Data Science and Machine Learning Workshop at Microsoft Sri Lanka.
Register Now –
Event Page-
In this workshop you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello World” example, throughout the workshop you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, Deep Neural Networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn popular deep learning API such as Keras and Teano.
Agenda-
· Introduction to Spyder IDE Jupiter Notebook with Python basics
· Python Data science libraries
· Introduction to tenser flow basics
· Basic Neural Networks using tenser flow
· Advanced Neural Networks using tenser flow
· Keras API for Deep learning
· Theano API for Deep learning
Introduction to R Programming workshop teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.
Overview
· History of R
· Advantages and disadvantages
· Downloading and installing
Introduction
· Using the R console
· Learning about the environment
· Writing and executing scripts
· Object oriented programming
· Installing packages
· Working directory
· Saving your work
Variable types and data structures
· Variables and assignment
· Data types
· Numeric, character, boolean, and factors
· Data structures
· Vectors, matrices, arrays,
· Assigning new values
· Viewing data and summaries
Base graphics system in R
· Scatterplots, histograms, barcharts, box and whiskers, dotplots
· Labels, legends, titles, axes
· Exporting graphics to different formats
General linear regression
· Linear and logistic models
· Regression plots
· Interaction in regression
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).
Course 20774A:
Perform Cloud Data Science with Azure Machine Learning
Course Outline
Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.Lessons
Lab : Introduction to machine Learning
After completing this module, students will be able to:
Describe machine learning
Describe machine learning algorithms
Describe machine learning languages
Module 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.Lessons
Lab : Introduction to Azure machine learning
After completing this module, students will be able to:
Describe Azure machine learning.
Use the Azure machine learning studio.
Describe the Azure machine learning platforms and environments.
Module 3: Managing Datasets
At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.Lessons
Lab : Managing Datasets
After completing this module, students will be able to:
Understand the types of data they have.
Upload data from a number of different sources.
Explore the data that has been uploaded.
Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.Lessons
Lab : Preparing data for use with Azure machine learning
After completing this module, students will be able to:
Pre-process data to clean and normalize it.
Handle incomplete datasets.
Module 5: Using Feature Engineering and Selection
This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.Lessons
Lab : Using feature engineering and selection
After completing this module, students will be able to:
Use feature engineering to manipulate data.
Use feature selection.
Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.Lessons
Lab : Building Azure machine learning models
After completing this module, students will be able to:
Describe machine learning workflows.
Explain scoring and evaluating models.
Describe regression algorithms.
Use a neural-network.
Module 7: Using Classification and Clustering with Azure machine learning models
This module describes how to use classification and clustering algorithms with Azure machine learning.Lessons
Lab : Using classification and clustering with Azure machine learning models
After completing this module, students will be able to:
Use classification algorithms.
Describe clustering techniques.
Select appropriate algorithms.
Module 8: Using R and Python with Azure Machine Learning
This module describes how to use R and Python with azure machine learning and choose when to use a particular language.Lessons
Lab : Using R and Python with Azure machine learning
After completing this module, students will be able to:
Explain the key features and benefits of R.
Explain the key features and benefits of Python.
Use Jupyter notebooks.
Support R and Python.
Module 9: Initializing and Optimizing Machine Learning Models
This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.Lessons
Lab : Initializing and optimizing machine learning models
After completing this module, students will be able to:
Use hyper-parameters.
Use multiple algorithms and models to create ensembles.
Score and evaluate ensembles.
Module 10: Using Azure Machine Learning Models
This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.Lessons
Lab : Using Azure machine learning models
After completing this module, students will be able to:
Deploy and publish models.
Export data to a variety of targets.
Module 11: Using Cognitive Services
This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.Lessons
Lab : Using Cognitive Services
After completing this module, students will be able to:
Describe cognitive services.
Process text through an application.
Process images through an application.
Create a recommendation application.
Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.Lessons
Lab : Machine Learning with HDInsight
After completing this module, students will be able to:
Describe the features and benefits of HDInsight.
Describe the different HDInsight cluster types.
Use HDInsight with machine learning models.
Module 13: Using R Services with Machine Learning
This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.Lessons
Lab : Using R services with machine learning
Microsoft EXAM – 70-774
Perform Cloud Data Science with Azure Machine Learning
https://www.microsoft.com/en-us/learning/exam-70-774.aspx
Second Course 20773A:
Analyzing Big Data with Microsoft R
https://www.microsoft.com/en-us/learning/course.aspx?cid=20773
Microsoft EXAM – 70-773
This 3 days training focus on getting started with Data Science technologies. You will learn Azure machine learning studio, R studio, Jupyter Notebook ,Spyder with Python for data science. This course includes real world usage of machine learning for regression, classification and product recommendations.
Day 1
Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
Lessons
· What is machine learning?
· Introduction to machine learning algorithms
· Introduction to machine learning languages
Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Lessons
· Azure machine learning overview
· Introduction to Azure machine learning studio
· Developing and hosting Azure machine learning applications
Managing Datasets
At the end of this module the student will be able to explore various types of data in Azure machine learning.
Lessons
· Categorizing your data
· Importing data to Azure machine learning
· Exploring and transforming data in Azure machine learning
Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.
Lessons
· Azure machine learning workflows
· Using regression algorithms
· Using neural networks
Using Azure Machine Learning Models
This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.
Lessons
· Deploying and publishing models
· Consuming Experiments
Day 2
Introduction to R
· Using the R console
· Learning about the environment
· Writing and executing scripts
· Object oriented programming
· Installing packages
· Working directory
· Saving your work
Variable types and data structures
· Variables and assignment
· Data types
· Numeric, character, boolean, and factors
· Data structures
· Vectors, matrices, arrays,
· Assigning new values
· Viewing data and summaries
Base graphics system in R
· Scatterplots, histograms, barcharts, box and whiskers, dotplots
· Labels, legends, titles, axes
· Exporting graphics to different formats
General linear regression
· Linear and logistic models
· Regression plots
· Interaction in regression
Day 3
Introduction to Python
· Python History
· Users of Python
· Installing Python
· Installing IDE
Datatypes
· Numbers
· Sequences
· File
· Tuples
· Dictionaries
Data Science Intro
· Why Python for Data Science
· Popular packages
· Use cases
· Popular Libraries
· Panda
· Numpy
· Matplotlib
· Scikit-learn
Working with data
· Reading & Writing to different data sources
· Cleaning data
· Visualisation
· Data Transformation