The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly.
However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.
NNs are much older than this chart. They aren't terribly good at problems like this because they tend to overfit more than other methods. They need lots of data to generalize well. They only really excel when the data has regular structure that can be exploited by weight sharing (like CNNs to images or RNNs to time series.)
Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496
However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.