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Courses
6
Lessons
325
Enrolled
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Courses
6
Lessons
325
Enrolled
334
What's Included
Statistics & Machine Learning for Regression Modeling with R
$200 Value
Social Media Mining & Text Data Analysis with Natural Language Processing in R
$200 Value
Working With Classes: Classify & Cluster Data With R
$200 Value
Pre-Process & Visualize Data With Tidy Techniques in R
$200 Value
Practical Data Pre-Processing & Visualisation Training with R
$200 Value
Practical Time Series Data Analysis With Statistics and Machine Learning
$200 Value
Product Details
Access
Lifetime
Content
6 hours
Lessons
50
Statistics & Machine Learning for Regression Modeling with R
Learn Hands-On Regression Analysis for Practical Statistical Modeling & Machine Learning in R
By Minerva Singh | in Online Courses
Regression analysis is one of the central aspects of both statistical and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions.
- Access 50 lectures & 6 hours of content 24/7
- Implement & infer Ordinary Least Square (OLS) regression using R
- Build machine learning-based regression models & test their robustness in R
- Apply statistical and machine learning-based regression models to deals with problems such as multicollinearity
- Learn when & how machine learning models should be applied
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Prior experience of working w/ R & RStudio
- Basic knowledge of statistics
- Prior experience of using simple linear regression modelling
Course Outline
- Welcome to Regression Modelling With R
- Introduction to the Course - 6:58
- Data and Code
- Install R and RStudio - 6:36
- Read in Data Using R - 15:28
- Data Cleaning - 17:12
- More Data Cleaning - 8:05
- Exploratory Data Analysis (EDA) - 18:53
- Conclusions to Section 1 - 1:58
- Ordinary Least Square Regression
- Ordinary Least Square Regression: Theory - 10:44
- OLS Implementation - 8:40
- Confidence Interval-Theory - 6:06
- Calculate the Confidence Interval in R - 4:53
- Confidence Interval and OLS Regressions - 7:19
- Linear Regression without Intercept - 3:40
- Implement ANOVA on OLS Regression - 3:37
- Multiple Linear Regression - 6:27
- Multiple Linear regression with Interaction and Dummy Variables - 15:05
- Some Basic Conditions that OLS Models Have to Fulfil - 12:56
- Conclusions to Section 2 - 2:55
- Deal with Multicollinearity in OLS Regression Models
- Identify Multicollinearity - 16:42
- Doing Regression Analyses with Correlated Predictor Variables - 5:36
- Principal Component Regression in R - 10:39
- Partial Least Square Regression in R - 7:33
- Lasso Regression in R - 4:24
- Conclusions to Section 3 - 2:00
- Variable & Model Selection
- Why Do Any Kind of Selection? - 4:40
- Select the Most Suitable OLS Regression Model - 13:19
- Select Model Subsets - 8:22
- Machine Learning Perspective on Evaluate Regression Model Accuracy - 7:10
- Evaluate Regression Model Performance - 14:26
- LASSO Regression for Variable Selection - 3:42
- Identify the Contribution of Predictors in Explaining the Variation in Y - 8:38
- Conclusions to Section 4 - 1:35
- Dealing With Other Violations of the OLS Conditions
- Data Transformations
- Robust Regression: Deal With Outliers - 6:58
- Deal With Heteroelasticity - 7:12
- Conclusion to Section 5 - 1:12
- Generalised Linear Models (GLMs)
- What are GLMs? - 5:25
- Implement a Logistic Regression - 16:18
- More Logistic Regression - 9:10
- Modelling Count Data - 6:19
- Multinomial Regression - 6:11
- Conclusion to Section 6 - 2:12
- Non-Parametric and Machine Learning Regression
- Polynomial Regression - 18:19
- Generalized Additive Models (GAMs) in R - 14:09
- Boosted GAM - 6:15
- Multivariate Adaptive Regression Splines (MARS) - 8:06
- CART For Regression - 10:54
- CIR - 5:45
- Random Forest (RF) Regression - 11:52
Access
Lifetime
Content
7 hours
Lessons
77
Social Media Mining & Text Data Analysis with Natural Language Processing in R
Practice Using R for Data Science Applications with 7 Hours of Hands-On Text Mining & Natural Language Processing
By Minerva Singh | in Online Courses
Mining unstructured text data and social media is the latest frontier of machine learning and data science. This course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like the caret and dplyr to work with real data in R.
