Microsoft Azure Data Scientist (DP-100)

Paths

Microsoft Azure Data Scientist (DP-100)

Authors: Paul Foran, Emilio Melo, Benjamin Culbertson, Janani Ravi, Jared Rhodes, Axel Sirota, Neeraj Kumar, Bismark Adomako, Ravikiran Srinivasulu, Mike West, Xavier Morera, Michael Heydt, David Tucker, Steph Locke, Saravanan Dhandapani, Tim Warner

Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable... Read more

What you will learn

  • Address the business requirements for a data science projects
  • Source, collect, and transform data into shapes appropriate for data modeling and machine learning
  • Extract features from complex data sources, such as documents and images
  • Build and interpret statistical and machine learning models
  • Glean insights from data, and communicate them back to the business

Pre-requisites

This path is intended for learners familiar with data science workflows and general principles, but who have never applied these on Microsoft Azure.

Bringing Data Science to the Business

The courses in this section teach you how data science fits into a business, and addresses legal and ethical issues that arise in data science. In addition, this part of the path discusses the role of effective communication as part of the data science workflow.

Analyzing Business Requirements for Data Science

by Paul Foran

Nov 13, 2019 / 50m

50m

Start Course
Description

Data Science has certainly become a ‘hot topic’ for all businesses. Investing in Data Science activities for your business can certainly yield some hidden gems in your data sets and lead to valuable IP or improvements in operational efficiencies for your organization! In this course, Analyzing Business Requirements for Data Science, you will learn the strategic, practical and technical skills to discover if data science can indeed benefit your organization. First, you will see how to gather and manage the right stakeholders with a view to providing a solid Data Science story to your company that adds REAL business value. Next, you will discover how to determine and risk assess your data science business and technical requirements. Finally, you will explore how to quantify the business problems and learn what to do when mitigating the risk of a project not yielding business value. When you're finished with this course, you will have the skills and knowledge of Analyzing business requirements for data science needed to guide you through the process of managing and delivering a data science project to your organization

Table of contents
  1. Course Overview
  2. Determining if Data Science Is an Appropriate Fit for the Organization
  3. Quantifying the Business Problem

Understanding Ethical, Legal, and Security Issues in Data Science

by Emilio Melo

Aug 11, 2020 / 1h 14s

1h 14s

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Description

With all the scandals hitting the news related to privacy, security breaches and bias issues, understanding how to properly manage data has become an increasingly demanded skill on the business world. In this course, Understanding Ethical, Legal, and Security Issues in Data Science, you will gain the ability to handle Azure-hosted data in a secure, highly available and compliant way. First, you will learn about the Shared Responsibility Model in the cloud, and your duties on this model. Next, you will discover authentication and authorization options in Azure, as well as how to maintain the data highly available. Finally, you will explore how to handle legal, compliance and ethical aspects in Azure. When you’re finished with this course, you will have the skills and knowledge of Data Management needed to host your workloads in the Microsoft cloud.

Table of contents
  1. Course Overview
  2. Authentication and Authorization Methods
  3. Determining Data Availability
  4. Assessing Ethical and Legal Data Compliance

Communicating Expectations to the Business

by Benjamin Culbertson

Sep 20, 2019 / 43m

43m

Start Course
Description

Discover critical skills in communicating barriers and solutions to data acquisition for model training. In this course, Communicating Expectations to the Business, you will learn foundational knowledge that will aid you in managing stakeholders' expectations of data science, machine learning, and augmented intelligence solutions. First, you will learn what is needed for a data science solution. Next, you will discover the four main sources of historical data that can be used to train models for a solution that will generate insights that will be used by a team, and what barriers you may encounter in acquiring that data. Then, you will examine an innovative solution, synthetic data generation, that will aid in transforming existing data while maintaining the data's character, personality, and richness. Finally, you will explore how to communicate solutions and expectations to stakeholders on data availability and formatting, and ask for a go/no-go decision. When you're finished with this course, you will have the skills and knowledge of communicating challenges around availability of data, and strategies needed to overcome barriers to bring needed historical data to your data science and machine learning solution.

