I'd advise you to keep trying with Weka. It's a great tool, not only for implementation but also to get an idea of which algorithms will work for you, what your data looks like, etc. The book is worth the price, but if you're not willing to buy it, this wiki page might be a good starting point.
It might be best to start with testing, not programming - I believe the quote goes "60% of the difficulty of machine learning is understanding the dataset". Play around with the Weka GUI, find out what works best for you and your data, and do try some of the meta-classifiers (boosting, bagging, stacking); they often give great results (at the cost of processing time).