There is a wide range of different areas in Artificial Intelligence, and one of them is Machine Learning. However, before talking about particular models, algorithms, and other related concepts, it is important to understand what Machine Learning is in general.

In short, Machine Learning is the way to build systems that learn some pattern from the data and then use it to make decisions without being programmed for each individual case specifically. Instead of writing the whole rule, we simply give the system data and tell it what to learn, and then the algorithm extracts some useful patterns. It seems pretty simple, but there is a lot going on underneath.

It is much easier to learn Machine Learning if you already know the basics of programming in Python, how to handle data, math, and statistics. Specifically, you should have the knowledge of variables, functions, loops, arrays, etc., and be able to program in Python. NumPy and Pandas will help you to work with numeric and tabular data respectively, and Matplotlib and Seaborn will enable you to visualize your data.

Also, math starts becoming very relevant at this stage. It is needed to understand the data and distributions with statistics, reason about uncertainties with probability, represent data mathematically using linear algebra, and optimize the models using calculus. You do not need to know everything about math before Machine Learning, however, the foundation will make the algorithms much more understandable.

It is not just a list of algorithms that is called Machine Learning. Before talking about individual models, I believe that it is more important to know the core principles behind them. First of all, we need the data. It is important to understand the contents of the dataset, useful features, and whether there are any issues with the data, such as missing or wrong values, or whether it needs some transformation before the training process.

The next thing is the learning problem. In supervised learning, the model learns on examples with already known output. Such problems include regression and classification. In unsupervised learning, the model works with unlabeled data and discovers some useful structures, for example, groups or clusters.

And now we come to the training and evaluation of the models. It is possible to have a great performance of the model on the data it has seen and poor performance on the unseen data. At this stage, the concepts of training data, validation data, test data, overfitting, underfitting, bias, variance, and evaluation metrics become crucial.

My plan is not just memorizing the list of algorithms and running them from the library. I want to understand why such algorithm exists, what problem does it solve, how it learns from the data, what assumptions it makes, and where it might fail.

I'll start with the foundation and gradually move to the regression, classification, tree-based models, nearest neighbor, support vector machines, clustering, dimensionality reduction, and model evaluation. On the way, I'll implement models, experiment with the real data, visualize results, and compare approaches. My only goal is very simple: not just knowing how to use Machine Learning, but understand what happens when the model is learning. This is the point when the math, programming, and data foundations start merging into something bigger. And this is the point where the real AI/ML journey starts.