After testing this idea for the last few months, we have formally launched this concept<\/p>\n \u00a0<\/p>\n The idea of \u2018Data Science Coding in a weekend\u2019<\/strong> originated from meetups we conducted in London<\/p>\n \u00a0<\/p>\n The idea is simple but effective<\/p>\n \u00a0<\/p>\n We choose a complex section of code and try to learn it in detail over the weekend<\/p>\n \u00a0<\/p>\n We work backwards i.e. try to drill down the concepts behind the main ideas<\/p>\n \u00a0<\/p>\n This led to the philosophy which I articulated in \u00a0learn machine learning coding basics in a weekend a new approach<\/a><\/p>\n \u00a0<\/p>\n And the first book free book classification and regression in a weekend<\/a><\/p>\n \u00a0<\/p>\n The \u201cin a weekend\u201d series of books on Data Science Central can be seen as an online version of our London based meetups. All the books have a single community HERE<\/a>. Like a meetup, the books are free to use. The code is in open source. We have drawn upon many sources which we have referenced in the books<\/p>\n \u00a0<\/p>\n For this first book, the steps in the code are<\/p>\n \u00a0<\/p>\n Regression<\/strong><\/p>\n Load and describe the data<\/p>\n Exploratory Data Analysis<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Exploratory data analysis \u2013 numerical<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Exploratory data analysis – visual<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Analyse the target variable<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 compute the correlation<\/p>\n Pre-process the data<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Dealing with missing values<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Treatment of categorical values<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Remove the outliers<\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0 Normalise the data<\/p>\n Split the data<\/p>\n Choose a Baseline algorithm<\/p>\n defining \/ instantiating the baseline model<\/p>\n fitting the model we have developed to our training set<\/p>\n Define the evaluation metric<\/p>\n predict scores against our test set and assess how good it is<\/p>\n Refine our dataset with additional columns<\/p>\n Test Alternative Models<\/p>\n Choose the best model and optimise its parameters<\/p>\n Gridsearch<\/p>\n \u00a0<\/p>\n Classification<\/strong><\/p>\n Load the data<\/p>\n Exploratory data analysis<\/p>\n \u00a0 \u00a0 \u00a0Analyse the target variable\u00a0 \u00a0 \u00a0 \u00a0Check the co-relations Feature engineering<\/p>\n Test Alternative Models \u00a0<\/p>\n The second book \u2013 coming by next week \u2013 is entitled \u201cAzure machine learning in a weekend\u201d.<\/p>\n \u00a0<\/p>\n I introduced the book in this blog \u2013 Azure machine learning concepts \u2013 an introduction.<\/a> \u00a0Most of us start learning development using a language like Python or R. But when you work professionally, you typically end up working with a Cloud platform. The top three Cloud platforms today in terms of market share are AWS, Azure and GCP(Google). These platforms are similar. Our goal is to learn the how to develop for these platform. \u00a0We start with Azure and then with Google next month.<\/p>\n \u00a0<\/p>\n We welcome your comments on the books and approach<\/p>\n You can download the first book free book classification and regression in a weekend<\/a> and join the community HERE<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
\u00a0 \u00a0 Check if the data is balanced<\/p>\n
Split the data
Choose a Baseline algorithm
Train and Test the Model
Choose an evaluation metric
Refine our dataset<\/p>\n
Ensemble models\u00a0
Choose the best model and optimise its parameters<\/p>\n