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The Complete Data Science A to Z Bundle

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Content
3 hours
Lessons
41

A Beginner's Introduction to Natural Language Processing

Learn Natural Language Processing with ML

By Updegree | in Online Courses

Today, with the digitization of everything, 80% of the data being created is unstructured. Organizations are turning to natural language processing (NLP) technology to develop an understanding of these unstructured data sets. NLP is a branch of artificial intelligence that gives computers the ability to understand human speech as it is spoken. In this course, you'll get an introduction to this powerful tool and understand how to use it to make sense of data.

  • Access 41 lectures & 3 hours of content 24/7
  • Gain an understanding of how to use the Natural Language Tool Kit
  • Load & manipulate your own text data
  • Learn how to formulate solutions to text-based problems
  • Understand when it is appropriate to apply solutions such as sentiment analysis & classification techniques

Instructor

UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain.

We work for different MNC including Microsoft, IBM, CISCO, eBay, Amazon, Flipkart, and a lot of startups also. We teach you practical hands-on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through example and step-by-step.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner

Requirements

  • Internet required

Course Outline

  • Chapter Zero
    • An Introduction to NLP
    • Real Life Applications - 3:56
    • Demand Of NLP Experts - 4:05
    • Course Curriculum - 2:04
  • Introduction & Overview
    • Introduction & Overview - 9:38
    • Installation and Setup - 13:20
    • Installation and Setup Study Note
    • Applications - 12:09
  • Introduction to NLTK toolkit
    • Word and Sentence Tokenization - 3:36
    • Word and Sentence Tokenization : Study Note
    • POS Tagging - 1:52
    • POS Tagging Study Note
    • Named Entity Recognition [NER] - 3:25
    • Named Entity Recognition (NER) Study Note
  • Introduction to Machine Learning
    • NLP and Machine Learning - 5:34
    • What is Machine Learning - 4:12
    • What is Machine Learning Study Note
    • Types Of Machine Learning Problems Regression and Classification - 9:39
    • Types Of Machine Learning Problems Regression and Classification Study Note
  • Machine Learning for binary and multi class Classification
    • Bag Of Words - 1:56
    • Bag Of Words Study Note
    • Binary Classification & Multi Class Classification - 8:20
    • Binary Classification & Multi Class Classification Study Note
  • Introduction to word Embedding
    • One Hot Encoding - 3:47
    • One Hot Encoding Study Note
    • Count Vectorizer - 13:35
    • Count Vectorizer Study Note
    • Tfidf Vectorizer - 8:48
    • Hash Vectorizer - 7:12
    • Hash Vectorizer and Tf-Idf Vectorizer Study Note
  • Deep Neural Networks for Word Embedding – Word2Vec, GloVe
    • Wor2Vec Usage - 5:10
    • Wor2Vec Usage Study Note
    • Introduction to Neural Net - 4:55
    • Introduction to Neural Net Study Note
    • Activation Functions - 3:49
    • Activation Functions Study Notes
  • Document and Sentence Embedding
    • Document and Sentence Embedding - 5:08
  • Sentiment Analysis – Classical and Deep ML
    • Sentiment Analysis - 6:53
  • Named Entity Extraction – Classical and Deep ML
    • Named Entity Extraction - 5:38
  • Neural Machine Translation
    • Neural Machine Translation - 9:57
  • Project : Question Classification on NLP
    • Project : Question Classification on NLP - 21:26

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Lifetime
Content
12 hours
Lessons
74

Data Science & Machine Learning Masterclass with R

Learn How to Break Into the Hottest Job On Earth

By Updegree | in Online Courses

Did you know the average salary of a data scientist is over six figures? Did you know over 10 million new jobs will be created in data science in just the next 3 years? That's why data science is the hottest job on earth for the last two years. So, how do you get in? This data science and machine learning in R course will help you develop the skills you need to break into the data science industry.

