A-Z Machine Learning using Azure Machine Learning (AzureML)
4.5 (2,708 ratings)
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15,425 students enrolled

A-Z Machine Learning using Azure Machine Learning (AzureML)

Azure ML (Machine Learning): Azure Machine Learning Studio, Machine Learning on cloud, Machine Learning without coding
Bestseller
4.5 (2,697 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
15,425 students enrolled
Last updated 6/2020
English
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Current price: $69.99 Original price: $99.99 Discount: 30% off
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This course includes
  • 11 hours on-demand video
  • 4 articles
  • 40 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Master Data Science and Machine Learning Models using Azure ML.
  • Understand the concepts and intuition of Machine Learning algorithms
  • Build Machine Learning models within minutes
  • Choose the correct Machine Learning Algorithm using the cheatsheet
  • Deploy production grade Machine Learning algorithms
  • Deploy Machine Learning webservices in the simplest form possible including excel
  • Bring in great value to business you manage
Course content
Expand all 91 lectures 10:51:36
+ Basics of Machine Learning
8 lectures 49:36

This lecture provides an overview of the section of Basics of Machine Learning and what is covered in this section.

Preview 02:02
The course slides as well as Data Files for all sections
00:16
Important Message About Udemy Reviews
03:17

Why machine learning is the future? The Data explosion. We will also see some common examples of ML as well as discuss couple of case studies of Machine Learning.

Preview 09:42

In this lecture we cover,

  • What is Machine Learning; definition and explanation
  • How machines learn? 
  • Examples of Machine Learning
  • Supervised, Unsupervised and Reinforcement Learning
What is Machine Learning?
09:31

In this lecture we will learn about reading and understanding the data 

  • Types of Variables 
  • Data Type and 
  • Category of the variables
Understanding various aspects of data - Type, Variables, Category
07:05

We will learn various basic terms such as Mean, Mode, Median, Range and their importance along with what is probability and how to calculate it for some simple example.

Common Machine Learning Terms - Probability, Mean, Mode, Median, Range
07:41

In this lecture, we are going to cover four fundamental model types that you would build and related algorithms. 

  • Classification
  • Regression
  • Cluster Analysis
  • Anomaly Detection
Types of Machine Learning Models - Classification, Regression, Clustering etc
10:02
Basics of Machine Learning
5 questions
+ Getting Started with Azure ML
6 lectures 26:48

Provides the section overview of Getting started with AzureML.

What You Will Learn in This Section?
02:08

Overview of AzureML and its high level architecture.

What is Azure ML and high level architecture.
03:59

Step by step guide to create your first Free AzureML account.

Creating a Free Azure ML Account
02:21

Overview of AzureML studio and various components of it.

  • Projects,
  • Experiments
  • Web services
  • Notebooks
  • Datasets
  • Trained Models
  • Settings
Azure ML Studio Overview and walk-through
05:00

Workflow of Azure Machine Learning experiment. 

  • GET THE DATA
  • PREPARE THE DATA
  • FEATURE SELECTION
  • CHOOSE AND APPLY LEARNING ALGORITHMS
  • TRAIN AND EVALUATE THE MODEL
Azure ML Experiment Workflow
07:19

In this lecture we will cover the Azure ML Cheat Sheet for model selection.

Azure ML Cheat Sheet for Model Selection
06:01
Getting Started with AzureML
4 questions
+ Data Processing
7 lectures 01:10:00

In this lecture we will cover how to upload a dataset to the azure ML Workspace and also how to enter the data manually.

[Hands On] - Data Input-Output - Upload Data
08:18

In this lecture we will cover how to convert the dataset format as well as how to unpack the zipped dataset.

[Hands On] - Data Input-Output - Convert and Unpack
08:53

In this lecture we cover how to import the data from external sources such as an HTTP link.

Preview 05:46

In this lecture, we are going to cover first part of the Data Transformation. We will cover some of the modules in Data Manipulation.

  • Add Columns
  • Add Rows
  • Remove Duplicate Rows
  • Select Coumns in a dataset
[Hands On] -Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns
11:34

This lecture will cover data manipulation using 

  • Apply SQL Transformation
  • Edit Metadata 
  • How to clean the missing values in a dataset
[Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata
18:29

This lecture covers, two important modules of AzureML data processing; Partition and Sample and Split Data for

  • How to partition the data to create train and test datasets
  • Create different bins of data for cross validation of your results
  • Create a random sample of observations or a more balanced dataset


[Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data
16:56
Update to Lecture Sequence.
00:04
Data Processing
8 questions
+ Classification
15 lectures 02:14:35

You will learn the intuition behind the Logistic Regression and derive the mathematical formula for Logistic Regression.

