Whats the importance of doing Descriptive Analysis of dataset before start to train an ML algorithm?
You might want to refer to this question which discusses the difference between Descriptive Statistics and Exploratory statistics (or analysis).
Descriptive statistics helps in extracting as much useful information from the dataset as possible. So, it is done before the model is constructed.
Is it best to apply a "simple and dirty" ML algorithm to the data set and start to refine?
No, just throwing any algorithm at the dataset might give you some accuracy and some results, but you wouldn't learn anything by doing so.
Instead, start by taking a numerical dataset, apply linear regression on it, and learn about the importance of coefficients and their effect on the regression model.
Then, try to optimize the results with a basic Gradient Descent algorithm. Appreciate the beauty of it. The concept of moving down the curve with differentials, is really good for an absolute beginner to savour the concept of predictive analytics and ML.
Then, take up a classification problem. Kaggle's titanic problem is a nice start.
The book on Pattern Recognition and Machine Learning by Bishop is an excellent book to have.
Some really nice ML problems on real-world datasets for getting hands dirty:
- Kaggle's Bicycle Demand: One has to forecast bike rental demand depending on the features in the data.
- US Census data: Working on this dataset would help you get really good at doing descriptive statistics.
- Analytics Edge on EdX: A nice course on EdX about real world analytics, which ends with a Kaggle competition for the course participants.