  • Access 74 lectures & 12 hours of content 24/7
  • Learn what is data science & its use in the modern world
  • Solve data science-related problems w/ R programming
  • Learn the R data structure & work w/ conditional statements, functions, and loops
  • Get your data in & out of R
  • Plot different types of data & draw insights
  • Manipulate data like a pro

Instructor

UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain.

We work for different MNC including Microsoft, IBM, CISCO, eBay, Amazon, Flipkart, and a lot of startups also. We teach you practical hands-on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through example and step-by-step.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • INTRODUCTION TO DATA SCIENCE
    • Trainer Introduction - 2:33
    • Free $497 Bonus Inside
    • Introduction to Business Analytics - 13:38
    • Application of Business Analytics - 12:23
    • Introduction to Machine Learning - 12:31
    • How to switch your career into ML - 14:56
    • How to switch your career into ML #2 - 3:31
  • Course Curriculum Overview
    • What We are Going to Cover in the Course - 22:41
  • INTRODUCTION TO R
    • Introduction to R - 15:07
    • Setting up R - 11:08
  • R Programming
    • R Operator - 13:45
    • R Loop - 12:02
    • R Function - 13:20
    • R Function #2 - 10:10
    • R Function part #3 [ Writing Function ] - 5:57
  • R Data Structure
    • An Introduction of R Data Structure + Vector - 11:13
    • Matrix, Array and Data Frame - 14:37
    • A Deep Drive to R Data Frame - 13:02
    • Factor - 4:12
    • List - 6:19
  • Import and Export in R
    • Import CSV Data in R - 9:26
    • Import Text Data in R - 3:19
    • Import Excel, Web Data in R - 12:47
    • Export Data in R - Text - 4:07
    • Export Data in R - CSV & Excel - 2:37
  • Data Manipulation
    • Data Manipulation - Apply Function - 13:15
    • Data Manipulation - select - 11:46
    • Data Manipulation - mutate - 14:28
    • Data Manipulation - filter - 14:11
    • Data Manipulation - arrange - 9:38
    • Data Manipulation - Pipe Operator - 11:26
    • Data Manipulation - group by - 8:30
    • Data Manipulation - Date - 10:35
  • Data Visualization
    • Scatter Plot - 12:01
    • mfrow - 7:37
    • pch - 12:30
    • Color - 1:19
    • Line Chart - 3:21
    • Bar Plot - 7:05
    • Pie Chart - 6:43
    • histogram - 7:06
    • Density Plot - 2:26
    • Boxplot - 5:01
    • Mosaic Plot - 7:59
    • 3D Plot - 10:39
    • Correlation Plot and Word Cloud - 9:02
    • ggplot 2-part1 - 14:03
    • ggplot2-part2 - 8:08
    • ggplot2-part3 - 15:04
  • Introduction To Statistics
    • Intro to Statistics-Part 1 - 13:25
    • Intro To Statistics -Part2 - 8:53
    • Intro To Statistics - Part 3 - 14:55
    • Intro To Statistics -part4 - 4:15
    • Intro To Statistics -Part 5 - 15:09
    • Intro To Statistics -Part 6 - 8:21
    • Intro To Statistics-Part7 - 15:04
    • Intro To Statistics-Part8 - 10:45
    • Intro To Statistics - Part9 - 10:24
    • Intro To Statistics-Part 10 - 14:34
    • Intro To Statistics -Part11 - 7:11
  • HYPOTHESIS Testing
    • Hypothesis Testing Intro -Part 1 - 10:08
    • Hypothesis Testing Intro -Part 2 - 11:28
    • Hypothesis Testing Intro -Part 3 - 14:21
    • Hypothesis Testing Intro -Part 4 - 6:01
  • Hypothesis Testing in Practice
    • Hypothesis Testing in Practice-Part 1 - 15:04
    • Hypothesis Testing in Practice-Part2 - 9:36
    • Hypothesis Testing In Practice - Part3 - 14:16
    • Hypothesis Testing in Practice -Part4 - 12:36
    • Hypothesis Testing in Practice -Part5 - 10:29
    • Hypothesis Testing in Practice -Part6 - 13:46
    • Chi Square -Part1 - 11:19
    • Chi Square -Part2 - 14:57
    • ANOVA-Part 1 - 12:30
    • ANOVA-Part 2 - 14:20
    • Chapter Summary of Hypothesis Testing - 5:08