Logistic Regression - What is Logistic Regression?
06:45

Experiment to create the Logistic Regression model. We are going to build a model that will predict whether the loan application will get approved or not.

[Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model
22:08

Every untrained model expects a set of parameters. They are also known as Hyperparameters. This lecture explains various parameters used for Logistic Regression.

Logistic Regression - Understand Parameters and Their Impact
11:19

Understanding the classification results and associated metrics such as 

  • AUC
  • Accuracy
  • F1-Score
  • Precision and recall
Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score
13:17

In this quick lecture, we are going to analyse the impact of certain parameters on the outcome of our model.

Logistic Regression - Model Selection and Impact Analysis
05:50

In this lecture, we will learn how to predict an outcome that can have multiple values. We are going to use the wine quality dataset and predict the quality of wine based on various characteristics or physiochemical properties of wine, that may affect its quality, such s the acidity, citric acid, residual sugar in it, density  and so on.

[Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model
08:13

We will learn what is a decision tree and how it gets constructed. We will build a decision tree on a small sample of data.

Decision Tree - What is Decision Tree?
07:34

In this lecture we are going to cover what is known as Ensemble learning along with the two most popular techniques of Bagging and Boosting.

Decision Tree - Ensemble Learning - Bagging and Boosting
07:05
Decision Tree - Parameters - Two Class Boosted Decision Tree
05:34

We are going to build a model based on Two Class Boosted decision tree using the data related with direct marketing campaigns of a banking institution.

[Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction
10:43
Decision Forest - Parameters Explained
03:37

In this lecture, we are going to cover one of the most interesting and very popular model called Decision Forest. Using Adult Census data, we will predict whether an individual earns more than $50K or not.

Preview 14:43

Multiclass Decision Forest using IRIS data which remains one of the most popular datasets on UCI.

[Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data
08:14

Intuition of Support Vector Machine. 

SVM - What is Support Vector Machine?
04:01

Adult census classification using Two Class SVM in AzureML.

[Hands On] - SVM - Adult Census Income Prediction
05:32
Classification Quiz
8 questions
+ Hyperparameter Tuning
1 lecture 09:53

How to use Tune Model Hyperparameter as well as how to train and configure the same.

[Hands On] - Tune Hyperparameter for Best Parameter Selection
09:53
Hyperparameter Tuning
2 questions
+ Deploy Webservice
3 lectures 12:28

Typical model deployment challenges and what is AzureML webservice.

Azure ML Webservice - Prepare the experiment for webservice
02:22

Create and set up the web service for the machine learning model.

[Hands On] - Deploy Machine Learning Model As a Web Service
03:28

Consume the web service and use the end points using excel.

[Hands On] - Use the Web Service - Example of Excel
06:38
AzureML Web Service
2 questions
+ Regression Analysis
10 lectures 01:01:18
What is Linear Regression?
06:19
Regression Analysis - Common Metrics
06:27
[Hands On] - Linear Regression model using OLS
11:05
[Hands On] - Linear Regression - R Squared
04:25
Gradient Descent
10:48
Linear Regression: Online Gradient Descent
02:12
[Hands On] - Experiment Online Gradient
04:21
Decision Tree - What is Regression Tree?
06:41
Decision Tree - What is Boosted Decision Tree Regression?
02:00
[Hands On] - Decision Tree - Experiment Boosted Decision Tree
07:00
Regression Analysis
6 questions
+ Clustering
3 lectures 33:11
What is Cluster Analysis?
11:52
[Hands On] - Cluster Analysis Experiment 1
13:15
[Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate
08:04
Clustering or Cluster Analysis
4 questions
+ Data Processing - Solving Data Processing Challenges
15 lectures 01:19:49
Section Introduction
02:49
How to Summarize Data?
06:28
[Hands On] - Summarize Data - Experiment
03:11
Outliers Treatment - Clip Values
06:51
[Hands On] - Outliers Treatment - Clip Values
07:51
Clean Missing Data with MICE
07:18
[Hands On] - Clean Missing Data with MICE
06:43
[Hands On] - SMOTE
05:50
Data Normalization - Scale and Reduce
03:10
[Hands On] - Data Normalization
02:32
PCA - What is PCA and Curse of Dimensionality?
06:24
[Hands On] - Principal Component Analysis
03:24
Join Data - Join Multiple Datasets based on common keys
06:03
[Hands On] - Join Data - Experiment
02:42
+ Feature Selection - Select a subset of Variables or features with highest impact
9 lectures 48:09
Feature Selection - Section Introduction
05:48
Pearson Correlation Coefficient
07:12
Chi Square Test of Independence
05:34
Kendall Correlation Coefficient
04:11
Spearman's Rank Correlation
03:42
[Hands On] - Comparison Experiment for Correlation Coefficients
07:40
[Hands On] - Filter Based Selection - AzureML Experiment
03:33
Fisher Based LDA - Intuition
04:43
[Hands On] - Fisher Based LDA - Experiment
05:46
Requirements
  • Basic Math is good enough. This course does not require background in Data Science. Will be great if you have one.
  • Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification
Description