Table of contents
  1. Course Overview
  2. Reviewing Available Data with Stakeholders
  3. Communicating Barriers to Data Access to Stakeholders
  4. Recommending Next Steps Based on Available Data

Preparing Data for Analysis and Modeling

This section teaches you how to source, clean, and shape your data for further analysis in Microsoft Azure.

Representing, Processing, and Preparing Data

by Janani Ravi

Jun 19, 2019 / 2h 44m

2h 44m

Start Course
Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. As the process of actually constructing models becomes democratized, the general view is shifting toward using the right data and using the data right.

In this course, Representing, Processing, and Preparing Data, you will gain the ability to correctly represent information from your domain as numeric data, and get it into a form where the full capabilities of models can be leveraged.

First, you will learn how outliers and missing data can be dealt with in a theoretically sound manner.

Next, you will discover how to use spreadsheets, programming languages and relational databases to work with your data. You will see the different types of data that you may deal with in the real world and how you can collect and integrate data to a common destination to eliminate silos.

Finally, you will round out the course by working with visualization tools that allow every member of an enterprise to work with data and extract meaningful insights.

When you are finished with this course, you will have the skills and knowledge to use the right data sources, cope with data quality issues and choose the right technologies to extract insights from your enterprise data.

Table of contents
  1. Course Overview
  2. Understanding Data Cleaning and Preparation Techniques
  3. Preparing Data for Analysis Using Spreadsheets and Python
  4. Collecting Data to Extract Insights
  5. Loading and Processing Data Using Relational Databases
  6. Representing Insights Obtained from Data

Sourcing Data in Microsoft Azure

by Jared Rhodes

Dec 11, 2019 / 1h 12m

1h 12m

Start Course
Description

The cloud has nearly infinite compute power for processing. In this course, Sourcing Data in Microsoft Azure, you'll learn foundational knowledge of data types, data policy, and finding data. First, you'll learn how to register data sources with Azure Data Catalog. Next, you'll discover how to extract, transform, and load data with Azure Data Factory. Finally, you'll explore how to set up data processing with Azure HD Insight. When you're finished with this course, you'll have the skills and knowledge of the tools and processes needed to source data in Microsoft Azure. Software required: Microsoft Azure portal.

Table of contents
  1. Course Overview
  2. Identifying Potential Data Sources
  3. Extracting and Loading Data into an Azure Workflow

Cleaning and Preparing Data in Microsoft Azure

by Jared Rhodes

Dec 16, 2019 / 1h 7m

1h 7m

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Description

No data science project of merit has ever started with great data ready to plug into an algorithm. In this course, Cleaning and Preparing Data in Microsoft Azure, you'll learn foundational knowledge of the steps required to utilize data in a machine learning project. First, you'll discover different types of data and languages. Next, you'll learn about managing large data sets and handling bad data. Finally, you'll explore how to utilize Azure Notebooks. When you're finished with this course, you'll have the skills and knowledge of preparing data needed for use in Microsoft Azure. Software required: Microsoft Azure.

Table of contents
  1. Course Overview
  2. Transforming Data into Usable Datasets
  3. Wrangling Data

Combining and Shaping Data

by Janani Ravi

Jun 21, 2019 / 3h 27m

3h 27m

Start Course
Description

Connecting the dots between data from different sources is becoming the most sought-after skill these days for everyone ranging from business professionals to data scientists.

In this course, Combining and Shaping Data, you will gain the ability to connect the dots by pulling together data from disparate sources and shaping it so that extracting connections and relationships becomes relatively easy.

First, you will learn how the most common constructs in shaping and combining data stay the same across spreadsheets, programming languages, and databases.

Next, you will discover how to use joins and vlookups to obtain wide datasets, and then use pivots to shape that into long form. You will then see how both long and wide data can be aggregated to obtain higher level insights. You will work with Excel spreadsheets and SQL as well as Python.

