Course Overview

Have you tried to learn about machine learning but ended up spending most of your time writing and debugging your code? Learning about machine learning can often be intimidating because students are expected to know R or Python, or at least learn it as they go. While these programming languages are absolutely essential to anyone seriously contemplating a career in data science, they can sometimes hold us back or derail us when our primary objective is to understand machine learning as a process. R and Python can be used in Azure ML Studio to give us more power and flexibility, but we not will emphasize them beyond providing a few examples.

It allows you to learn how to think like a data scientist, learn about machine learning concepts and best practices without being held back by the complexities of any particular programming language (usually R or Python). While we do cover some programming examples just to familiarize you, no background in programming is needed to take this course. Instead, come prepared to learn concepts and best practices and consolidate them through hands-on exercises in the Azure ML Studio.

Key Learning Areas

After completing this course, you will have a deeper understanding of Machine Learning and how data scientists think and talk about building ML solutions. We will introduce you to some of the most fundamental machine learning algorithms, such as linear regression and decision trees. However, this course is *not* about learning specific ML algorithms and the ones we introduce are just to help build a good intuition around them. Instead, we focus our attention on the practice of machine learning and cover concepts such as:

    • What is the data science process life cycle?
    • What are the different types of ML algorithms (or models)?
    • How do we train, test and evaluate a model?
    • How do we choose one model versus another?
    • What do we mean by model deployment or operationalization?

Labs are a big part of this course and we recommend that you take them seriously and take the time to work on them. Even if your ultimate goal is not to build ML models in person, the labs will really solidify your knowledge and understanding of the concepts we cover in the course.

Course Outline

  • Basic Overview
  • Azure Machine Learning
  • Just Enough Statistics
  • Exploratory Data Analysis (EDA)
  • Machine Learning Through Basic Examples
  • k-Means Clustering
  • Concepts in Machine Learning
  • Model Building and Evaluation

Who Benefits

The audience for this course can be anyone as there are no pre-requisites, but here are some scenarios to consider:

  • You are a data scientist and want to know how to leverage the Azure ML Studio in your work.
  • You are an aspiring data scientist who needs to learn the fundamentals of machine learning without being bogged down by specifics of languages like R or Python.
  • You are a solutions architect and need to have a more solid understanding of the data science process.
  • You are a developer, architect, or sales engineer and need to have a fundamental understanding of data science which would allow you to interface with data scientists and speak their language.

Prerequisites

This course is for trainers re-delivering this training. There are no prerequisites for this course other than basic algebra.