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The Deep Learning & Data Analysis Certification Bundle

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2.0 hours
Lessons
18

Business Data Visualization, Analytics & Reporting with Google Data Studio

Master a Free Tool for Data Analytics & Business Intelligence

By Minerva Singh | in Online Courses

Google Data Studio (GDS) is a free dashboard and reporting tool (which lives in the cloud). It allows you to create dynamic, collaborative reports, and visualization dashboards. Paid Business Intelligence and Data Analytics Tools Like Tableau Are have either plateaued or will plateau soon. Many of these are either too expensive for small or teams or have a steep learning curve for beginners. This course helps you start with GDS and become proficient in producing powerful visualizations and reports.

4.5/5 average rating: ★ ★ ★ ★

  • Access 18 lectures & 2 hours of content 24/7
  • Gain familiarity with the interface of Google Data Studio
  • Lean to add your own data to GDS
  • Connect to different analytic tools such as Youtube Analytics & Google Ads
  • Implement different data tabulation techniques
  • Present the results as powerful & interactive reports
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program

  • Say A Quick Hello To Google Data Studio

    • Why Google Data Studio (GDS)? - 1:39
    • Data Used In The Course
  • Get Started With Google Data Studio (GDS)

    • The GDS Interface and Functionality - 35:33
  • Data Visualization With GDS

    • Add Your Data To GDS - 3:12
    • Data At A Glance - 4:03
    • Introduction To Pivot Tables - 8:45
    • More On Pivot Tables - 5:42
    • Visualize Discrete Data - 7:53
    • Filter Your Data - 4:55
    • More Filtering - 4:29
    • Visualize Time Series - 7:17
    • Visualize Youtube Temporal Analytics - 4:58
    • Settle on Date Ranges - 4:00
    • Visualize The Relationship Between Two Numerical Variables - 9:16
    • Bubble Charts For Quantitative Variables - 7:53
    • Candlesticks For Stock Market Data - 4:31
  • Geo-Visualization in GDS

    • Map Making With GDS - 15:36
  • Report Your Findings

    • Data To Dashboard With GDS - 16:02

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1.0 hours
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6

Harness the Power of the H2O Framework For Machine Learning in R

Master Powerful R Package for Machine Learning, Artificial Neural Networks, & Deep Learning

By Minerva Singh | in Online Courses

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks, and deep learning via a powerful framework, H2O in R, you can give your company a competitive edge and boost your career to the next level. This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R based data science as you will have the keys to a very powerful R supported data science framework.

4.4/5 average rating: ★ ★ ★ ★

  • Access 6 lectures & 1 hour of content 24/7
  • Be familiar with powerful R-based deep learning packages such as H2O
  • Learn the important concepts of machine learning without the jargon
  • Implement both supervised & unsupervised algorithms using H2O
  • Do Artificial Neural Networks (ANN) & Deep Neural Networks (DNN)
  • Work with real data within the framework
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • Welcome to the Course
    • What is This Course About? - 2:30
    • Data and Code
    • Install R and RStudio - 6:36
    • Common data types - 3:37
    • Install H2O - 5:37
  • Read in Data From Different Sources
    • Read CSV Files - 9:56

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Lessons
50

Statistics & Machine Learning For Regression Modelling 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.

4.3/5 average rating: ★ ★ ★ ★

  • 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
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

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

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6.0 hours
Lessons
52

Master PyTorch For Artificial Neural Networks (ANN) & Deep Learning

Get Introduced to Deep Neural Networks & Become a Pro in Practical PyTorch-Based Data Science

By Minerva Singh | in Online Courses

This is a complete neural network and deep learning training with PyTorch in Python. It's a full 6-hour PyTorch Bootcamp that will help you learn basic machine learning, how to build neural networks, and explore deep learning using one of the most important Python Deep Learning frameworks. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of frameworks such as PyTorch is revolutionizing deep learning. By gaining proficiency in PyTorch, you can give your company a competitive edge and boost your career to the next level.