- Access 77 lectures & 7 hours of content 24/7
- Be able to read in data from different sources including databases
- Learn basic web scraping—extracting text & tabular data from HTML pages
- Learn social media mining from Facebook & Twitter
- Analyze text data for emotions
- Extract information relating to tweets & posts
- Carry out Sentiment analysis
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Prior experience of R & RStudio
- Prior experience of statistical & machine learning techniques will be beneficial
Course Outline
- Introduction to the course
- About the course - 7:58
- Data & Scripts
- Introduction to R & RStudio
- Conclusion to Section 1 - 1:18
- Read in Data From Different Sources
- Read in CSV & Excel Data - 9:56
- Read in Online CSV Data - 4:04
- Read in Zipped File - 3:04
- Read in databases - 8:23
- Read in JSON Data - 5:28
- Conclusions to Section 2 - 1:03
- Webscraping: Extract Data from Webpages
- Read in Data From Online Google Sheets - 4:03
- Read in Data from Online HTML Tables-Part 1 - 4:13
- Read in Data From HTML Tables-Part 2 - 6:24
- Get & Clean Data From HTML Tables - 7:30
- Read Text Data from HTML - 8:52
- Introduction to Selector Gadget - 6:11
- More Web-scraping With rvest - 8:52
- Another Way of Accessing Web Data - 2:52
- Conclusion to Section 3 - 1:35
- Start With APIs
- What is an API?
- Scraping the Guardian Newspaper - 6:42
- Text Data Mining from Social Media
- Extract Data from Facebook - 4:12
- Get More out Of Facebook
- Set Up a Twitter App - 3:52
- Extract Tweets With R - 5:21
- More Twitter Data Extraction - 6:28
- Geo-locational Information from Twitter - 5:06
- Get Location Specific Trends - 2:02
- Learn More About the Followers of a Twitter Handle - 6:55
- Another Way of Extracting Information From Twitter- the rtweet Package - 3:18
- Geolocation Specific Tweets With "rtweet" - 7:49
- More Data Extraction Using rtweet - 3:18
- Locations of Tweets - 4:02
- Mining Github Using R - 7:04
- Set up the FourSquare App - 4:32
- Extract Reviews for Venues on FourSquare - 11:28
- Conclusion to Section 5 - 1:46
- Exploring Text Data For Preliminary Ideas
- Explore Tweet Data - 7:51
- A Brief Explanation - 4:22
- EDA With Text Data - 9:02
- Examine Multiple Document Corpus of Text - 5:30
- Brief Introduction to tidytext - 8:28
- Text Exploration & Visualization with tidytext - 11:09
- Explore Multiple Texts with tidytext - 9:22
- Count Unique Words in Tweets - 4:54
- Visualizing Text Data as TF-IDF - 7:55
- TF-IDF in Graphical Form - 5:49
- Conclusions - 1:18
- Natural Language Processing: Sentiment Analysis
- Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy - 12:29
- Wordclouds for Visualizing Reviews - 10:32
- Tidy Wordclouds - 5:35
- Quanteda Wordcloud - 8:34
- Word Frequency in Text Data - 3:24
- Twitter Sentiments: Mugabe - 4:52
- Tidy Sentiments: Sentiment Analysis With tidytext - 8:38
- Examine the Polarity of Text - 10:58
- Examine the Polarity of Tweets - 6:24
- Topic Modelling of a Document - 8:15
- Topic Modelling of Multiple Documents - 14:19
- Topic Modelling of Tweets Using Quanteda - 8:21
- Conclusion to Section 7 - 2:14
- Text Data and Machine learning
- Clustering for Text Data - 7:17
- Clustering Tweets with Quanteda - 4:35
- Regression on Text Data - 6:11
- Identify Spam Emails with Supervised Classification - 10:09
- Introduction to RTextTools - 6:16
- The Doc2Vec Approach - 4:00
- Doc2Vec Approach For Predicting a Binary Outcome - 12:24
- Doc2Vec Approach for Multi-class Classification - 9:00
- Network Analysis
- A Small (Social) Network - 2:43
- Some Theory - 4:25
- Build & Visualize a Network - 14:31
- Network of Emails - 6:50
- More on Network Visualisation - 4:10
- Analysis of Tweet Network - 8:13
- Identify Word Pair Networks - 9:13
- Network of Words - 4:42
Access
Lifetime
Content
7 hours
Lessons
56
Working With Classes: Classify & Cluster Data With R
Harness The Power of Machine Learning for Unsupervised & Supervised Learning in R
By Minerva Singh | in Online Courses
In this course, you'll learn to implement R methods using real data obtained from different sources. After this course, you'll understand concepts like unsupervised learning, dimension reduction, and supervised learning.
