

Exploratory Data Analysis

      Exploratory data analysis – numerical

      Exploratory data analysis - visual

      Analyse the target variable

      compute the correlation

Pre-process the data

      Dealing with missing values

      Treatment of categorical values

      Remove the outliers

      Normalise the data

Split the data

Choose a Baseline algorithm

defining / instantiating the baseline model

fitting the model we have developed to our training set

Define the evaluation metric

predict scores against our test set and assess how good it is

Refine our dataset with additional columns

Test Alternative Models

Choose the best model and optimise its parameters

      Gridsearch

*** Classification ***

Load the data

Exploratory data analysis

Analyse the target variable 
Check if the data is balanced

Check the co-relations
Split the data
Choose a Baseline algorithm
Train and Test the Model
Choose an evaluation metric
Refine our dataset

Feature engineering

Test Alternative Models
Ensemble models 
Choose the best model and optimise its parameters

Comment by ajit jaokar on May 1, 2019 at 8:28am

more cheatsheets! https://github.com/afshinea/stanford-cs-229-machine-learning/tree/m... 

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