Would I be wasting my time attempting these with such a basic level of knowledge?
When I started I was in a similar position to you and just wanted to see if I could even tread water with some of the really knowledgeable members of the community. I ended up placing in the top 5 for one of the contests I was in (with btw a really simple model).
They usually give you some starter code in either R or Python which will give you the results for a benchmark, start there and then use cross-validaton to see if you can beat that bench mark, and if you do submit. It's very addictive and you'll come away knowing a lot more than you started with.
Why do you think it is that simple models often win? Is it due to the experts no participating or is there a lot more low-hanging fruit than I previously thought? Or just that simple models are easier to use and reason with for humans and thus easier to get right.
I know for my own beginner mistakes, it's a big error to try something out-of-the-box and immediately try to get better cv scores by creating much more complicated solutions.
The truth is a lot of work has been put into any standard implementation of an SVM, RandomForest etc (and even more work has been put into the theory behind those algorithms). Since I haven't come in 1st in any competition and am not a ML expert I don't think I can give you the correct strategy to win. But I can say as a general trend, all of my attempts to create non-standard complicated models did terribly, and many of the decisions I made based on research into fixing a particular problem in a known solution seemed to be better performing (i.e. "How to deal with imbalanced classification problems with a RF?" type questions)
In many cases where simple models win, there's some insight into the data that the winner found - engineered a new feature, or noticed a pattern and appropriately tuned a particular method. Where those insights exist, they often overshadow any gains by super-sophisticated ML techniques.