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
Conducted by-
Uditha Bandara (MVP) is specializes in Microsoft development , AI, Mobile App, Cloud and Software Testing technologies. He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional). He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Indonesia, Sri Lanka and India. He has published several books, articles, tutorials, and demos on his Blog – https://uditha.wordpress.com
Contact +94 071-6092918
udithamail@yahoo.com
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