Finally, you will round out the course by integrating data from a variety of sources and working with streaming data, which helps your enterprise gain real-time insights into the world around you.

When you are finished with this course, you will have the skills and knowledge to pull together data from disparate sources, including from streaming sources, to construct integrated data models that truly connect the dots.

Table of contents
  1. Course Overview
  2. Exploring Techniques to Combine and Shape Data
  3. Combining and Shaping Data Using Spreadsheets
  4. Combining and Shaping Data Using SQL
  5. Combining and Shaping Data Using Python
  6. Integrating Data from Disparate Sources into a Data Warehouse
  7. Working with Streaming Data Using a Data Warehouse

Building Statistical Models in Microsoft Azure

The courses in this section apply descriptive and inferential statistics to data using Microsoft Azure.

Summarizing Data and Deducing Probabilities

by Janani Ravi

Jun 20, 2019 / 2h 47m

2h 47m

Start Course
Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. So, it is really important that you know your data well.

In this course, Summarizing Data and Deducing Probabilities, you will gain the ability to summarize your data using univariate, bivariate, and multivariate statistics in a range of technologies.

First, you will learn how measures of mean and central tendency can be calculated in Microsoft Excel and Python. Next, you will discover how to use correlations and covariances to explore pairwise relationships. You will then see how those constructs can be generalized to multiple variables using covariance and correlation matrices.

You will understand and apply Bayes' Theorem, one of the most powerful and widely-used results in probability, to build a robust classifier.

Finally, you will use Seaborn, a visualization library, to represent statistics visually.  

When you are finished with this course, you will have the skills and knowledge to use univariate, bivariate, and multivariate descriptive statistics from Excel and Python in order to find relationships and calculate probabilities.

Table of contents
  1. Course Overview
  2. Understanding Descriptive Statistics for Data Analysis
  3. Performing Exploratory Data Analysis in Spreadsheets
  4. Summarizing Data and Deducing Probabilities Using Python
  5. Understanding and Applying Bayes' Rule
  6. Visualizing Probabilistic and Statistical Data Using Seaborn

Experimental Design for Data Analysis

by Janani Ravi

Jun 20, 2019 / 2h 45m

2h 45m

Start Course
Description

Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. Now, a point-of-view must represent a hypothesis, ideally backed by data. In this course, Experimental Design for Data Analysis, you will gain the ability to construct such hypotheses from data and use rigorous frameworks to test whether they hold true. First, you will learn how inferential statistics and hypothesis testing form the basis of data modeling and machine learning. Next, you will discover how the process of building machine learning models is akin to that of designing an experiment and how training and validation techniques help rigorously evaluate the results of such experiments. Then, you will round out the course by studying various forms of cross-validation, including both singular and iterative techniques to cope with independent, identically distributed data and grouped data. Finally, you will also learn how you can refine your models using these techniques with hyperparameter tuning. When you’re finished with this course, you will have the skills and knowledge to build and evaluate models, specifically including machine learning models, using rigorous cross-validation frameworks and hyperparameter tuning.

Table of contents
  1. Course Overview
  2. Designing an Experiment for Data Analysis
  3. Building and Training a Machine Learning Model
  4. Understanding and Overcoming Common Problems in Data Modeling
  5. Leveraging Different Validation Strategies in Data Modeling
  6. Tuning Hyperparameters Using Cross Validation Scores

Interpreting Data with Statistical Models

by Axel Sirota

Sep 28, 2020 / 2h 48m

2h 48m

Start Course
Description

Data is everywhere, from the newspaper you read on the subway to the report you are using to analyze yesterday's stock market performance. In this course, Interpreting Data with Statistical Models, you will gain the ability to effectively understand how to tackle problems that appear at your work, understand which is the right statistical analysis to use, and how to interpret the results to obtain insights. First, you will learn the very basics of statistics. Next, you will discover hypothesis testing to compare variables. Finally, you will explore how to make multiple comparisons and detect functional relationships with ANOVA and Regression. When you’re finished with this course, you will have the skills and knowledge of data analysis and statistical models needed to make your data speak for itself.

