Machine Learning Algorithms

You can learn how ML Algorithms work from MLOps standpoint

In this course, you will learn how Machine Learning algorithms work from the MLOps perspective. We won’t focus on building ML models, but on how to run them in production, and how model type affects deployment, monitoring, explainability, speed, and cost.

We’ll explore regression, decision trees, gradient boosting, clustering, and dimensionality reduction, plus core deep learning blocks like CNNs and MLPs, and practical SVMs and Naive Bayes. You’ll learn how to track model drift, choose the right metrics, plan safe rollouts, and handle issues in real systems with hands-on labs.

You’ll also see how model behavior connects to infrastructure, CI/CD, observability, and cost control. This course builds strong foundations for daily MLOps work with classic ML and essential deep learning models.

Course Content

  • Course Introduction
  • About Your Instructor
  • Course Structure
  • ML Algorithms in day-to-day activities of MLOps
  • Global usage trends in ML models
  • Overview of ML Models
  • GitHub repositories
  • What is Supervised Learning
  • What is Linear Models
  • Examples of Linear Models
  • Ops Features of Linear Models
  • Customer churn prediction, Hands-on Lab
  • What is Decision Trees & Random Forests
  • Examples of Decision Trees & Random Forests Models
  • Ops Features of Decision Trees & Random Forests Models
  • Loan approval classification, Hands-on Lab
  • What is Gradient Boosting Machines (GBMs)
  • Examples of Gradient Boosting Machines (GBMs) Models
  • Ops Features of Gradient Boosting Machines (GBMs)
  • Credit risk scoring system, Hands-on Lab
  • What is Unsupervised Learning
  • What is Clustering
  • Examples of Clustering
  • Ops Features of Clustering
  • Customer segmentation, Hands-on Lab
  • What is Dimensionality Reduction
  • Examples of Dimensionality Reduction
  • Ops Features of Dimensionality Reduction
  • Visualization of product recommendation embeddings, Hands-on Lab
  • What is Deep Learning
  • What is Feedforward Networks (MLPs)
  • Examples of Feedforward Networks (MLPs)
  • Ops Features of Feedforward Networks (MLPs)
  • Predicting sales from tabular features, Hands-on Lab
  • What is Convolutional Neural Networks (CNNs)
  • Examples of Convolutional Neural Networks (CNNs)
  • Ops Features of Convolutional Neural Networks (CNNs)
  • Manufacturing defect detection, Hands-on Lab
  • What is Specialized Models
  • What is SVMs
  • SVMs Examples
  • Ops Features of SVMs
  • Classifying legal documents, Hands-on Lab
  • What is Naive Bayes
  • Naive Bayes Examples
  • Ops Features of Naive Bayes
  • Spam filtering, Hands-on Lab
  • What factors should you consider when choosing the right model
  • Performance aspects of different ML models
  • MLOps Problems Related to Specific Algorithms
  • Trade-off matrix for ML models
  • Selecting models for three real-world case studies, Hands-on Lab

Start learning high demand tech skills today

About Your Instructor

Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.

In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.

You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.

All courses are developed by experienced instructors with over 10 years of real-world industry expertise. We focus on delivering practical, up-to-date content – not just collecting enrollments, so that every course gives you real value.

Our courses meet high academic standards, and we’re actively working on certification to ensure they align with recognized best practices.

Each course includes video lectures, hands-on labs with screen recordings, quizzes, reading materials, GitHub repository with real project code, and a capstone project. This structure is designed to help you build practical, in-demand skills and knowledge that employers care about.

However, if you’re not satisfied for any reason, you can request a refund in accordance with our Refund Policy – your satisfaction matters to us.

It’s not just skills. It’s your next chapter.

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