ADVANCE YOUR CAREER. TRANSFORM YOUR LIFE WITH IN-DEMAND SKILLS FOR AN ON.DEMAND WORLD. EARN MINIMUM £50K WITH YOUR MICROSOFT PREDICTIVE ANALYTICS  & MACHINE LEARNING SKILLS.

 


PERFECT INTERACTIVE TRAINING MATERIAL (COMPUTER BASED TRAINING - CBT) WITH VERY GOOD SIMULATIONS USING REAL-LIFE BASED SCENARIOS. PACKED WITH TASKS, ACTIVITIES AND EXERCISES.


IDEAL FOR THOSE DESIGNING AND DEVELOPING MACHINE LEARNING, DATA MINING, PREDICTIVE ANALYTICS, DATA SCIENCE, MACHINE LEARNING, BIG DATA ANALYTICS AND REPORTING APPLICATIONS IN A MICROSOFT AZURE MACHINE LEARNING ENVIRONMENT.

 


PREDICTIVE ANALYTICS WITH MICROSOFT AZURE MACHINE LEARNING SELF-STUDY CBT

How This Course Is Organised

The Predictive Analytics with Microsoft Azure Machine Learning course provides step-by-step instructions on how to use a combination of data science and Machine Learning techniques to address your business challenges. You will perform basic to advanced Data Science and Machine Learning tasks using a combination of the R open source tool and Microsoft Azure Machine Learning - ML Studio  for activities based on real-life work scenarios.

Demand for Data Science and machine learning talent is exploding. According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. 

Learn Data Science and machine learning with experts from WebLearning Publishing partnering with Oracle, IBM and Microsoft to help develop your career as a data scientist.

With this course, you will learn how to build and derive insights from data science and machine learning models. You will learn key concepts in data acquisition, preparation, exploration and visualization along with examples on how to build a data science solution using Azure Machine Learning Studio, R or Python.


Audience:
Project Managers of teams of business intelligence, analytics, and big data professionals
Business and Data Analysts 
Data and database professionals
Business Intelligence and Data Warehousing Professionals
Data Engineers and ETL Developers
Data Science and big data developers


Prerequisites:
To complete this course successfully:
You need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed.
WebLearning Fundamentals of Data Science and Machine Learning course


Objectives:
Describe key concepts of data science and machine learning
Understand Open Source Data Science tools and Platforms including Spark, R and Python
Understand Open Source Machine learning Tools and Platforms
Understand Commercial Data Science tools including Microsoft Azure ML Studio,IBM SPSS and Oracle Advanced Analytics
Learn to Install and Configure R and Python
Learn to Configure Microsoft Azure ML for R and Python Development
Prepare data for predictive modelling
Visualize and explore data
Create supervised and unsupervised machine learning models
Develop Data Science solutions using R on Azure ML Platform
Develop Machine Learning solutions using Microsoft Azure Notebooks
Develop Data Science solutions using Microsoft Azure ML Studio


Course Outline

Introduction to Data Science
Overview of Data Science
What Is Data Science?
The Data Science Process
Overview of the Data Science Process
Example of the Data Science Process

Introduction to Machine Learning
Understanding Machine Learning
Introduction to Classification
Introduction to Regression
Statistical Learning Theory
Introduction to Clustering
Introduction to Recommendation

Regression
Recap of Regression
Simple Linear Regression
Ridge Regression
Support Vector Machine Regression
Cross-Validation
Nested Cross-Validation

Classification
Recap of Classification
Loss Functions
Decision Trees
Multi-class Classification
Imbalanced Data
ROC Curves

Clustering
Recap of Unsupervised Learning
Clustering
K-Means Clustering
Hierarchical Agglomerative Clustering

Recommendation
Recommender Systems
Matrix Factorization

Introduction to Data Science Technologies
Introduction to Open-Source tools
Introduction to R for Data Science
Introduction to Python for Data Science
Introduction to KNIME for Data Science
Introduction to Microsoft Azure Machine Learning

Introduction to Setting Up R, Python and Azure ML
Installing R Studio
Accessing and Setting Up R Environment
Setting Up Python
Introduction to Jupyter Notebooks
Accessing Jupyter Notebooks
Accessing Azure Notebooks
Setting Up Azure ML
Exploring Azure ML Studio

