Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed.
Machine learning (ML), which is a subset of Artificial Intelligence (AI), has taken giant leaps in the last two years. Often, a Machine Learning Engineer will partner with a Data Scientist, and current demand for these candidates is extremely high. While the Data Scientist will be expected to be stronger in statistics and analytics, the Machine Learning Engineer will be an expert in computer science, and generally much stronger at coding.
Machine Learning requires that professionals are proficient in math, data science, and software engineering, and also requires skills in probability, statistic, and data modeling. Additionally, since Machine Learning involves creating dynamic algorithms, programming and software development skills are vital. And when it comes to programming languages, Machine Learning isn't bound to any one specific language; however, the most common include Python, R, and even C/C++.
Available Machine Learning Courses
Here's a closer look at our Machine Learning course offerings:
20773 Analyzing Big Data with Microsoft R
The main purpose of this three-day course is to give students the ability to use Microsoft R Server to create and run an analysis on a large dataset. Students will also learn how to utilize it in Big Data environments, such as a Hadoop or Spark cluster, or a SQL Server database.
20774 Perform Cloud Data Science with Azure Machine Learning
The goal of this five-day course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools, such as HDInsight and R Services.
Introduction to R Programming
This two-day course, which is geared toward Business Analysts, Technical Managers, and Programmers will help students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs, which allow attendees to immediately apply their theoretical knowledge in practice.
Python With Data Science
This two-day course covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.
Associated Microsoft Certifications
Earning an MCSA: BI Reporting demonstrates knowledge relevant to data analysis, data visualization, modeling, dashboards, and direct connectivity to data sources in Excel and Power BI. It is the first step on your path to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).
Courses and Exams for the MCSA: BI Reporting:
Microsoft Certified Solutions Associate (MCSA): Machine Learning
Earning an MCSA: Machine Learning demonstrates knowledge relevant to Machine Learning, Data Scientists and Data Analysts positions, particularly those who process and analyze large data sets using R and use Azure cloud services to build and deploy intelligent solutions. It is the first step on your path to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE). This certification retires on June 30, 2019.
An MCSA: Machine Learning certification demonstrates the knowledge to use machine learning to process and analyze big data using Microsoft Azure, R Server, and SQL R. Typical job roles for certification holders include data scientist and business intelligence analyst. See required exams and associated courses below:
Microsoft Certified Solutions Expert (MCSE): Database Management & Analytics
Demonstrate your broad skill sets in SQL administration, building enterprise-scale data solutions, and leveraging business intelligence data—both on-premises and in cloud environments. Earning an MCSE: Data Management and Analytics certification qualifies you for such positions as database analyst, database designer, and business intelligence analyst.
Once you obtain an associated Microsoft Certified Solutions Associate (MSCA), pass any ONE of the following elective exams to earn an MCSE: Database Management & Analytics:
- 70-464: Developing Microsoft SQL Server Databases
- 70-465: Designing Database Solutions for Microsoft SQL Server
- 70-466: Implementing Data Models and Reports with Microsoft SQL Server
- 70-467: Designing Business Intelligence Solutions with Microsoft SQL Server
- 70-473: Designing and Implementing Cloud Data Platform Solutions
- 70-475: Designing and Implementing Big Data Analytics Solutions
- 70-762: Developing SQL Databases
- 70-767: Implementing a Data Warehouse using SQL
- 70-768: Developing SQL Data Models
- 70-773: Analyzing Big Data with Microsoft R
- 70-774: Perform Cloud Data Science with Azure Machine Learning
- 70-775: Perform Data Engineering on Microsoft Azure HDInsight
Note that Microsoft will change the required elective exams every calendar year.