It is not easy

You’ll spend a lot of time searching resources. Just running an algorithm from a library isn’t enough. You have to understand the math. Any resource that tells you “you don’t need to learn math” is probably not worth it.

Machine learning is just one part of data science

Data science involves data pre-processing and cleansing, feature engineering, modeling, training, validation, and presentation of results.

Mostly, you will be occupied with preparing data sets

Extracting data, cleaning, aligning, mapping attributes. This is where most of the time goes.

Visualizations are important

Executives and product managers won’t look at your R console. Creating visualizations for both technical and non-technical audiences is crucial.

There are libraries for everything

Most popular algorithms are already implemented in R and Python. They serve as performance references.

Most of the valuable material is in books and papers

Online resources usually work only if you follow exactly what they say. Eventually you will start reading books.

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Following the path of machine learning is a big commitment. Be prepared for it.