Table of contents
  1. Course Overview
  2. Thinking Like a Statistician
  3. Testing a Hypothesis
  4. Comparing Categorical Values with Frequency Analysis
  5. Analyzing Experiments with ANOVA
  6. Comparing Groups and Effects with ANOVA
  7. Predicting Linear Relationships with Regression
  8. Predicting Non-linear Relationships with Regression

Interpreting Data with Advanced Statistical Models

by Axel Sirota

Sep 10, 2019 / 3h 9m

3h 9m

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Description

When you look at the core of machine learning, there are advanced statistical models. In this course, Interpreting Data with Advanced Statistical Models, you will gain the ability to effectively understand how to create an ML application that will be able to revolutionize the problems that appear at your work. First, you will learn the basic of Machine learning. Next, you will discover linear regression in a more general pattern, expanding to multiple and polynomial features. Continuing, you will explore how to classify with Logistic Regression, SVMs, and Bayesian methods. Finally, you will learn the intrinsic patterns of data with unsupervised techniques such as K Means and PCA. When you’re finished with this course, you will have the skills and knowledge of Machine Learning needed to apply it in a real-world application.

Table of contents
  1. Course Overview
  2. Getting Started with Machine Learning
  3. Finding Those Models
  4. Predicting Linear Relationships with Regression
  5. Understanding Regression Models in Depth
  6. The Problem of Correct Classification
  7. Large Margin and Bayesian Classification
  8. The Subtle Art of Not Needing Labels: Unsupervised Learning

Communicating Data Insights

by Janani Ravi

Jun 21, 2019 / 2h 26m

2h 26m

Start Course
Description

Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. In this course, Communicating Data Insights you will gain the ability to summarize complex information into such clear and actionable insights. First, you will learn how to sum up the important descriptive statistics from any numeric dataset. Next, you will discover how to build and use specialized visual representations such as candlestick charts, Sankey diagrams and funnel charts in Python. You will then see how the data behind such representations can now be fed in from enterprise-wide sources such as data warehouses and ETL pipelines.

Finally, you will round out the course by working with data residing in different public cloud platforms, and even in a hybrid environment, that is with some of it on-premise and some of it on the cloud.

When you’re finished with this course, you will have the skills and knowledge to pull together data from disparate sources and use nifty visualizations to convey crisp, actionable points-of-view to a senior executive audience.

Table of contents
  1. Course Overview
  2. Communicating Insights from Statistical Data
  3. Communicating Insights from Business Data
  4. Visualizing Distributions and Relationships in Data
  5. Integrating Data in a Multi-cloud Environment
  6. Integrating Data in a Hybrid Environment

Exploring and Modeling Data in Microsoft Azure

This part of the path teaches how to leverage Azure services as part of everyday data science, including the use of notebooks, data exploration tools, and model building.

Building Your First Data Science Project in Microsoft Azure

by Jared Rhodes

Jun 19, 2020 / 1h 25m

1h 25m

Start Course
Description

The past five years have shown a boom in the data science field with advancements in hardware and cloud computing. In this course, Building Your First Data Science Project in Microsoft Azure, you will learn about data science and how to get started utilizing it in Microsoft Azure. First, you will learn the data science and the tools surrounding it. Next, you will discover how to create a development environment in Microsoft Azure. Finally, you will explore how to maintain and utilize that development environment. When you are finished with this course, you will have the skills and knowledge of data science to build your first data science project in Microsoft Azure. Software required: Microsoft Azure

Table of contents
  1. Course Overview
  2. Determining Which Tools to Use
  3. Setting up a Development Environment
  4. Using the New Development Environment

Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer

by Neeraj Kumar

Sep 12, 2019 / 2h 45m

2h 45m

Start Course
Description

Businesses around the world are making huge investments in data analytics to be able to make critical decisions for steering growth and remain competitive. Data science is the ability to capture and process raw data, and then analyze, visualize, and communicate the processed information as insights to the stakeholders that brings in the value by enabling businesses in making critical decisions. In this course, Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer, you will use Azure Data Explorer to perform data exploration services. First, you will familiarize yourself with data exploration service in Azure. Next, you will learn how to build the Azure Data Explorer environment from Azure Portal as well as using PowerShell and CLI. Then, you will discover how to use the Kusto Query Language to perform time series analysis, followed by how data ingestion is performed using EventHubs and EventGrids. Finally, you will explore how to manage Data Explorer cluster performance and database permissions. When you’re finished with this course, you will have all the necessary skills and confidence to take your organization to the next level as a data scientist.

Table of contents
  1. Course Overview
  2. Azure Data Explorer (ADX) Overview
  3. Building the ADX Environment
  4. Using the Kusto Query Language (KQL)
  5. Data Ingestion for ADX
  6. Managing Azure Data Explorer

Building, Training, and Validating Models in Microsoft Azure

by Bismark Adomako

Oct 5, 2020 / 1h 42m

1h 42m

Start Course
Description

Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.

Next, you will quickly get Azure ML set up and learn why you need to split data for training and testing the models.

Then, you will explore the dependent and independent variables, which independent variables should be picked, why they should be picked, as well as feature data conversion such as label encoding and feature scaling.

Finally, you will discover which models to choose and why before obtaining the score of the model which will show how we can optimize the model and re-test.

When you are finished with this course, you will be ready to put your own model into production and monitor and retrain that model when necessary.

Table of contents
  1. Course Overview
  2. Creating a Hypothesis
  3. Sourcing and Transforming Data Relevant to a Hypothesis
  4. Identifying Features from Raw Data
  5. Building the Model
  6. Monitoring and Managing the Performance of a Model

Feature Engineering in Microsoft Azure

Data must be represented in a manner appropriate for the analysis or model being used. This part of the path addresses feature engineering and feature extraction on Microsoft Azure.

Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure

by Ravikiran Srinivasulu

Dec 16, 2019 / 2h 19m

2h 19m

Start Course
Description

Data comes from many different sources. So when you join them, they are naturally inconsistent. In this course, Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure, you will be taken on a journey where you begin with data that's unsuitable for machine learning and use different modules in Azure Machine Learning to clean and preprocess the data. First, you will learn how to set up the data and workspace in Azure Machine Learning. Next, you will discover the role of feature engineering in machine learning. Finally, you will explore how to Identify specific data-level issues for machine learning models. When you’re finished with this course, you will have a clean dataset processed with azure machine learning modules that’s ready to build production-ready machine learning models.

Table of contents
  1. Course Overview
  2. Getting Started with Azure Machine Learning
  3. Differentiating Data, Features, Targets, and Models
  4. Preparing Input Data for Machine Learning Models
  5. Handling Missing Data
  6. Role of Feature Engineering in Machine Learning
  7. Split a Data Set into Training and Testing Subsets
  8. Identify Data-level Issues In Machine Learning Models

Building Features from Nominal and Numeric Data in Microsoft Azure

by Mike West

Nov 7, 2019 / 1h 19m

1h 19m

Start Course
Description

At the core of applied machine learning is data. In this course, Building Features from Nominal and Numeric Data in Microsoft Azure, you will learn how to cleanse data within the confines of Azure Machine Learning Service. First, you will discover the sundry options you have within Azure Machine Learning Service for building your models end to end. Next, you will explore the importance of applying statistical techniques to your data to improve model performance. Finally, you will learn how to apply various data cleansing techniques to your data for enhancing real-world performance. When you are finished with this course, you will have a foundational knowledge of Azure Machine Learning Service and a solid understating of how to apply statistical techniques to your data that will help you as you move forward to becoming a machine learning engineer.