R and Python for Data Science
R and Python
Using R: Introducing the R Language and Environment
Accessing R Help
R language basics
Debugging with R
Producing Graphs in R
R Graph Types
R Graphics Packages
Using Python:  Introducing the Python Language and Environment

Data Sampling and Quantization
Data and Data Types
Quantizing Variables
Quantizing Variables with R
Quantizing Variables with Python

Data Cleansing and Transformation
Introduction to Data Cleansing
Missing and Repeated Values
Handling Missing and Repeated Values
Outliers and Errors
Handling Outliers
Visualizing and Handling Outliers in Scripts
Handling Outliers with R
Handling Outliers with Python
Scaling Data

Visualizing Data and Exploring Models
Data Exploration and Visualization
Exploratory Data Analysis
Views of Data
Data Visualization with R
Data Visualization with Python

Model Evaluation and Comparison
Model Evaluation Process
The Evaluate Model Module
Evaluating Models with R

Building Machine Learning Models
Regression Modeling
Recap of Regression
Refining a Regression Model with R

Classification Modeling
Understanding Classification
A Classification Example
Preparing Data for Classification Using R
Preparing Data for Classification Using Python
Creating a Decision Forest Model
Evaluating Classification Models Using R

Unsupervised Learning Models
Introduction to Unsupervised Models
Building a K-Means Clustering Model
Exploring a K-Means Clustering Model with R
Creating a Hierarchical Clustering Model with R
Creating a Hierarchical Clustering Model with Python

NOTE: THIS COURSE IS ALSO AVAILABLE IN SELF-STUDY E-LEARNING, TRAINING GUYIDE OR DISTANCE LEARNING FORMATS. FEEL FREE TO ASK IF YOU PREFER DISTANCE, E-LEARNING OR Training Guide LEARNING  FORMATS 


The WebLearning unique Methodology

All WebLearning® CBT and Training Guide courses are based on the latest materials available at the time of publishing and are regularly updated every 3 months with FREE UPGRADES to the LATEST EDITION: A NEW, very effective and proven way of learning.

FACT:

·        Material retention with CBT’S are 45% better than with conventional training methods

·        Your learning curve with CBT is 25%-55% less than conventional methods

·        The cost of CBT training is over 65% less than Classroom training

·        CBT’S are self pacing, interactive and simulate the actual software being studied


What is Computer Based Training - CBT?

CBT's are usually interactive with Software Simulations (behaves like you're using the real thing on the PC, therefore you don't even need the real software installed on your machine) and sometimes BUT NOT ALWAYS with voice. This Publisher's CBT Titles do NOT have voice..

Generally, CBT's tend to be more expensive. Our CBT Titles are NOT based on Flash. They are based on the latest technology which uses the: See It, Do It, and Try It learning paradigm. A very powerful new way of learning. All CBT and Training Guide Learning formats are delivered with FULL LICENCE KEY.

Today's competitive business environment and frequent software updates by software vendors demands rapid skill acquisition. WebLearning Publishing helps you keep up-to-date by delivering quality Interactive Self-Study in CBT format. These factors give CBT's a clear advantage over conventional, inconvenient and expensive Classroom and Hard-copy print training methods.

CBT's not only offers the advantage in terms of convenience and lower cost, but it also facilitates improved subject-matter comprehension.

 The WebLearning unique Methodology

All WebLearning® Training Guide courses are based on the latest materials available at the time of publishing and are regularly updated every 3 months with FREE UPGRADES to the LATEST EDITION: A NEW, very effective and proven way of learning.

NOTE: WebLearning Publishing Training Guide products are no longer available in Hard-copy Printformat. They are now delivered in eBook Format with FULL LICENCE KEY.


FACT:

  • Material retention with Self-study Training Guide is 35% better than with conventional training methods
  • Your learning curve with Self-study Training Guide is 20%-40% less than conventional methods
  • The cost of Self-study Training Guide training is over 95% less than Classroom training
  • Self-study Training Guide are self pacing, hands-on and offer step-by- step guide to actual software being studied.