4.5/5 average rating: ★ ★ ★ ★

  • Access 52 lectures & 6 hours of content 24/7
  • Learn implement deep learning models w/ PyTorch
  • Implement PyTorch based deep learning algorithms on imagery data
  • Configure the Anaconda Environment for getting started w/ PyTorch
  • Implement common machine learning algorithms for Image Classification
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to the Course
    • Welcome to the Course - 2:32
    • Data and Code
    • Get Started With the Python Data Science Environment: Anaconda - 10:57
    • Anaconda for Mac Users - 4:05
    • The iPython Environment - 19:13
    • Why PyTorch? - 9:42
    • Install PyTorch - 3:36
    • Installing PyTorch-Written Instructions
    • Further Installation Instructions for Mac - 1:19
    • Working With CoLabs - 7:13
  • Non PyTorch Python Data Science Packages
    • Python Packages for Data Science - 3:16
    • Introduction to Numpy - 3:46
    • Create Numpy Arrays - 10:51
    • Numpy Operations - 16:48
    • Numpy for Basic Vector Arithmetric - 6:16
    • Numpy for Basic Matrix Arithmetic - 6:32
    • PyTorch Basics: What Is a Tensor? - 2:36
    • Explore PyTorch Tensors and Numpy Arrays - 4:26
    • Some Basic PyTorch Tensor Operations - 3:40
  • Other Python Data Science Packages For Dealing With Data
    • Read CSV - 5:42
    • Read Excel - 5:31
    • Basic Data Exploration With Pandas - 11:20
  • Basic Statistical Analysis With PyTorch
    • Ordinary Least Squares (OLS) Regression- Theory - 10:44
    • OLS Linear Regression-Without PyTorch - 11:18
    • OLS Linear Regression From First Principles-Theory - 12:48
    • OLS Linear Regression From First Principles-Without PyTorch - 9:22
    • OLS Linear Regression From First Principles-With PyTorch - 4:33
    • More OLS With PyTorch - 11:23
    • Generalised Linear Models (GLMs)-Theory - 5:25
    • Logistic Regression-Without PyTorch - 5:06
    • Logistic Regression-With PyTorch - 4:52
  • Introduction to Artificial Neural Networks (ANN)
    • Introduction to ANN - 9:17
    • PyTorch ANN Syntax - 5:24
    • What Are Activation Functions? Theory - 5:50
    • More on Backpropagation - 10:20
    • Bringing Them Together - 14:46
    • Setting Up ANN Analysis With PyTorch - 6:21
    • DNN Analysis with PyTorch - 11:26
    • More DNNs - 8:43
    • DNNs For Identifying Credit Card Fraud - 9:40
    • An Explanation of Accuracy Metrics - 4:19
  • Neural Networks on Images
    • What Are Images? - 4:54
    • Read in Images in Python - 7:46
    • Basic Image Conversions - 3:07
    • Why AI and Deep Learning? - 9:51
    • Artificial Neural Networks (ANN) For Image Classification - 10:50
    • Deep Neural Networks (DNN) For Image Classification - 5:27
  • Introduction to Artificial Intelligence (AI) and Deep Learning
    • What is CNN? - 11:25
    • Implement CNN on Imagery Data - 7:33
    • More on CNN - 4:36
    • Introduction to Transfer Learning: Theory - 7:41
    • Implement CNN Using a Pre-Trained Model - 7:25

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Lessons
61

Image Processing & Analysis Bootcamp with OpenCV and Deep Learning in Python

Implement Both Machine Learning & Deep Learning Techniques in a Hands-On Manner

By Minerva Singh | in Online Courses

This course is your complete guide to practical image processing and computer vision tasks using Python. It covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning. By gaining proficiency in Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

