Intelligent Analytics and Automation with ML & AI

Course Overview

Machine learning products are being used daily, perhaps without realizing it. The future of machine learning is already here, so gearing yourself up with machine learning skills is a good move now. You and your teams can use Artificial Intelligence to deliver value to your customers. You too can build and deploy machine learning products to meet business opportunities.

You’ll learn what Artificial Intelligence is, and the fundamentals of Machine Learning and Deep Learning. The knowledge will be applied to different technologies, in different programming languages, and on different Cloud platforms. The tools will always be evolving, but the methodology remains. In this 5-day, hands-on course, you will learn how you can leverage Artificial Intelligence in many business scenarios.

Key Learning Areas

Attendees will acquire first-hand experience in using Machine Learning and the different tools around it:

How you, your team, and your company should think the Machine Learning Lifecycle
How to choose the right algorithm for the right problem
How to use the right metrics to monitor your models
How to use libraries in R and Python to predict values
How to build a Neural Network
How to use Cognitive Services and how industry takes advantage of them
How to simplify the Machine Learning process with Azure Machine Learning Studio and Amazon SageMaker
How to identify and mitigate the risks of Machine Learning

Each course module consists of a lecture followed by a hands-on lab designed to reinforce the principles presented during lecture.

Course Outline

Introduction to Machine Learning

The Machine Learning Process
Supervised Learning
Unsupervised Learning
Azure ML Studio
Lab: Machine Learning with Azure Machine Learning Studio

Visualization and Statistics

Data Visualization
Histogram
Scatter plot
Just enough statistics
Variables
Lab: Data Visualization
Lab: Compare two models

Improving and Deploying Machine Learning Models

Ensemble models
Bagging
Boosting
Deploying your model
Lab: Principal Component Analysis
Lab: Deploying your Model
Lab: Using your Model

Machine Learning with R – R Basics

Why R
R Overview
R Introduction
R Interface
R Workspace
Help
R Packages
Input/output
Reusing Results
Lab: Iris Dataset

Machine Learning with R – Data Input

Data Types
Importing Data
Missing Data
Date Values
Lab: Iris Data Set – Visualize Dataset

Machine Learning with R – Data Manipulation

Creating New Variable
Operators
Control Structures
Built-in functions
Sorting Data
Merging Data
Data Type Conversions
Lab: Iris Dataset – Evaluate and Predict

Machine Learning with Python – Data Handling

Introduction to NumPy
Arrays
Aggregations
Introduction to Pandas
Handling missing Data
Combining datasets
Lab: Predicting Bicycle Traffic
Lab: K-Means on digit

Machine Learning with Python – Visualization and Modeling

Introducing Matplotlib
Simple Line Plots
Simple histogram
Introducing Scikit-Learn
Model Validation
Feature Engineering
Linear Regression
Principal Component Analysis
Decision Trees and Random Forests
Lab: Random Forest for Classifying Digits
Lab: Eigen faces

Robotic Process Automation (RPA)

Introduction to RPA
What are the benefits of RPA?
What are the pitfalls of RPA?
Comparing the different major RPA systems available
Business applications for RPA
Lab: creating a basic robot using UIPath

Machine Learning with Internet of Things (IoT)

What is IoT?
How is it used?
Characteristics of an IoT solution
Why Azure IoT?
Lab: Create an Azure IoT Central application

Deep Learning

What is Deep Learning?
Why is it useful?
Activation functions
Regularization Methods
Basic architectures
Azure Cognitive Services
Lab: Handwritten Digit Recognition using Convolutional Neural Networks

Azure Cognitive Services: Audio-Based Machine Learning

What services are offered for Audio
What services are offered for Speech
What are their purposes?
How are these services being utilized today?
LAB: Setting up recognized speakers with Speaker Recognition
LAB: Invoking audio-based services.
Lab: Having your say… Make your Desktop app obey only recognized voices

Azure Cognitive Services: Computer Vision/Image-Based Services

What is Computer Vision?
What services are offered for Computer Vision?
Other Image-based services
Examples of Image-based services utilized today
Lab: Recognizing a face from an image.
Lab: Step by Step: Invoking image-based services
Lab: Building a Vision Accessibility feature which describes the objects found in the image taken for the blind

Azure Cognitive Services: Text/Language-Based Services

What are the Text-based services?
What is their purpose?
What are the Language-based services?
What is natural language processing?
Examples of text and language services
LAB: Analyzing Intent from text messages
LAB: Building a LUIS model for Hair and Nail Salon conversations
LAB: invoking natural language services
Lab: Building a Bot which determines bad intent
Lab: Building a Bot which automates the scheduling of a Hair appointment

Introduction to Machine Learning with AWS

What is Amazon SageMaker?
Machine Learning with Amazon SageMaker
Explore, Analyze and Process Data
How it is used in the industry
Lab: Build a recommendation system

Building Machine Learning Models with AWS

Train Models with SageMaker
Deploy Models with SageMaker
Lab: Build a recommendation system – Part 2

Security Risks of Machine Learning Models

Data Poisoning
Evasion Attacks
Inversion by Surrogate Model
Adversarial Attacks
Impersonation

Mitigating Risks of Machine Learning Models

Model Monitoring with Azure Machine Learning
Data Drift Monitoring with Azure Machine Learning
Models Explainers with Azure Machine Learning

Who Benefits

This course is designed for software developers and for business consultants who want to learn the landscape of Artificial Intelligence. The hands-on labs involve a broad range of technologies with some light coding. Coding is done in Python, R, C#. Some labs will be done with visual (non-coding) programming. The course is valuable for non-developers as many business scenarios are presented.

In order to work along with the labs, attendees must have the following:

A modern web browser
An Azure subscription (Azure Pass, MSDN subscription, free trial, company subscription, etc.)
An AWS Subscription
Local administrative permissions to install additional tools that may be required during the course of the labs

Prerequisites

Basic programming experience is a plus

Want this course for your team?

Atmosera can provide this course virtually or on-site. Please reach out to discuss your requirements.

Atmosera is thrilled to announce that we have been named GitHub AI Partner of the Year.