Ending In:

$1,600

Courses

8

Lessons

506

Enrolled

4,228

Courses

8

Lessons

506

Enrolled

4,228

Tensorflow and Keras Masterclass For Machine Learning and AI in Python

$200.07 Value

Tensorflow Masterclass For Machine Learning and Artificial Intelligence in Python

$199.99 Value

Python Regression Analysis: Statistics & Machine Learning

$199.99 Value

Complete Data Science Training with Python for Data Analysis

$199.99 Value

Complete Time Series Data Analysis Bootcamp In R

$199.99 Value

Practical Neural Networks & Deep Learning In R

$199.99 Value

Working With Classes: Classify and Cluster Data With R

$199.99 Value

Working With Classes: Classify and Cluster Data With Python

$199.99 Value

Access

Lifetime

Content

5 hours

Lessons

62

By Minerva Singh | in Online Courses

This course is your complete guide to practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.

- Access 62 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow & Keras installation
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow & Keras

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Course
- Tensorflow and Keras For Data Science - 2:12
- Data and Code
- Python Data Science Environment
- For Mac Users
- Install Tensorflow - 15:12
- Written Instructions for Tensorflow Install
- 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

- Introduction to Tensorflow
- A Brief Touchdown - 2:36
- A Brief Touchdown: Computational Graphs - 2:56
- Common Mathematical Operator
- A Tensorflow Session - 4:37
- Interactive Tensorflow Session - 1:38
- Constants and Variables in Tensorflow - 3:42
- Placeholders in Tensorflow

- Introduction to Keras
- What is Keras? - 3:29

- Some Preliminary Tensorflow and Keras Applications
- Theory of Linear Regression (OLS) - 10:44
- OLS From First Principles - 9:22
- Visualize the Results of OLS - 3:28
- Multiple Regression With Tensorflow-Part 1 - 5:08
- Estimate With Tensorflow Estimators - 3:05
- Multiple Regression With Tensorflow Estimators - 5:24
- More on Linear Regressor Estimator - 8:24
- GLM: Generalized Linear Model - 5:25
- Linear Classifier For Binary Classification - 9:33
- Accuracy Assessment For Binary Classification - 4:19
- Linear Classification with Binary Classification With Mixed Predictors - 8:15
- Softmax Classification With Tensorflow - 7:35

- Some Basic Concepts
- What is Machine Learning?
- 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 Tensorflow (Binary Class Problem) - 7:32
- Autoencoders in Tensorflow (Multiple Classes) - 5:43
- Autoencoders in Keras (Simple) - 5:43
- Autoencoders in Keras (Sparsity Constraints) - 4:32

- Neural Network for Tensorflow & Keras
- Multi Layer Perceptron (MLP) with Tensorflow - 6:24
- 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
- Deep Neural Network (DNN) Classifier With Tensorflow - 6:47
- Deep Neural Network (DNN) Classifier With Mixed Predictors - 8:11
- Deep Neural Network (DNN) Regression With Tensorflow - 5:24
- New Lecture
- Wide & Deep Learning (Tensorflow) - 11:34
- DNN Classifier With Keras - 3:30
- DNN Classifier With Keras-Example 2 - 4:23

- 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

Access

Lifetime

Content

5 hours

Lessons

62

By Minerva Singh | in Online Courses

This course is your complete guide to practical data science using the Tensorflow framework in Python. Here, you'll cover all the aspects of practical data science with Tensorflow, Google's powerful deep learning framework used by organizations everywhere.