4.1/5 average rating: ★ ★ ★ ★

  • Access 61 lectures & 5 hours of content 24/7
  • Get started with the Python data science environment
  • Read in image data into the Jupiter/iPython environment
  • Carry out basic image pre-processing & computer vision tasks with Python
  • Implement Unsupervised Learning Algorithms on image data
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • Welcome to Image Processing And Analysis in Python
    • Brief Introduction to the Course - 2:31
    • Data and Code
    • Get Started With the Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • Introduction to iPython/Jupyter - 19:13
    • Working With Colabs - 7:13
  • Getting Started With Basic Image Processing in Python
    • What Are Images? - 4:54
    • Read in Images in Python - 7:46
    • Some Basic Image Conversions - 3:07
    • Basic Image Resizing - 4:01
    • Basic Image Resizing - 4:01
    • What is Interpolation? A Geographic Perspective - 5:08
    • Basic Image Transformations - 6:08
    • Contrast Stretching - 6:20
    • Filtering Images - 6:21
  • Introduction to Computer Vision
    • What is Computer Vision? - 4:54
    • Read in Images Using OpenCV - 5:59
    • Image Filtering With OpenCV - 7:30
    • Edge Detection With OpenCV - 5:19
    • More Edge Detection: Sobel Method - 3:25
    • Corner Detection - 1:31
    • Face Detection With Haar Features: Theory - 5:42
    • Face Detection - 5:32
  • Introduction to Some Concepts
    • What is Machine Learning? - 5:32
  • Unsupervised Learning Methods
    • What is Unsupervised Learning? - 1:38
    • Theory Behind PCA - 2:37
    • Implement PCA on Images - 4:36
    • PCA For Image reconstruction - 4:14
    • Randomised PCA - 2:45
    • Theory Behind K-means - 1:57
    • K-Means For Image Reconstruction - 1:48
    • Classify High Dimensional Data With t-SNE - 4:55
    • Practical Case Study: Identify Flowers - 3:09
    • Cluster the Flowers: Read in Images - 7:45
    • Implement PCA - 4:04
    • Implement t-SNE - 2:25
  • Supervised Learning: Classifying Images
    • Brief Introduction to Supervised Learning - 10:10
    • Implement SVM to Classify Digits - 7:00
    • Accuracy Assessment - 9:42
    • rf - 4:19
  • Start With Deep Learning
    • Why Deep Learning? - 9:51
    • Tensorflow Installation - 15:12
    • Written Tensorflow Installation Instructions
    • Install Keras on Windows 10 - 5:16
    • Install Keras on Mac - 4:19
    • Written Keras Installation Instructions
  • Deep Learning For Image Classification
    • Introduction to CNN - 11:25
    • Implement a CNN for Multi-Class Supervised Classification - 7:27
    • More on CNN - 4:36
    • Pre-Requisite For Working With Imagery Data - 2:33
    • CNN on Image Data-Part 1 - 10:41
    • CNN on Image Data-Part 2 - 6:38
    • More on TFLearn - 7:54
    • CNN Workflow for Keras - 4:04
    • CNN With Keras - 4:10
    • CNN on Image Data with Keras-Part 1 - 2:27
    • CNN on Image Data with Keras-Part 2 - 5:05
  • Transfer Learning
    • What is Transfer Learning? - 7:41
    • Implement a Pre-Built Transfer Learning Model - 6:57
  • Unsupervised Deep Learning
    • Simple Autoencoders - 5:43
    • Add Sparsity Constraint - 4:32

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Lessons
35

Keras Bootcamp For Deep Learning & AI in Python

Master Keras: An Important Deep Learning Framework for Deep Learning & Artificial Intelligence

By Minerva Singh | in Online Courses

This is a full 3-hour Python Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Deep Learning frameworks—Keras. This course is your complete guide to the practical machine and deep learning using the Keras framework in Python. This means, this course covers the important aspects of Keras (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Keras based data science.

4.5/5 average rating: ★ ★ ★ ★

  • Access 35 lectures & 3 hours of content 24/7
  • Get started w/ Jupyter notebooks for implementing data science techniques
  • Understand the basics of Keras syntax
  • Create artificial neural networks & deep learning structures w/ Keras
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction to the Course
    • What is Keras? - 3:29
    • Data and Code
    • Python Data Science Environment - 10:57
    • For Mac Users - 4:05
    • Install Keras on Windows 10 - 5:16
    • Install Keras with Mac - 4:19
    • Written Keras Installation Instructions
  • Introduction to Python Data Science Packages
    • Python Packages For Data Science - 3:16
    • Introduction to Numpy - 3:46
    • Create Numpy - 10:51
    • Numpy for Statistical Operations - 7:23
    • Introduction to Pandas - 12:06
    • Read in CSV - 7:13
    • Read in Excel - 5:31
    • Basic Data Cleaning - 4:30
  • Some Basic Concepts
    • What is Machine Learning? - 5:32
    • Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17
  • Unsupervised Learning With Tensorflow and Keras
    • What is Unsupervised Learning? - 5:32
    • Autoencoders for Unsupervised Classification - 1:46
    • Autoencoders in Keras (Simple) - 5:43
    • Autoencoders in Keras (Sparsity Constraints) - 4:32
  • Neural Network With Keras
    • Multi Layer Perceptron (MLP) With Keras - 3:31
    • Keras MLP For Binary Classification - 4:01
    • Keras MLP for Multiclass Classification - 6:01
    • Keras MLP for Regression - 3:27
  • Deep Learning For Tensorflow & Keras
    • DNN Classifier With Keras - 3:30
    • DNN Classifier With Keras-Example 2 - 4:23
  • Convolutional Neural Networks (CNN)
    • What are CNNs? - 11:25
    • Implement a CNN With Keras - 4:04
    • CNN on Image Data with Keras-Part 2 - 5:05
  • Autoencoders with Convolution Neural Networks (CNN)
    • Autoencoders With CNN-Tensorflow - 7:15
    • Autoencoders With CNN- Keras - 4:46
  • Recurrent Neural Network (RNN)
    • Introduction to RNN - 5:40
    • LSTM for Time Series - 6:24
    • LSTM for Stock Prices - 7:21

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Lessons
51

Complete Artificial Neural Networks & Deep Learning In R

Discuss Artificial Intelligence & Machine Learning for Practical Data Science in R

By Minerva Singh | in Online Courses

Dive into R data science using real data in this comprehensive, hands-on course. Get up to speed with data science packages like caret, h20, MXNET, as well as underlying concepts like which algorithms and methods are best suited for different kinds of data. Help your company scale by becoming an R expert!