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Content
3 hours
Lessons
20

Data Science Interview Preparation: Career Guide

Prepare for Your Data Science Interview with This Full Guide

By Updegree | in Online Courses

You may know data science but interviewers are still going to try and trip you up. That's why this course was made. If you're pursuing a career in data science, this course will help you plan for the interview and nail the trickiest questions the interviewers will ask.

  • Access 20 lectures & 3 hours of content 24/7
  • Prepare your resume for a data science interview
  • Cover the trickiest questions data science interviewers ask
  • Understand why interviewers ask what they ask
  • Get practice questions

Instructor

UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain.

We work for different MNC including Microsoft, IBM, CISCO, eBay, Amazon, Flipkart, and a lot of startups also. We teach you practical hands-on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through example and step-by-step.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Introduction to Data Science Interview
    • Choose Your Role - 13:46
    • FREE Bonus ($497) Inside
    • Roadmap You have to Follow! - 8:49
  • Get Job First - Resume Building PRO tips
    • PRO Resume Building Tips - 10:03
  • Interview Tips To Crack the Interview
    • Interview PRO Tips - 14:16
  • Technical Q&A
    • Statistics and Hypothesis Testing Q&A - 11:13
    • Data Cleaning Q&A - 4:35
    • Data Exploration Q&A - 3:04
    • Model Building Q&A - 12:37
    • Model Evaluation Q&A - 12:32
    • Generic Machine Learning Q&A - 8:39
  • Statistics Quiz
    • Statistics Quiz (Beginner Level) - 8:32
    • Statistics Quiz - intermediate level - 8:12
    • Statistics Quiz - Advance Level - 14:31
  • Linear Regression Quiz
    • Linear Regression Quiz - 22:12
  • Logistics Regression Quiz
    • Quiz - Logistics Regression - 12:04
  • SVM Quiz
    • SVM Quiz - 11:05
  • KNN Quiz
    • Quiz KNN - 13:58
  • Decision Tree Quiz
    • Decision Tree Quiz - 21:01
  • QUIZ - Clustering
    • Clustering Quiz - 8:32
  • QUIZ - PCA
    • PCA Quiz - 8:29

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Access
Lifetime
Content
30 hours
Lessons
159

Data Science Master Class With R

Be a Data Science Master with 8 Case Studies + 4 Industry Projects

By Updegree | in Online Courses

The best way to learn is by doing and if you want to score the hottest career on earth, this master class is the hands-on path to get there! Through 8 case studies and 4 real-life projects, you'll get real, practical knowledge of working with R, preparing you for a lucrative career.

  • Access 159 lectures & 30 hours of content 24/7
  • Learn R through 8 case studies & 4 real-life projects
  • Learn what data science is & its use in the modern world
  • Solve data science-related problems w/ R programming
  • Systemically explore data in R

Instructor

UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain.