Table of contents
  1. Course Overview
  2. Setting the Stage
  3. Approaching Normalization and Standardization
  4. Defining Normalization and Standardization Techniques
  5. Leveraging Nominal Data in Machine Learning

Feature Selection and Extraction in Microsoft Azure

by Xavier Morera

Dec 12, 2019 / 1h 27m

1h 27m

Start Course
Description

It is no secret that Data Scientists spend a very large proportion of their time preparing data. In this course, Feature Selection and Extraction in Microsoft Azure, you'll gain the ability to prepare your data for use in your machine learning models. First, you'll learn how to extract features from raw data, including non-text formats. Next, you'll discover how to normalize features, converting your data to a common scale without distorting your data. Finally, you'll explore how to select those features that are more relevant to your model. When you're finished with this course, you'll have the skills and knowledge of feature extraction, normalization, and selection needed to prepare your data. Software required: Azure ML Studio classic.

Table of contents
  1. Course Overview
  2. Exploring Your Dataset for Feature Selection and Extraction
  3. Performing Feature Extraction
  4. Performing Feature Normalization
  5. Performing Feature Selection
  6. Final Takeaway

Building Features from Text Data in Microsoft Azure

by Michael Heydt

Dec 17, 2019 / 1h 54m

1h 54m

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Description

Using text data to make decisions is key in creating text features for machine learning models. In this course, Building Features from Text Data in Microsoft Azure, you'll obtain the ability to structure your data several ways that are usable in machine learning models using Microsoft Azure Machine Learning Service virtual machines. First, you’ll discover how to use natural language processing to prepare text data, and how to leverage several natural language processing technologies, such as document tokenization, stopword removal, frequency filtering, stemming and lemmatization, parts-of-speech tagging, and n-gram identification. Then, you’ll explore documents as text features, where you'll learn to represent documents as feature vectors by using techniques including one-hot and count vector encodings, frequency based encodings, word embeddings, hashing, and locality-sensitive hashing. Finally, you'll delve into using BERT to generate word embeddings. By the end of this course, you'll have the skills and knowledge to use textual data and Microsoft Azure in conceptually sound ways to create text features for machine learning models.

Table of contents
  1. Course Overview
  2. Processing and Simplifying Text to Simplify Feature Creation
  3. Building Features Around Text Data for Use in Machine Learning Models

Building Features for Computer Vision in Microsoft Azure

by David Tucker

Jun 24, 2020 / 1h 39m

1h 39m

Start Course
Description

Computer vision enables insights and experiences that previously weren’t possible, but it can seem daunting to know how to extract the information you need out of an image. In this course, Building Features from Image Data in Microsoft Azure, you will learn how to leverage the tools and services provided by Microsoft Azure alongside popular computer vision and deep learning frameworks to extract relevant information from images. First, you will explore computer vision, its use cases, and also take a look at what Azure provides to make this easier for you. Next, you will learn about the algorithmic approach to computer vision by reviewing popular feature descriptors like the scale-invariant feature transform and the histogram of oriented gradients. Finally, you will delve into deep learning as a tool to leverage in computer vision by creating a convolutional neural network to classify images. When you are finished with this course, you will have both the knowledge and tools to build features out of your image data on Microsoft Azure.

Table of contents
  1. Course Overview
  2. Exploring Computer Vision on Azure
  3. Utilizing the SIFT and HOG Algorithms for Feature Detection
  4. Leveraging Convolutional Neural Networks for Feature Extraction

Reducing Complexity in Data in Microsoft Azure

by Steph Locke

Dec 10, 2019 / 2h 14m

2h 14m

Start Course
Description

If you're building models for data science, your feature sets can quickly become complicated and hard to understand. In this course, Reducing Complexity in Data in Microsoft Azure, you will learn how to reduce the complexity of feature sets, making models more understandable, more straightforward to build, and more robust. First, you will learn to understand feature set complexity and how it impacts your models. Next, you will discover a range of different techniques to improve the complexity of your feature sets. Finally, you will explore various advanced methods for feature set complexity reduction. When you are finished with this course, you will have the skills and knowledge needed to reduce the complexity of your models, and create more straightforward and manageable models, leading to better and more consistent insights into your data.

