ML Programmer to ML Architect – e-learning with Skillsoft

Develop your skills from machine learning programming to the design and architecture of more advanced machine learning and deep learning solutions.

Register interest

ML Programmer to ML Architect is a comprehensive learning path from Skillsoft that builds skills step by step through four parts: ML Programmer, DL Programmer, ML Engineer and ML Architect.

The learning path covers linear regression, computational theory, training datasets, neural networks, CNNs, RNNs, predictive modeling, analysis and architecture of machine learning solutions, among other topics. The goal is to develop your skills from hands-on work with machine learning to a more holistic understanding of how ML solutions are designed and built.

You get access to 75 courses with more than 71 hours of learning content in total.

What do you get out of the e-learning bundle?

Through the learning path, you can, among other things:

  • Build machine learning skills from programming to architecture
  • Work with linear regression and key ML algorithms
  • Understand computational theory and how training data is used
  • Develop knowledge of neural networks and deep learning
  • Work with CNNs and RNNs
  • Understand predictive modeling and analysis
  • Develop and evaluate machine learning models
  • Understand how ML solutions can be designed as part of larger systems
  • Build skills toward a more architecture-oriented role in machine learning

Who is the bundle for?

The learning path is particularly suited to you if you already work in, or want to develop further within:

  • Machine learning
  • Deep learning
  • AI development
  • Data science
  • Software development
  • ML engineering
  • AI and ML architecture

It is especially well suited if you want a clear progression from practical model development to more advanced work in engineering and architecture.

Prerequisites

No mandatory prerequisites are specified for the learning path.

Before you start, there is an optional section with information about prerequisites and an introduction to the ML Architect role.

Course content

Track 1: ML Programmer

The first part focuses on key concepts in machine learning, including linear regression, computational theory and working with training datasets.

Track 2: DL Programmer

Here, your skills are built further in deep learning, with a focus on neural networks, CNNs, RNNs and machine learning algorithms.

Track 3: ML Engineer

This part is aimed at the development and engineering of machine learning solutions, with a focus on predictive modeling, analysis, ML modeling and architecture.

Track 4: ML Architect

The final part builds toward a more holistic architecture perspective and covers applied predictive modeling, CNNs, RNNs and ML algorithms, among other topics.