We work for different MNC including Microsoft, IBM, CISCO, eBay, Amazon, Flipkart, and a lot of startups also. We teach you practical hands-on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through example and step-by-step.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web and mobile
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • INTRODUCTION TO DATA SCIENCE
    • Trainer Introduction - 2:33
    • $497 FREE Bonus Inside
    • Introduction to Business Analytics - 13:38
    • Application of Business Analytics - 12:23
    • Introduction to Machine Learning - 12:31
    • How to switch your career into ML - 14:56
    • How to switch your career into ML #2 - 3:31
  • Course Curriculum Overview
    • What We are Going to Cover in the Course - 22:41
  • INTRODUCTION TO R
    • Introduction to R - 15:07
    • Setting up R - 11:08
  • R Programming
    • R Operator - 13:45
    • R Loop - 12:02
    • R Function - 13:20
    • R Function #2 - 10:10
    • R Function part #3 [ Writing Function ] - 5:57
  • R Data Structure
    • An Introduction of R Data Structure + Vector - 11:13
    • Matrix, Array and Data Frame - 14:37
    • A Deep Drive to R Data Frame - 13:02
    • Factor - 4:12
    • List - 6:19
  • Import and Export in R
    • Import CSV Data in R - 9:26
    • Import Text Data in R - 3:19
    • Import Excel, Web Data in R - 12:47
    • Export Data in R - Text - 4:07
    • Export Data in R - CSV & Excel - 2:37
  • Data Manipulation
    • Data Manipulation - Apply Function - 13:15
    • Data Manipulation - select - 11:46
    • Data Manipulation - mutate - 14:28
    • Data Manipulation - filter - 14:11
    • Data Manipulation - arrange - 9:38
    • Data Manipulation - Pipe Operator - 11:26
    • Data Manipulation - group by - 8:30
    • Data Manipulation - Date - 10:35
  • Data Visualization
    • Scatter Plot - 12:01
    • mfrow - 7:37
    • pch - 12:30
    • Color - 1:19
    • Line Chart - 3:21
    • Bar Plot - 7:05
    • Pie Chart - 6:43
    • histogram - 7:06
    • Density Plot - 2:26
    • Boxplot - 5:01
    • Mosaic Plot - 7:59
    • 3D Plot - 10:39
    • Correlation Plot and Word Cloud - 9:02
    • ggplot 2-part1 - 14:03
    • ggplot2-part2 - 8:08
    • ggplot2-part3 - 15:04
  • Introduction To Statistics
    • Intro to Statistics-Part 1 - 13:25
    • Intro To Statistics -Part2 - 8:53
    • Intro To Statistics - Part 3 - 14:55
    • Intro To Statistics -part4 - 4:15
    • Intro To Statistics -Part 5 - 15:09
    • Intro To Statistics -Part 6 - 8:21
    • Intro To Statistics-Part7 - 15:04
    • Intro To Statistics-Part8 - 10:45
    • Intro To Statistics - Part9 - 10:24
    • Intro To Statistics-Part 10 - 14:34
    • Intro To Statistics -Part11 - 7:11
  • HYPOTHESIS Testing
    • Hypothesis Testing Intro -Part 1 - 10:08
    • Hypothesis Testing Intro -Part 2 - 11:28
    • Hypothesis Testing Intro -Part 3 - 14:21
    • Hypothesis Testing Intro -Part 4 - 6:01
  • Hypothesis Testing in Practice
    • Hypothesis Testing in Practice-Part 1 - 15:04
    • Hypothesis Testing in Practice-Part2 - 9:36
    • Hypothesis Testing In Practice - Part3 - 14:16
    • Hypothesis Testing in Practice -Part4 - 12:36
    • Hypothesis Testing in Practice -Part5 - 10:29
    • Hypothesis Testing in Practice -Part6 - 13:46
    • Chi Square -Part1 - 11:19
    • Chi Square -Part2 - 14:57
    • ANOVA-Part 1 - 12:30
    • ANOVA-Part 2 - 14:20
    • Chapter Summary of Hypothesis Testing - 5:08
  • Machine Learning Toolbox
    • Machine Learning Toolbox - Part1 - 14:00
    • Machine Learning Toolbox - Part2 - 12:31
  • Business Use Case Understaing
    • Business Case Understanding - 12:51
  • Data Pre-processing
    • Data Pre-Processing 1 - 14:45
    • Data Pre-Processing 2 - 14:29
    • Data Pre-Processing 3 - 10:25
    • Data Pre-Processing 4 - 9:39
