Recently I was introduced to the field of Data Science (its been 6 months approx), and Ii started the journey with Machine Learning Course by Andrew Ng and post that started working on the Data Science Specialization by JHU.On practical application front, I have been working on building a predictive model that would predict attrition. So far I have used glm, bayesglm, rf in an effort to learn and apply these methods, but I find a lot of gap in my understanding of these algorithms.My basic dilemma is:Whether I should focus more on learning the intricacies of a few algorithms or should I use the approach of knowing a lot of them as and when and as much as required?Please guide me in the right direction, maybe by suggesting books or articles or anything that you think would help. I would be grateful if you would reply with an idea of guiding someone who has just started his career in the field of Data Science, and wants to be a person who solves practical issues for the business world.I would read (as many as possible) resources (books,articles) suggested in this post and would provide a personal feed back on the pros and cons of the same so as to make this a helpful post for people who come across a similar question in future,and i think it would be great if people suggesting these books can do the same.
Studying machine learning algorithms: depth of understanding vs. number of algorithms
machine learning