I'm very new to machine learning and want to understand the general process by which it is carried out. I've worked through the famous 'iris' tutorial and want to ask if the principles in that tutorial are applicable to every future machine learning project I undertake.
I work in biological sciences, and am interested in applying machine learning algorithms to biological sequence data as away of categorizing classes or doing unsupervised clustering. From my understanding, each project starts with:
- Me defining the 'scope' or aim of what I want to learn from the raw data.
- Generating features/attributes from my OWN code/algorithms that could potentially differentiate between the two classes
- This basically generates a huge matrix of x_features by y_entries
- Feed the matrix into a machine learning algorithm (I'm sure this is vastly oversimplified).
To give an example, say I have 10,000 protein sequences, and I believe 5000 are 'Class1' and 5000 are 'Class2', but I do not know how to differentiate them by eye. I need to generate x_features (in some informed way) of this sequence using my own custom algorithms, and feed the resulting 10000 entries into an algorithm.
Is this the right approach? I'd be eternally grateful if someone could direct me towards a beginners tutorial that revolves around analyzing biological sequence data.