Certificate Program in
Machine Learning on Azure
- 24 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Training Aligned with Microsoft Certified: Azure Data Scientist Associate
- Complimentary Machine Learning Primer
- Complimentary Python Programming
Academic Partners & International Accreditations
Azure is a cloud-based environment that is used to manage, train, deploy, and track Machine Learning models without any coding involved. Using Azure, you will be able to create your machine learning models using its drag and drop interface. Machine learning technique allows computers to use data and predict outcomes, future behavior, trends, and also can make apps and devices smarter. Machine Learning is also being leveraged to solve business challenges in different industries. This course has been designed for Data Scientists, Developers, Analysts, students, and professionals who wish to excel in this exciting domain of Machine Learning. So, think no more and enroll in the course, ‘Machine Learning on Azure’, and launch your career in this growing field.
ML on Cloud
INR 40,460 28,320/-
Machine Learning on Azure Course Overview
Build models and learn the various parameters involved in designing models. Learn about the real-world applications of ML models and also how to train, manage, scale, and track them on Azure. This course will give an introduction to Machine Learning and how to build and deploy models faster. Learn to develop end-to-end workflow pipelines to build and track your assets. This course presents you with the concept and framework of cloud computing and the various machine learning services in Azure. You will learn about the benefits of cloud computing and also explore the Data Visualization capabilities of Azure. By the end of the course, you will be able to build predictive models using Machine learning services on Azure.
Define Machine Learning
Machine Learning is a branch of artificial intelligence that brings out the power of data and enables computer systems to learn and perform tasks automatically through detections and predictions. Machine learning applications are leveraged in everything from Virtual Assistant solutions to self-driving cars to robots to several other practical applications that drive the business world and the customer care industry. Professionals with the right skills in machine learning will be well-positioned because companies are ramping up their workforces to take full advantage of the benefits of Machine Learning.
Machine Learning on Azure Learning Outcomes
With machine learning, a huge amount of information can be processed to make decisions based on logic and data collected. Azure is a cloud-based platform that allows training, deploying, and managing Machine Learning models and in this course, you will be exposed to various tools and interfaces that are used to work on Azure Machine Learning. Students will learn to work with data pipelines to build ML solutions and use the collected data for experimentation and model training while learning to create a workspace in Azure. Students will gain hands-on experience in training a model and also prepare data for analyzing and making predictions using Azure. Students will be also able to demonstrate their skills in using the various tools and interfaces that help to solve large data processing projects. Join the course in Machine learning on Azure and explore the Extracting, Transforming, Loading Capabilities of Azure and also learn to
Block Your Time
Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data scientists, technology heads, decision-makers
- Professionals with analytics knowledge
- Professionals with industry domain experience in various areas (banking, finance, insurance, mechanical, IoT etc.)
Modules for Machine Learning on Azure Course
Each module encapsulates the essential tools to impart machine learning skills using the two most popular programming languages Python and R. You will learn how to establish a gateway between different databases with R and Python and also how to connect to external sources of data. One of the major modules of Data Science is Machine Learning. Learn about the various modules that make up Machine Learning using the two most popular tools R and Python. As a data scientist one will be engaged in a multitude of data mining techniques in both supervised and unsupervised learning. One of the major variants of the same is reinforcement learning that enables machines to learn through rewards. Get introduced to all the supervised techniques of prediction and classification; learn about the major unsupervised learning methods and the application of reinforcement learning in Data Mining.
Learn two of the most powerful programming languages used in Data Analytics. Both R and Python are the top two tools used by Data Analytics professionals world over. Start learning from the very basics, right from installation and work your way up through simple commands, writing small functions and programs.
Both R and Python can connect to a wide variety of data sources. Under this module, learn how to establish a gateway between different databases with R and Python. Also, learn how to connect to external sources of data.
One of the major modules of Data Science is Machine Learning. Learn about the various modules that make up Machine Learning using the two most popular tools R and Python. Get introduced to the broad overview of ML and the various quality metrics with the help of R and Python.
In the real world, oftentimes, the datasets cannot be used as such and some amount of preprocessing activity needs to be done. Imbalance in the output classes is one of the common problems where sometimes the proportion may be as lopsided as 95% to 5% or even higher. Learn about the various methods and algorithms to address this problem of imbalanced data sets.
One of the integral parts of learning Data Science and working on Analytics projects is the sound understanding of Statistical tools. In this module learn about the need to know statistical measures and their application in Data Science. Also, learn how to visualize the data in a concise form to derive various meaningful insights.
The essence of analytics is to be able to get the story from the data. And for the data to be able to truly be useful one needs to munge the raw data to make it legible. Using tools like R and Python, learn how to manipulate data from the raw form to make it ready for subsequent ML algorithms. The topic is all the more important in the current context given that a lot of data is moving to the unstructured format.
Both R and Python while being classified under the object-oriented programming languages category, still require some traditional approach to programming whereby the user-defined function needs to be spelled out and the use of conventional program snippets is of utmost importance. In this module, learn how to create simple to complex user-defined functions and hone your programming skills in the context of machine learning.
As a data scientist one will be engaged in a multitude of data mining techniques in both supervised and unsupervised learning. One of the major variants of the same is reinforcement learning that enables machines to learn through rewards. Under this module, get introduced to all the supervised techniques of prediction and classification; learn about the major unsupervised learning methods and the application of reinforcement learning in Data Mining.
Under supervised learning one of the most popular methods of predicting numeric data is linear regression and for classifying categorical data is logistic regression. These two methods will be covered in detail under this module. And the participant will be introduced to multiple examples using R and Python.
Under the classification modeling, decision trees method has a special place even though they are not truly classification modeling techniques but rule-based algorithms. The popularity of Decision trees is in its simplicity, high accuracy, and most important the ability to explain behind- the- scenes working of the algorithm. Under this module, the participant will be introduced to working with Decision trees using R and Python.
Trends in Machine Learning
A branch of Artificial Intelligence, Machine Learning is used to make predictions and drive valuable insight into business processes and operations. The power of Machine learning lies in the fact that it works independently of any explicit programming instructions. Some of the trends that we will see in machine learning include Regulation of Digital Data, the advent of many technologies has led to an acceleration in the supplement of data at an unprecedented pace. The question here is, does all the data that is being generated relevant? This is where Machine Learning paves its way to untangle the mystery by juggling with any amount of data by establishing cloud solutions and data centers. It simply filters the data and categorizes data by bringing up functional and relevant data for better efficiency.
Marketing is the core of every business as it promotes its visibility and presence, and helps survive the cut-throat competition. With Machine learning, businesses can extract insights that are valuable to design an effective marketing strategy by mining the data and evaluating the various research methods to maintain accuracy and formulate a successful business plan. Machine Learning technology also helps check malware attacks by automating complex tasks and providing multiple layers of protection and detects cyber-attacks on its own. In the future we are to see only added applications of this innovative technology, providing advanced solutions to business problems and imparting unprecedented customer satisfaction. Enroll for the course on Machine Learning on Azure, and take your career to new heights.
How We Prepare You
Additional Assignments of over 80+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Data Science Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Live Projects
Life Time Free Access to Industry Webinars
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