- Access 56 lectures & 7 hours of content 24/7
- Learn how to harness the power of R for practical data science
- Read-in data into the R environment from different sources
- Carry out basic data pre-processing & wrangling in R studio
- Implement unsupervised/clustering techniques such as k-means clustering
- Explore supervised learning techniques/classification such as random forests
- Evaluate model performance & learn best practices for evaluating machine learning model accuracy
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: beginner
Requirements
- Able to operate & install software on a computer
- Prior exposure to common machine learning terms such as unsupervised & supervised learning
Course Outline
- Introduction to the Course
- Welcome to the Course and Instructor Info
- Data and Code
- Install R and RStudio - 6:36
- Preprocessing Data in R - 17:12
- Read in Data From Different Sources
- Read CSV & Excel Data - 9:56
- Read in Online CSV - 4:04
- Read in Googlesheet - 4:03
- Read in JSON Data - 5:28
- Read in Database - 8:23
- Data Pre-Processing and Visualisation
- Start With Data Cleaning: Remove Missing Values - 17:12
- Slightly Advanced Data Cleaning - 8:05
- Introduction to dplyr for data summarising- part 1 - 4:44
- Use dplyr for summarising & visualisations - 6:07
- Exploratory data analysis (EDA) in R - 18:53
- More EDA - 4:16
- Association between quantitative variables - 19:50
- Testing for correlation - 19:50
- Association Between Qualitative Variables - 8:20
- Cramer's Test for qualitative variable - 3:35
- Machine Learning for Data Science
- Difference Between Machine Learning And Statistical Modelling? - 5:36
- Machine Learning:Basic Theory - 5:32
- Cluster Unlabeled Data in R
- k-means clustering - 14:31
- Hierarchical clustering - 14:13
- Weighted k-means - 6:04
- Fuzzy k-means - 18:14
- Expectation maximisation (EM) - 5:50
- DBSCAN for clustering - 4:58
- Cluster a mixed dataset - 4:01
- Should we even do clustering? - 3:07
- Evaluate clustering accuracy - 5:46
- Dimension Reduction
- Theory behind dimension reduction - 3:17
- Principal Component Analysis (PCA) - 13:10
- More PCA - 4:27
- Feature Selection: Identify the Most Important Variables
- Removing Highly Correlated Predictor Variables - 16:42
- Variable Selection Using LASSO Regression - 3:42
- Variable Selection With FSelector - 13:35
- Boruta analysis for feature selection - 4:51
- Theory of Supervised Learning
- Some Basic Supervised Learning Concepts - 10:10
- Prepare data for ML analysis - 3:31
- Work With Labelled Classes: Classification
- Generalised Linear Models (GLMs) - 5:25
- Logistic Regression Models as Binary Classifiers
- Binary Classifier with PCA - 6:29
- How Good is the Model: Evaluate Accuracy - 9:42
- Accuracy of Binary Classification - 8:18
- More on Binary Accuracy Measures - 4:19
- Linear Discriminant Analysis (LDA) - 12:55
- Our Multi-class Classification Problem - 6:14
- Classification Trees - 11:55
- More on Classification Tree Visualization - 9:20
- Classification with Party Package - 5:12
- Decision Trees - 8:39
- Random Forest (RF) - 8:15
- Examine Individual Variable Importance for Random Forests - 3:53
- GBM Classification - 4:10
- Support Vector Machines (SVM) for Classification - 3:55
- More SVM for Classification - 3:42
Access
Lifetime
Content
4 hours
Lessons
39
Pre-Process & Visualize Data With Tidy Techniques in R
Become Highly Proficient in Data Pre-Processing, Wrangling & Visualization Using the Two Most In-Demand R Data Science Packages
By Minerva Singh | in Online Courses
With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.
