Given the modern state of machine learning technologies and tools (e.g. TensorFlow, Theano, etc.), it seems like entry threshold have recently lowered and it is enough to be able to program on, say, Python, to build interesting things. Another source that supports this point is [Machine Learning Specialization on Coursera](https://www.coursera.org/specializations/machine-learning), that states the following in their FAQ:

> 
> What background knowledge is necessary?
> 
> 
> You should have some experience with computer programming; most assignments in this Specialization will use the Python programming language. This Specialization is designed specifically for scientists and software developers who want to expand their skills into data science and machine learning, but is appropriate for anyone with basic math and programming skills and an interest in deriving intelligence from data.
> 
> 
> 

On the other hand, there are plenty of other on-line courses (e.g. Stanford Machine Learning on Coursera or Google's Deep Learning on Udacity), as well as classical books, like S. Haykin, *Neural Networks: A Comprehensive Foundation*, packed with mathematics. Even though I was studying math for several years in university, including statistics, matrices, integral calculus and so on, it's been so long unused that I feel despair by merely looking at those equations. Even *Concrete Mathematics* by Knuth is discerned at such slow pace that it is seems impossible to finish it at all.

Thus, the following questions arise:

1. **Is it reasonable for someone with shallow knowledge in math but programming skills to dive into neural network/machine learning studies?**
2. **Is it possible to build interesting projects in this area, like those [playing atari](http://www.pcworld.com/article/2889432/google-ai-program-masters-classic-atari-video-games.html), using only high-level tools?**

Or would it take so much time, that it is better not to make oneself struggle and do something else?