Table of contents
  1. Course Overview
  2. Understanding How Feature Set Complexity Impacts Model Quality
  3. Applying Criteria-based Feature Reduction Techniques
  4. Using Principal Component Analysis to Reduce Numeric Feature Sets
  5. Processing Categorical or Text Feature Sets
  6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets

Building and Deploying Models in Microsoft Azure

Finally, this section of the skill addresses operational aspects of your data models, such as deploying them for use in Microsoft Azure, monitoring and evaluating their effectiveness, and communicating their insights back to the business.

Developing Models in Microsoft Azure

by Saravanan Dhandapani

Sep 11, 2020 / 1h 55m

1h 55m

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Description

Many developers struggle with the time and effort it takes in designing and developing a highly optimal machine learning model. In this course, Developing Models in Microsoft Azure, you will learn the foundational knowledge of machine learning solutions offered by Microsoft Azure. First, you will understand the basics of setting up workspaces, creating blobstore, and making and registering datasets. Next, you will explore how to create an experiment and submit the run on a compute target provided by Microsoft Azure. Finally, you will explore how to tune hyperparameters and use automated machine learning models, allowing for the development of models without writing a single line of code. When you are finished with this course, you will have the skills and knowledge on the various cutting edge features offered by Microsoft Azure Machine Learning service that are necessary in developing a machine learning model.

Table of contents
  1. Course Overview
  2. Understanding Azure Machine Learning service
  3. Import and Prepare Data for Modeling
  4. Training, Tracking, and Monitoring a Model
  5. Tuning Hyperparameters and AutoML

Evaluating Model Effectiveness in Microsoft Azure

by Tim Warner

Dec 19, 2019 / 50m

50m

Start Course
Description

Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.

Table of contents
  1. Course Overview
  2. Evaluating Model Effectiveness
  3. Improving Model Performance
  4. Assessing Model Explainability

Deploying and Managing Models in Microsoft Azure

by Jared Rhodes

Sep 2, 2020 / 59m

59m

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Description

One of the most overlooked processes in data science is managing the life cycle of models. In this course, Deploying and Managing Models in Microsoft Azure, you'll gain foundational knowledge of Azure Machine Learning. First, you'll discover how to create and utilize Azure Machine Learning. Next, you'll find out how to integrate with Azure DevOps. Finally, you'll explore how to utilize them together to automate the deployment and management of models. When you're finished with this course, you'll have the skills and knowledge of model life cycle management needed to manage a machine learning project. Software required: Microsoft Azure.

Table of contents
  1. Course Overview
  2. Deploying a Machine Learning Model
  3. Using Continuous Integration and Continuous Deployment
  4. Managing a Model's Lifecycle

Communicating Insights from Microsoft Azure to the Business

by Neeraj Kumar

Sep 4, 2020 / 2h 24m

2h 24m

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Description

There is currently a paradigm shift happening in the way organizations are doing business. They need tools to help them get expository insights from the plethora of sources, including their own historical data. This helps them make crucial business decisions at the right moment in order to grow their business or to meet the current and future business requirements. In this course, Communicating Insights from Microsoft Azure to the Business, you will gain the ability to utilize machine learning and artificial intelligence to uncover meaningful insights from data that can be used by business owners to make critical decisions. First, you will learn about data science and how to use the Azure Data Science services to perform data analysis on a large volume of data. Then, you will explore Azure Databricks and how to utilize features like Event Hubs, Data Lake Storage, and what you can glean from an exploratory analysis using these tools. Finally, you will discover how to communicate critical insights to the business from Microsoft Azure using tools like Power BI and MatPlotLib. By the end of this course, you will be confident in taking up Azure Data Science projects for your organization as a skilled data scientist and help your organization grow by providing knowledge business insights to the stakeholders and enable them to take critical decisions.

Table of contents
  1. Course Overview
  2. Setting the Stage
  3. Working with Azure Databricks
  4. Model Evaluation and Summarizing Results
  5. Reviewing Results with Stakeholders