- Access 62 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow installation & other Python data science packages
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment
- Learn statistical modeling w/ Tensorflow
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Course
- Welcome to the World of TensorFlow - 4:03
- Data and Code
- Python Data Science Environment
- For Mac Users
- Install Tensorflow - 15:12
- Written Instructions for Tensorflow Install

- 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

- Introduction to Tensorflow
- A Brief Touchdown - 2:36
- A Brief Touchdown: Computational Graphs - 2:56
- Common Mathematical Operator
- A Tensorflow Session - 4:37
- Interactive Tensorflow Session - 1:38
- Constants and Variables in Tensorflow - 3:42
- Placeholders in Tensorflow
- TensorBoard: Visualize Graphs in TensorFlow - 2:44
- Access TensorBoard Graphs - 2:55

- Some Preliminary Tensorflow and Keras Applications
- Theory of Linear Regression (OLS) - 10:44
- OLS From First Principles - 9:22
- Visualize the Results of OLS - 3:28
- Multiple Regression With Tensorflow-Part 1 - 5:08
- Estimate With Tensorflow Estimators - 3:05
- Multiple Regression With Tensorflow Estimators - 5:24
- More on Linear Regressor Estimator - 8:24
- GLM: Generalized Linear Model - 5:25
- Linear Classifier For Binary Classification - 9:33
- Accuracy Assessment For Binary Classification - 4:19
- Linear Classification with Binary Classification With Mixed Predictors - 8:15

- Some Basic Concepts
- What is Machine Learning?
- Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17

- Unsupervised and Supervised Learning With Tensorflow
- What is Unsupervised Learning? - 5:32
- K-means Clustering: Theory - 5:44
- Implement K-Means on Real Data - 5:37
- Softmax Classification - 7:35
- Random Forests (RF) Theory - 7:14
- Random Forest (RF) for Binary Classification - 7:09
- Random Forest (RF) for Multiclass Classification - 5:07
- kNN Theory
- Implement kNN - 3:22

- Neural Network for Tensorflow & Keras
- Multi Layer Perceptron (MLP) with Tensorflow - 6:24
- 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
- Deep Neural Network (DNN) Classifier With Tensorflow - 6:47
- Deep Neural Network (DNN) Classifier With Mixed Predictors - 8:11
- Deep Neural Network (DNN) Regression With Tensorflow - 5:24
- New Lecture
- Wide & Deep Learning (Tensorflow) - 11:34
- DNN Classifier With Keras - 3:30
- DNN Classifier With Keras-Example 2 - 4:23

- 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
- LSTM for Stock Prices - 7:21

Access

Lifetime

Content

6 hours

Lessons

50

By Minerva Singh | in Online Courses

This course offers a complete guide to practical data science using Python. You'll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can giver your company a competitive edge and boost your career to the next level.

- Access 50 lectures & 6 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Data Science in Python Bootcamp
- Welcome to the Course - 1:40
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26
- Conclusion to Section 1 - 2:36

- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 9:14
- Conclusions to Section 4 - 2:06

- Data Pre-Processing/Wrangling
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47

- Basic Statistical Data Analysis
- What is Statistical Data Analysis? - 10:08
- Some Pointers on Collecting Data for Statistical Studies - 8:38
- Explore the Quantitative Data: Descriptive Statistics - 9:05
- Group By Qualitative Categories - 10:25
- Visualize Descriptive Statistics-Boxplots - 5:28
- Common Terms Relating to Descriptive Statistics - 5:15
- Data Distribution- Normal Distribution - 4:07
- Check for Normal Distribution - 6:23
- Standard Normal Distribution and Z-scores - 4:10
- Confidence Interval-Theory - 6:06
- Confidence Interval-Calculation - 5:20

- Regression Modelling for Defining Relationship bw Variables
- Explore the Relationship Between Two Quantitative Variables - 4:26
- Correlation Analysis - 8:26
- Linear Regression-Theory - 10:44
- Linear Regression-Implementation in Python - 11:18
- Conditions of Linear Regression-Check in Python - 12:03
- Polynomial Regression - 3:53
- GLM: Generalized Linear Model - 5:25
- Logistic Regression - 11:10

- Machine Learning for Data Science
- How is Machine Learning Different from Statistical Data Analysis? - 5:36
- What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

- Machine Learning Based Regression Modelling
- What is this section about - 10:10
- Data Preparation for Supervised Learning - 9:47
- Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42
- Random Forest (RF) Regression - 9:20
- Support Vector Regression - 4:30
- kNN Regression - 3:48
- Gradient Boosting-regression - 4:46
- Theory Behind ANN and DNN - 9:17
- Regression with MLP - 3:48

