Machine Learning Engineer for Microsoft Azure

A machine learning engineer needs to follow an iterative process to train the machine learning model which requires computing resources and takes much time. However, with the use of automated machine learning tools, these things become easier in the design and development process of modern applications that depend on predictive machine learning models. Furthermore, in this machine learning engineer course for Microsoft Azure, you will learn to create and publish models with no-code techniques by developing your skills in Azure Machine Learning.

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Microsoft Azure Machine Learning Course – Overview

In this machine learning course for Microsoft azure, students will learn to use popular open-source tools and frameworks through which they will be able to deliver sophisticated machine learning solutions. In this Microsoft Azure machine learning program, you’ll be completely dedicated to boosting your skills with the various available azure tools. This enables you to deal with complex machine learning tasks and thus, gain hands-on practical experience.

Azure Machine Learning Online Course – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.
Azure ML – Skills Covered: Automated Machine Learning. Azure Machine Learning Designer. Create a Regression Model. Classification Model with Azure AI. Clustering Model with Azure AI.

Microsoft Azure Machine Learning Training – Benefits

This program enables you to acquire deeper technical skills and dedicate yourself to the development of intricate machine learning models. Therefore, with this advanced knowledge, you can aim for better and high-level positions.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Machine Learning Engineer

UK
Hiring Companies

Azure Machine Learning Online Course – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the Azure ML course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team’s needs

  • 1-year access to the Azure ML course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

Machine Learning Engineer for Microsoft Azure Course Online – Curriculum

Eligibility

Students or working professionals who have a keen interest in machine learning and planning to make a career in the machine learning domain can join this Microsoft azure machine learning course online. Software developers and business analysts who want to make a career switch can get through this training program and become machine learning engineers.

Pre-requisites

In order to get into the right pace of learning during the course period, you should have the knowledge of the basic python programming concepts, algebra, and fundamental statistics, fundamental machine learning concepts, and an understanding of azure basics. However, before getting into this advanced-level course, check out our machine learning course online.

Course Content

  • 1.001 Learn the difference between Regression and Classification
  • 1.002 Train a Linear Regression model to predict values
  • 1.003 Learn to predict states using Logistic Regression
  • 2.001 Learn the definition of a perceptron as a building block for neural networks, and the perceptron algorithm for classification.
  • 3.001 Train Decision Trees to predict states
  • 3.002 Use Entropy to build decision trees, recursively
  • 4.001 Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm.
  • 4.002 Train models using Bayesian Learning
  • 4.003 Complete an exercise that uses Bayesian Learning for natural language processing
  • 5.001 Learn to train a Support Vector Machines to separate data,linearly
  • 5.002 Use Kernel Methods in order to train SVMs on data that is not linearly separable
  • 6.001 Build data visualizations for quantitative and categorical data.
  • 6.002 Create pie, bar, line, scatter, histogram, and boxplot charts.
  • 6.003 Build professional presentations.
  • 7.001 Learn about different metrics to measure model success.
  • 7.002 Calculate accuracy, precision, and recall to measure the performance of your models.
  • 6.001 Train and test models with Scikit-learn.
  • 6.002 Choose the best model using evaluation techniques like cross-validation and grid search.
  • 1.001 Learn the foundations of deep learning and neural networks.
  • 1.002 Implement gradient descent and backpropagation in Python.
  • 2.001 Implement gradient descent using NumPy matrix multiplication.
  • 3.001 Learn several techniques to effectively train a neural network.
  • 3.002 Prevent overfitting of training data and learn best practices for minimizing the error of a network.
  • 4.001 Learn how to use PyTorch for building deep learning models.
  • 1.001 Learn the basics of clustering data
  • 1.002 Cluster data with the K-means algorithm
  • 2.001 Cluster data with Single Linkage Clustering.
  • 2.002 Cluster data with DBSCAN, a clustering method that captures the insight that clusters are dense group of points
  • 3.001 Cluster data with Gaussian Mixture Models
  • 3.002 Optimize Gaussian Mixture Models with and Expectation Maximization
  • 4.001 Reduce the dimensionality of the data using Principal
  • 4.002 Component Analysis and Independent Component Analysisv

Machine Learning Engineer Course for Microsoft Azure – FAQs

Managing and utilizing big data is a quite difficult and challenging task. However, Microsoft Azure machine learning helps you to build advanced analytics solutions easily. It provides an easy and flexible building interface for machine learning engineers and enables easy documentation maintenance for machine learning solutions.

Microsoft Azure machine learning is a cloud-based service platform that enables data scientists, machine learning professionals and engineers to accelerate and manage the machine learning project lifecycle. Microsoft Azure machine learning is used to manage MLOps by training and deploying models in their daily workflows.

One can learn Microsoft azure for machine learning with no programming knowledge. However, you need to write a small deployment script or configuration code in situations where you need to deploy an application to azure. Furthermore, Microsoft Azure can be easily used for infrastructure management activities and other tasks.

Azure helps in the processing of large data projects and gives you predictive arrangements of information. It saves time for a machine learning engineer and also minimizes the coding requirements. It allows you to use drag and drop features to create experiments and publish data models in less time. Therefore, learning Microsoft azure for machine learning is an added advantage.

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