    • Data Pre-Processing 5 - 12:33
    • Data Pre-Processing 6 - 7:19
    • Data Pre-Processing 7 - 15:04
  • Supervised Learning: Regression
    • Linear Regression #1 - 11:47
    • Linear Regression #2 - 14:23
    • Linear Regression #3 - 20:21
    • Linear Regression #4 - 19:02
    • Linear Regression #5 - 25:00
    • Linear Regression #6 - 15:02
    • Linear Regression #7 - [Part 1] - 14:31
    • Linear Regression #7 - [Part 2] - 13:44
    • Linear Regression #8 - 12:52
    • Linear Regression #9 - 16:03
    • Dummy Variable - 12:34
    • Non Linear Regression - 10:55
  • Logistic Regression
    • Classification Overview - 13:29
    • Logistics Regression Intuition - 14:04
    • R Code Implementation - [Part 1] - 5:09
    • R Code Implementation - [Part2 ] - 10:37
    • Model Evaluation - 12:28
    • Telecom Churn Case Study - 22:27
    • Chapter Summary - 6:41
  • K-NN
    • KNN Intuition - 13:26
    • KNN Case Study - 13:59
    • KNN R Code Implementation - 12:48
  • SVM
    • SVM Intuition - 8:44
    • SVM R Code Implementation - 8:22
    • SVM Model Tuning - 9:00
    • SVM Telecom Case Study - 7:56
    • SVM Pros and Cons - 7:27
    • SVM Chapter Summary - 3:49
  • Naive Bayes
    • Naive Bayes Intuition - 19:56
    • Naive Bayes R Code Implementation - 8:25
    • Naive Bayes Case Study - 8:25
  • Decision Tree
    • Decision Tree Intuition - 14:54
    • How Decision Tree works - 7:40
    • Decision Tree R Code Implementation - 13:44
    • Decision Tree Pruning - 15:36
    • Decision Tree Case Study - 14:24
  • Random Forest
    • Random Forest Intuition - 11:37
    • Random Forest R Code Implementation - 5:13
    • Random Forest Case Study - 10:02
  • Capstone Project - Titanic Survival Project
    • Introduction to the Titanic Survival Project - 11:03
    • Capstone Project - Data Understanding - 5:10
    • Capstone Project - Lazy Predictor - 7:20
    • Capstone Project - Data Preparation - 6:12
    • Capstone Project - Data Exploration - 4:34
    • Capstone Project - Feature Engineering - 7:33
    • Capstone Project - Logistics Regression - 16:10
    • Capstone Project - Logistics Regression #2 - 4:06
    • Capstone Project - Decision Tree - 18:28
    • Capstone Project - Random Forest - 3:06
  • K-Mean Clustering
    • Unsupervised Learning Introduction - 6:12
    • K-Mean Clustering Intuition - 19:40
    • K-Mean Clustering -R Code Implementation - 15:28
    • K-Mean Clustering -Case Study - 12:04
  • Hierarchical Clustering
    • Hierarchical Clustering Intuition - 12:44
    • Hierarchical Clustering - R Code Implementation - 16:49
    • Hierarchical Clustering - Case Study - 7:35
  • DBScan Clustering
    • DBScan Clustering - Case Study - 8:59
    • DBScan Clustering - Intuition and R Code - 11:37
  • Principal Component Analysis [ PCA ]
    • PCA Intuition - 12:05
    • PCA R Code Implementation - 16:35
    • PCA Case Study - 16:39
  • Association Rule Mining
    • Association Rule Mining -Introduction - 16:16
    • Association Rule Mining - Pre-Processing - 7:14
    • Association Rule Mining -R Code Implementation - 10:56
    • Association Rule Mining - Case Study - 18:16
  • Capstone Project - Big Mart
    • Big Mart Sale - Data Structure - 15:23
    • Big Mart Sale - Univariate Analysis - 11:14
    • Big Mart Sale - Bi-Variate Analysis - 7:15
    • Big Mart Sale - Fetature Engineering - 13:21
    • Big Mart Sale - Pre-Processing - 19:08
    • Big Mart Sale - Model Building & Evaluation - 18:18
  • Model Deployment
    • Model Deployment - Workflow - 10:01
    • Model Deployment - Pre Requisite - 9:39
    • Model Deployment - Steps To Follow - 19:07
    • Model Deployment - Azure ML DEMO - 11:06

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  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.