- Read-in data into the R environment from different sources
- Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2
- Carry out basic data pre-processing & wrangling in R studio
- Gain proficiency in data pre-processing, wrangling & data visualization in R
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Ability to install R & RStudio on your computer/laptop
- Know how to install & load R packages
Course Outline
- Welcome To The Course
- Introduction to the Course - 2:16
- Data & Scripts
- Install R and RStudio - 6:36
- Common Data Types We Encounter in Data Analysis - 3:37
- Read in Data From Different Sources
- Read in CSV and Excel Data - 9:56
- Read in Data from Online HTML Tables-Part 1 - 4:13
- Read in Data from Online HTML Tables-Part 2 - 6:24
- Read in Data from Databases - 8:23
- Read in Data from JSON - 5:28
- Data Processing With dplyr
- Introduction to Pipe Operators - 9:14
- Get acquainted with our data using "dplyr" - 8:29
- More selections with dplyr - 12:28
- Row filtering - 7:05
- More row filtering - 4:59
- Select desired rows and columns - 4:03
- Add new variables/columns - 10:02
- Making sense of data by grouping different categories - 5:28
- Grouping Data-Part 2 - 8:55
- Introduction to dplyr-1 - 6:11
- Introduction to dplyr-2 - 4:44
- Process your Data the Tidy Way: Start With tidyverse
- Getting Started With the tidyverse Package - 3:17
- Rename Columns - 6:59
- Long and Wide Format - 5:03
- Joining Tables - 5:58
- Nesting - 3:59
- Theory Behind Hypothesis Testing - 5:42
- Implement t-test With tidyverse - 3:44
- Dealing With Missing Values
- Removing NAs- the ordinary way - 17:12
- Remove NAs- using "dplyr" - 5:15
- Data imputation with dplyr - 4:44
- More data imputation - 3:53
- Data Visualisation and Explorations
- What is Data Visualisation? - 9:33
- Some Principles of Data Visualisation - 6:46
- Data Visualisation With dplyr and ggplot2 - 6:07
- Mining and Visualising Information About the Olympic Games - 12:49
- Of Winter and Summer Olympic Games - 4:16
- Of Men and Women - 8:26
- Theory of Ordinary Least Square (OLS) Regression - 10:44
- Implement OLS on Different Categories - 7:57
Access
Lifetime
Content
6 hours
Lessons
51
Practical Data Pre-Processing & Visualisation Training with R
Learn to Pre-Process, Wrangle & Visualize Data for Practical Data Science Applications in R
By Minerva Singh | in Online Courses
This course is designed to equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. You'll discover data visualization concepts in a practical manner that will help you apply them for practical data analysis and interpretation. You'll also be able to determine which wrangling and visualization techniques are best suited to specific problems.
- Access 51 lectures & 6 hours of content 24/7
- Read in data into the R environment from different sources
- Carry out basic data pre-processing & wrangling in R Studio
- Learn to identify which visualizations should be used in any given situation
- Build powerful visualizations & graphs from real data
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: all levels
Requirements
- Ability to install R & RStudio on your computer/laptop
- Know how to install & load R packages
Course Outline
- Welcome To The Course
- Introduction To The Course and Instructor - 1:59
- Data and Code Used in the Course
- Install R and RStudio - 6:36
- Read in Data From Different Sources
- Read in CSV and Excel Data - 9:56
- Read Unzipped Folder - 3:00
- Read Online CSV - 4:04
- Read in Googlesheets - 3:53
- Read in Data from Online HTML Tables-Part 1 - 4:13
- Read in Data from Online HTML Tables-Part 2 - 6:24
- Read Data from a Database - 8:23
- Common Data Pre-Processing Techniques
- Basic Data Cleaning in R: Remove NA - 17:12
- Additional Data Cleaning - 8:05
- Indexing and Subsetting Data - 11:59
- Summarising Based on Qualitative Attributes - 3:40
- Of Long and Wide - 5:36
- Pre-processing Tasks and the Pipe Operator - 9:14
- Introduction to dplyr for Data Summarizing-Part 1 - 6:11
- Introduction to dplyr for Data Summarizing-Part 2 - 4:44
- Start with Tidyverse - 3:17
- Column Renaming - 6:59
- Tidy Data: Long and Wide - 5:03
- Joining Tables - 5:58
- Summarising Based on Qualitative Attributes - 3:40
- Of Long and Wide - 5:36
- Basic Data Visualization
- What is Data Visualisation? - 9:33
- Some Principles of Data Visualisation - 6:46
- Exploratory Data Analysis (EDA) in R - 9:02
- More Exploratory Data Analysis with xda - 4:16
- Grammar of Graphics: ggplot2
- Start with qplot - 4:45
- More qplot Visualizations - 7:24
- Start with ggplot - 4:59
- Scatterplots with ggplot2 - 5:38
- Faceting With ggplot2 - 4:42
- More Faceting - 11:51
- Insert a Smoothing Line - 7:08
- Boxplots - 3:50
- More Boxplots - 11:21
- Histograms - 11:58
- Error Bars - 7:08
- Barplots For Discrete Numbers - 14:12
- Line Charts - 5:57
- Additional ggplot2 Themes - 4:32
- Real Life Data
- Use dplyr and ggplot2 - 6:07
- nobel1 - 16:26
- nobel2 - 7:35
- Mining and Visualising Information About the Olympic Games-Part 1 - 12:49
- Of Winter and Summer Olympic Games - 4:16
- Of Men and Women - 8:26
- Geographic Visualisations
- Brief Introduction - 4:17
- Work With R's Inbuilt Geospatial Data-Part 2 - 7:32
- Use ggplot2 For Geographic Data Visualisations - 14:11
Access
Lifetime
Content
5 hours
Lessons
52
Practical Time Series Data Analysis With Statistics and Machine Learning
Learn How To Work with Temporal Data Using Statistical Modeling & Machine Learning Techniques In R
By Minerva Singh | in Online Courses
In this course, you'll use easy-to-understand, hands-on methods to absorb the most valuable R Data Science basics and techniques. After this course, you'll understand the underlying concepts to understand what algorithms and methods are best suited for your data.
