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2020-12-19 04:11:39
30-Day Money-Back Guarantee

This course includes:

  • 11 hours on-demand video
  • 4 articles
  • 40 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Machine Learning

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
Rating: 4.5 out of 54.5 (3,168 ratings)
18,357 students
Created by Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team
Last updated 12/2020
English
English, French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

13 sections • 91 lectures • 10h 52m total length

  • Preview02:02
  • 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?
    Preview09:42
  • What is Machine Learning?
    09:31
  • Understanding various aspects of data - Type, Variables, Category
    07:05
  • Common Machine Learning Terms - Probability, Mean, Mode, Median, Range
    07:41
  • Types of Machine Learning Models - Classification, Regression, Clustering etc
    10:02
  • Basics of Machine Learning
    5 questions

  • What You Will Learn in This Section?
    02:08
  • What is Azure ML and high level architecture.
    03:59
  • Preview03:24
  • Azure ML Studio Overview and walk-through
    05:00
  • Azure ML Experiment Workflow
    07:19
  • Azure ML Cheat Sheet for Model Selection
    06:01
  • Getting Started with AzureML
    4 questions

  • [Hands On] - Data Input-Output - Upload Data
    08:18
  • [Hands On] - Data Input-Output - Convert and Unpack
    08:53
  • Preview05:46
  • [Hands On] -Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns
    11:34
  • [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata
    18:29
  • [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

  • Logistic Regression - What is Logistic Regression?
    06:45
  • [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model
    22:08
  • Logistic Regression - Understand Parameters and Their Impact
    11:19
  • Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score
    13:17
  • Logistic Regression - Model Selection and Impact Analysis
    05:50
  • [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model
    08:13
  • Decision Tree - What is Decision Tree?
    07:34
  • Decision Tree - Ensemble Learning - Bagging and Boosting
    07:05
  • Decision Tree - Parameters - Two Class Boosted Decision Tree
    05:34
  • [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction
    10:43
  • Decision Forest - Parameters Explained
    03:37
  • Preview14:43
  • [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data
    08:14
  • SVM - What is Support Vector Machine?
    04:01
  • [Hands On] - SVM - Adult Census Income Prediction
    05:32
  • Classification Quiz
    8 questions

  • [Hands On] - Tune Hyperparameter for Best Parameter Selection
    09:53
  • Hyperparameter Tuning
    2 questions

  • Azure ML Webservice - Prepare the experiment for webservice
    02:22
  • [Hands On] - Deploy Machine Learning Model As a Web Service
    03:28
  • [Hands On] - Use the Web Service - Example of Excel
    06:38
  • AzureML Web Service
    2 questions

  • 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

  • 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

  • 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
  • Preview08:33
  • [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 - 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 DP-100 Exam - Designing and Implementing a Data Science Solution on the Azure 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

Featured review

Zen Coder
Zen Coder
237 courses
73 reviews
Rating: 5.0 out of 5a year ago
One of the best Machine Learning Courses I've taken on Udemy! The explanations are detailed and easy to understand with good examples, the supporting slides are meaningful and helped me grasp the concepts quickly. The Instructor is clearly knowledgeable and shares when and how to use a model / algorithm along with tips / suggestions based on his real world experience. I'm impressed with what I've seen so far and can heartily recommend this course to anyone thinking of Azure Machine Learning.

Instructors

Jitesh Khurkhuriya
Data Scientist and Digital Transformation Consultant
Jitesh Khurkhuriya
  • 4.5 Instructor Rating
  • 4,934 Reviews
  • 28,021 Students
  • 6 Courses

Jitesh has over 20 years of technology experience and worked as programmer, Product Head as well as the Data Scientist.  

Jitesh has worked with various fortune 500 companies and governments across the world.

As the Data Scientist and Anti-Fraud Expert, he was the member of the high-profile team to suggest tax reforms and amendments in VAT, Customs and Income Tax based on fraud pattern analysis, countrywide data mining and analysis, business process security analysis. This not only contributed to a revolutionary change in the tax processes but also reduced the tax and customs frauds. 

As a seasoned leader in Digital Transformation, Jitesh has developed and executed strategies that generated high top and bottom line revenue streams.

Python, Data Science & Machine Learning A-Z Team
Helping you succeed in Data Science and Machine Learning.
Python, Data Science & Machine Learning A-Z Team
  • 4.5 Instructor Rating
  • 4,729 Reviews
  • 26,846 Students
  • 4 Courses

Hi,

We are Jitesh's Python, Data Science and Machine Learning Assistant team. We are on a mission to help you understand the Data Science and Machine Learning in the simplest possible way. Jitesh has over 20 years of Technology experience and more than a decade of Data Science and Machine Learning experience with various Fortune 500 companies as well as few governments across the globe.

We help create various courses in the Data Science domain and one of the leading training providers.

You will hear from us socially and during the Q&A.

See you in the class and enjoy learning Data Science and Machine Learning.

Sincerely,

Data Science Learning Academy Team


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