A machine learning engineer takes models from a notebook to a system that runs reliably for real users. You clean and shape data, train and compare models, then package, deploy and monitor them. The job sits between data science and software engineering, so you need math and statistics, solid Python, and good engineering habits. Most US employers want a bachelor's degree in a quantitative or computing field, but a public portfolio of end-to-end projects counts for a lot. Everything below uses free resources. The plan assumes 8-10 hours a week: learn a concept, apply it to a small real dataset,
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Stage 1 of 4 · about 14 weeks
Learn Python, NumPy and pandas, SQL, Git and the command line (Bash). Cover the math you need: linear algebra (vectors, matrices), basic calculus (gradients), probability and descriptive statistics. Finish by cleaning a messy public dataset and plotting 5 clear findings with matplotlib.
Python and the command line with Git
Every ML task you do later, from cleaning data to training models, is written in Python and run from a terminal, with code saved in Git. If these feel natural, everything after gets easier. O*NET lists Bash and data-oriented programming languages among the tools this job uses.
NumPy and pandas for data wrangling
The first core task in this job is to clean, manipulate and process large sets of data. Real data is messy: missing values, wrong types, duplicates. pandas is the tool most data people use to fix this.
SQL for getting data
Most company data lives in databases, not CSV files. Amazon Redshift and Apache Hive appear in the O*NET software list, and both are queried with SQL. Almost every data interview tests it.
Math for ML: statistics, probability, linear algebra, gradients
O*NET ranks Mathematics as the top skill and knowledge area for this job. You do not need a math degree, but you must understand what models are doing so you can compare them and spot when results are wrong.
Visualizing and communicating findings
A core task is to create graphs and charts that convey results and to present them to people who make decisions. A finding nobody understands changes nothing.
Then 3 more stages (about 88 weeks in all) with lessons, drills, projects, 10 interview questions with strong answers and free resources.