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AI - Deep Learning / Machine Learning Coding in a weekend book series group

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AI - Deep Learning / Machine Learning Coding in a weekend book series group

hello

This group is about a new book series we are launching

AI / ML coding in a weekend 

The first book  in this series is 

Coding in a weekend - Classification and Regression

launched in Apr 2019
Each book will have 
a) Content 
b) Code based on a single program 
c) Community i.e. this group where you can ask questions

forthcoming books
a) Deep learning
b) Keras in the cloud  (Google Cloud)
c) Developing for azure 

Created and managed by Ajit Jaokar, Dan Howarth, Ayse Mutlu - London UK

we welcome other community moderators 

The books are free but exclusive to Data Science Central 

Members: 226
Latest Activity: on Friday

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You need to be a member of AI - Deep Learning / Machine Learning Coding in a weekend book series group to add comments!

Comment by Ernest Paul on Friday

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
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
Comment by ajit jaokar on April 30, 2019 at 9:50pm

Hello all
Here are two excellent resources which will help you with the coding books

Deep learning notes by Tess Fernandez
https://drive.google.com/file/d/0BzipSlf0e7yCSm90UXczekRuWGNPTjR3SE...

Data Science Cheatsheet by Maverick Lin
https://drive.google.com/file/d/1BXvMn1OxW1c4nyTNCxAvaSkqY9AYCL0V/v...

These will help you with the coding books we are working on in this group

Our strategy will be to work on one set of code per book in detail - but these references provide a good backstory

hope you find them useful
kind rgds
Ajit Dan Ayse

Comment by ajit jaokar on April 27, 2019 at 10:53pm

hello all, we continue to update the documentation. Here is the big picture diagram. kind rgds ajit

Comment by ajit jaokar on April 27, 2019 at 10:48pm

 

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