4.8/5 average rating: ★ ★ ★ ★

  • Access 51 lectures & 5 hours of content 24/7
  • Get introduced to powerful R-based deep learning packages such as h2o & MXNET
  • Explore deep neural networks (DNN), convolution neural networks (CNN) & recurrent neural networks (RNN)
  • Learn to apply these frameworks to real life data for classification & regression applications
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

Course Outline

  • INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
    • Introduction - 1:31
    • Data and Scripts For the Course
    • Install R and RStudio - 6:36
    • Read CSV and Excel Data - 9:56
    • Read in Online CSV - 4:04
    • Read in Data from Online HTML Tables-Part 1 - 4:13
    • Read in Data from Online HTML Tables-Part 2 - 6:24
    • Remove NAs - 17:12
    • More Data Cleaning - 8:05
    • Introduction to dplyr for Data Summarizing-Part 1 - 6:11
    • Introduction to dplyr for Data Summarizing-Part 2 - 4:44
    • Exploratory Data Analysis(EDA): Basic Visualizations with R - 18:53
    • More Exploratory Data Analysis with xda - 4:16
    • Difference Between Supervised & Unsupervised Learning - 5:32
  • Introduction to Artificial Neural Networks (ANN)
    • Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17
    • Neural Network for Binary Classifications - 6:51
    • Neural Network with PCA for Binary Classifications - 3:57
    • Evaluate Accuracy - 4:19
    • Multi Layer Perceptron (MLP) - 4:45
    • Neural Network for Multiclass Classifications - 7:04
    • Neural Network for Image Type Data - 4:31
    • Multi-class Classification Using Neural Networks with caret - 8:26
    • Neural Network for Regression - 4:31
    • More on Neural Networks- with neuralnet - 4:31
    • Identify Variable Importance in Neural Networks - 8:49
  • Start With Deep Neural Network (DNN)
    • Implement a Simple DNN With "neuralnet" for Binary Classifications - 8:09
    • Implement a Simple DNN With "deepnet" for Regression - 4:15
    • A Package for DNN Modelling in R-H2o - 5:37
    • Working with External Data in H2o - 4:21
    • Implement an ANN with H2o For Multi-Class Supervised Classification - 10:30
    • Implement a DNN with H2o For Multi-Class Supervised Classification - 6:17
    • Implement a (Less Intensive) DNN with H2o For Supervised Classification - 3:58
    • Identify Variable Importance - 9:02
    • What Are Activation Functions? - 5:50
    • Implement a DNN with H2o For Regression - 3:51
    • Autoencoders for Unsupervised Learning - 1:46
    • Autoencoders for Credit Card Fraud Detection - 4:11
    • Use the Autoencoder Model for Anomaly Detection - 5:00
    • Autoencoders for Unsupervised Classification - 6:57
  • ANN & DNN With MXNet Package in R
    • Install MXnet in R and RStudio - 3:13
    • Install MxNet in R
    • Implement an ANN Based Classification Using MXNet - 8:29
    • Implement an ANN Based Regression Using MXNet - 3:48
    • Implement a DNN Based Multi-Class Classification With MXNet - 10:46
    • Evaluate Accuracy of the DNN Model - 2:47
    • Implement MXNET via "caret" - 6:16
  • Convolution Neural Networks (CNN)
    • What is a CNN? - 11:25
    • Implement a CNN for Multi-Class Supervised Classification - 8:31
    • More About Our CNN Model Accuracy - 5:52
    • Implement CNN on Actual Images with MxNet - 7:44
    • RNNs With Temporal Data - 7:42

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52

Data Analysis Masterclass With Statistics & Machine Learning In R

Learn How to Work With Time Series/Temporal Data Using Statistical Modelling & Machine Learning Techniques in R

By Minerva Singh | in Online Courses

This course is your complete guide to time series analysis using R. So, all the main aspects of analyzing temporal data will be covered n depth. You’ll start by absorbing the most valuable R Data Science basics and techniques. It uses easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. 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.

4.3/5 average rating: ★ ★ ★ ★

  • 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
Minerva Singh | Best Selling Instructor & Data Scientist
4.5/5 Instructor Rating: ★ ★ ★ ★

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.

63,454 Total Students
11,305 Reviews

Important Details

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

Requirements

  • Any device with basic specifications

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

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Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.