Machine Learning is one of the hottest and top paying skills. It's also one of the most interesting field to work on.

In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve.

This course will help you prepare for the entry to this hot career path of Machine Learning as well as the Azure DP-100: Azure Data Scientist Associate exam.

This course has more than 80 lectures and is over 11 hours in content.  That simply means, we go through the details of Data Science and Machine Learning along with its implementation. Almost every topic has a hands-on lab that you can practice. I have dealt with almost all scenarios during my tenure with various governments across the world and Fortune 500 companies.

I am committed to and invested in your success. I have always provided answers to all the questions and not a single question remains unanswered for more than a few days. The course is also regularly updated with newer features.

Learning data science and then further deploying Machine Learning Models have been difficult in the past. To make it easier, I have explained the concepts using very simple and day-to-day examples. Azure ML is Microsoft's way of democratizing Machine Learning. We will use this revolutionary tool to implement our models. Once learnt, you will be able to create and deploy machine learning models in less than an hour using Azure Machine Learning Studio.

Azure Machine Learning Studio is a great tool to learn to build advance models without writing a single line of code using simple drag and drop functionality. Azure Machine Learning (AzureML) is considered as a game changer in the domain of Data Science and Machine Learning.

This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists to learn the AzureML tool. You can skip some of the initial lectures or run them at 2x speed, if you are already familiar with the concepts or basics of Machine Learning.

The course is very hands on and you will be able to develop your own advance models while learning,

  • Advance Data Processing methods

  • Statistical Analysis of the data using Azure Machine Learning Modules

  • MICE or Multiple Imputation By Chained Equation

  • SMOTE or Synthetic Minority Oversampling Technique

  • PCA; Principal Component Analysis

  • Two class and multiclass classifications

  • Logistic Regression

  • Decision Trees

  • Linear Regression

  • Support Vector Machine (SVM) 

  • Understanding how to evaluate and score models

  • Detailed Explanation of input parameters to the models

  • How to choose the best model using Hyperparameter Tuning

  • Deploy your models as a webservice using Azure Machine Learning Studio

  • Cluster Analysis

  • K-Means Clustering

  • Feature selection using Filter-based as well as Fisher LDA of AzureML Studio

  • Recommendation system using one of the most powerful recommender of Azure Machine Learning

  • All the slides and reference material for offline reading

You will learn and master, all of the above even if you do not have any prior knowledge of programming.

This course is a complete Machine Learning course with basics covered. We will not only build the models but also explain various parameters of all those models and where we can apply them. 

In this course, we will start with some basic terms which are used very frequently in machine learning.

I will also explain 

  • What is Machine Learning and some real world examples.

  • Azure Machine Learning Introduction

  • Provide an overview of Azure Machine Learning Studio and high level architecture.

We would also look at 

  • Steps for building an ML model.

  • Supervised and Unsupervised learning

  • Understanding the data and pre-processing

  • Different model types

  • The AzureML Cheat Sheet.

  • How to use Classification and Regression

  • What is clustering or cluster analysis

KDNuggets one of the leading forums on Data Science calls Azure Machine Learning as the next big thing in Machine Learning. It further goes on to say, "people without data science background can also build data models through drag-and-drop gestures and simple data flow diagrams."

Azure Machine Learning's library has many pre-built models that you can re-use as well as deploy them.

This course will also be a great help in preparing for the Microsoft 70-774 exam-Perform Data Science on Cloud using Azure Machine Learning. It covers almost all the topics of Azure Machine Learning.

So see you inside the course.

Who this course is for:
  • Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning
  • Business Analysts who want to apply Data Science to solve business problems
  • Functional Experts who can take help of Machine Learning and build/test their hypothesis quickly
  • Anyone who wants to learn Machine Learning
  • Students and non-technical professionals who want to start a career in Machine Learning
  • Business Process Managers who want to automate their processes or decision making
  • Marketing professionals who want to apply machine learning for better predictions of sales, conversion, churn