I once had to do a presentation for Hidden Markov Models at my university from scratch and had to self learn it. A good source for start understanding is the well known Artificial Intelligence: A Modern Approach (3rd Edition) book. Note that I am not suggesting you to read the whole book at all, there is a chapter that talks about HMM and I was fine on understanding the examples and the idea despite not having background on either AI or statistics.
Looking into research papers could be useful to grasp few examples, such as this one. I find the AI book example particular useful about having the idea of inferring the weather by only know someone is carrying or not a umbrella in a given day.
For implementation, see this. I used this code myself and actually tried to understand it on my own despite not knowing C++. The website provides examples you can see the theory being applied as well, which was very interesting for me. You might want to be skeptical on the AI book regarding the probability proofs before getting a full picture of HMM. I also noticed that one approach about HMM reasons more about matrixes and other is more visual such as the paper linked here that consider state machines. I find states machines easier to understand for beginners.