- Access 52 lectures & 5 hours of content 24/7
- Get an introduction to powerful R-based packages for time series analysis
- Learn commonly used techniques, visualization methods & machine/deep learning techniques that can be implemented for time series data
- Apply these frameworks to real-life data including temporal stocks & financial data
Instructor
Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.Important Details
- Length of time users can access this course: lifetime
- Access options: web & mobile streaming
- Certification of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Internet access required
Course Outline
- INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
- Course Information - 1:30
- Data and Scripts For the Course
- Install R and RStudio - 6:36
- Read in CSV & Excel Data - 9:56
- Remove Missing Values - 17:12
- More Data Cleaning - 8:05
- Exploratory Data Analysis - 18:53
- Start With Time Series Data
- Works With Dates in R - 7:33
- Pre-Processing Data With Times - 8:28
- Visualize Temporal Data in R - 12:35
- Components of Time Series Data - 9:03
- Moving Averages (MA) For Visualizing a Trend/Pattern - 4:06
- Detecting Significant Trend - 5:29
- Other Ways Of Identifying Trend in Time Series Data - 5:37
- Visualize Monthly Temporal Data - 7:46
- Identify Cyclical Behavior with Fourier Transforms - 4:21
- STL Decomposition - 3:49
- Work With Seasonality - 4:04
- Important Pre-Conditions of Time Series Modelling
- Is My Time Series Stationary? - 4:56
- Differencing: Make A Non-Stationary Time Series Stationary - 8:21
- Use Mean & Variance - 2:56
- Seasonal Differencing - 4:46
- Detrending Time Series With Linear Regression - 3:54
- Detrending Time Series With Mean Subtraction - 2:28
- Time Series Based Forecasting
- Simple Exponential Smoothing for Short Term Forecasts - 6:33
- Other Basic Forecasting Techniques - 5:04
- New Lecture
- Moving Averages (MA) For Forecasting - 2:50
- Simple Moving Average - 4:55
- Theta Lines - 5:22
- Forecasting On the Fly - 7:23
- Linear Regression For Predicting Values As a Function of Time - 7:38
- Linear Regression For Forecasting With Trend & Seasonality - 9:13
- Lags - 3:20
- Weekly Lag - 2:38
- Lagged Regression - 3:46
- Automatic ARIMA Model Fitting and Forecasting - 3:37
- Automatic ARIMA With Real Life Data - 4:40
- ARIMA With Fourier Terms - 7:47
- BATS For Forecasting - 6:47
- Machine Learning Techniques For Time Series Data
- Linear Regression With "timetk" - 6:03
- Linear Regression On Real Data - 8:58
- Machine Learning Regression Models for Non-Parametric Data For Forecasting - 7:07
- XGBoost For Time Series Forecasting - 4:30
- Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17
- Neural Network for Forecasting - 4:06
- RNNs With Temporal Data - 7:42
- Evaluate the Performance of an RNN Model - 7:30
- Detecting Sudden Changes/Major Events
- Detect An Anomaly in Time Series Data - 8:56
- Breaks For Additive Season and Trend (BFAST) For Time Series in R - 7:25
- Structural Change Detection - 6:25
- Structural Changes in Forex Regime - 4:57
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.