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NextiaNextia Learning

Learning path · Build AI

New graduate: practical ML and model deployment

Train machine learning models on real data, and serve them to users.

This is you

You studied computing, science or engineering, or you will finish soon. You can write some code. You want a first job where you train models on real data and put them in front of real users.

Where you are going

A junior machine learning engineer

  • Turn a business question into a prediction problem
  • Train and compare models, and choose one with evidence
  • Serve a model behind an API, in a container, in the cloud
  • Show work that an employer can run for themselves
  • 3 stages
  • 9 courses, 9 available now
  • About 103–138 hours of study
  • 9 certificates of completion

How much code you need

  1. No code
  2. Read code
  3. Some code
  4. Code is central

Code is central on this path: you write Python every week. If you are new to programming, the Python course starts from zero, and every lesson shows the full code.

Your route, stage by stage

Each stop is a course. Its shape tells you how much of it to take.

  • Full course. The whole course, with its practice. It leads to a certificate of completion.
  • Concepts. Only the concept modules, read without running code. They lead to a concepts certificate.
  • Optional. Useful for some people. You do not need it, and it is not counted in the time.
  1. You, today

  2. Stage 1 Foundationsabout 39–53 h

    1. AI Career Foundations: Roles, Skills and Roadmaps

      Full course4–6 h

      Choose your role, and plan your weeks.

    2. Python for Practical AI Engineering

      Full course18–24 h

      Python is the tool of every later course.

    3. Git and Team Development Essentials

      Full course5–7 h

      Save your work and share it as a team does.

    4. Essential Mathematics and Statistics for AI

      Full course12–16 h

      The numbers behind model results, with no proofs.

      Focus: If you already studied this, do its final assignment instead.

  3. Stage 2 Machine learningabout 38–48 h

    1. SQL and Data Preparation for AI

      Full course12–16 h

      Most machine learning work is getting the data right.

    2. Machine Learning: From Problem to Reliable Model

      Full course26–32 h

      The core of this path: from a question to a model that you can trust.

  4. Stage 3 Serve real usersabout 26–37 h

    1. Build and Deploy Your First API

      Full course11–15 h

      Put your model behind an API.

    2. Docker and Cloud Essentials for AI Developers

      Full course7–10 h

      Package it so that it runs the same everywhere.

    3. Deploying AI Models for Real Users

      Full course8–12 h

      Serve models to real users, and keep them fast.

  5. You arriveA junior machine learning engineer

Earn certificates as you go

You do not need to finish the whole path to show what you learned. Each full course gives a certificate of completion, and each concepts course gives a concepts certificate. Share each one when you get it.

When you are ready, prepare for the interview: Interview preparation: Machine Learning Engineer

Make it my path All learning paths

Choose this goal in your profile. My learning then shows your progress on this route, and the weeks it takes at your pace.