Access

Lifetime

Content

11 hours

Lessons

117

By Minerva Singh | in Online Courses

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

- Access 117 lectures & 11 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more
- Discover how to create artificial neural networks & deep learning structures

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Data Science in Python Bootcamp
- What is Data Science? - 3:37
- Introduction to the Course & Instructor
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26
- Conclusion to Section 1 - 2:36

- Introduction to Python Pre-Requisites for Data Science
- Different Types of Data Used in Statistical & ML Analysis - 3:37
- Different Types of Data Used Programatically - 3:46
- Python Data Science Packages To Be Used - 3:16
- Conclusion to Section 2 - 1:59

- Introduction to Numpy
- Numpy: Introduction - 3:46
- Create Numpy Arrays
- Numpy Operations - 16:48
- Matrix Arithmetic and Linear Systems - 7:34
- Numpy for Basic Vector Arithmetic - 6:16
- Numpy for Basic Matrix Arithmetic
- Broadcasting for Numpy - 3:52
- Solve for Equations - 5:04
- Numpy For Statistics - 7:23
- New Lecture
- Conclusions to Section 3 - 2:24

- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 9:14
- Conclusions to Section 4 - 2:06

- Data Pre-Processing/Wrangling
- Rationale behind this section - 4:19
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Drop Column/Row - 4:42
- Subset and Index Data - 9:44
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Crosstabulation - 4:54
- Reshaping - 9:26
- Pivotting - 8:30
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47
- Conclusion to Section 5

- Introduction to Data Visualization
- What is Data Visualisation? - 9:33
- Theory Behind Data Visualisation - 6:46
- Histograms- Visualise the Distribution of Quantitative Variables - 12:13
- Boxplot- Visualise the Data Summary - 5:54
- Scatterplot- Visualise The Relationship Between Quantitative Variables - 11:57
- Line Chart - 12:31
- Barplot - 22:25
- Pie Chart - 5:29
- Conclusion to Section 6 - 2:14

- Basic Statistical Data Analysis
- What is Statistical Data Analysis? - 10:08
- Some Pointers on Collecting Data for Statistical Studies - 8:38
- Explore the Quantitative Data: Descriptive Statistics - 9:05
- Group By Qualitative Categories - 10:25
- Visualize Descriptive Statistics-Boxplots - 5:28
- Common Terms Relating to Descriptive Statistics - 5:15
- Data Distribution- Normal Distribution - 4:07
- Check for Normal Distribution - 6:23
- Standard Normal Distribution and Z-scores - 4:10
- Confidence Interval-Theory - 6:06
- Confidence Interval-Calculation - 5:20
- Conclusion to Section 7 - 1:28

- Statistical Inference & Relationship Between Variables
- What is Hypothesis Testing? - 5:42
- Test the Difference Between Two Groups - 7:30
- Test the Difference Between More Than Two Groups - 10:55
- Explore the Relationship Between Two Quantitative Variables - 4:26
- Correlation Analysis - 8:26
- Linear Regression-Theory - 10:44
- Linear Regression-Implementation in Python - 11:18
- Conditions of Linear Regression-Check in Python - 12:03
- Polynomial Regression - 3:53
- GLM: Generalized Linear Model - 5:25
- Logistic Regression - 11:10
- Conclusion to Section 8 - 1:52

- Machine Learning for Data Science
- How is Machine Learning Different from Statistical Data Analysis? - 11:12
- What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

- Unsupervised Learning
- Some Basic Pointers - 1:38
- kmeans-theory - 2:31
- KMeans-implementation on the iris data
- Quantifying KMeans Clustering Performance - 3:53
- kmeans clustering on real data - 4:16
- How Do We Select the Number of Clusters? - 5:38
- Theory of hierarchical clustering - 4:10
- Implement hierarchical clustering - 9:19
- Theory of Principal Component Analysis (PCA) - 2:37
- Implement PCA - 3:52
- Conclusion to Section 10 - 2:08
- Data Preparation for Supervised Classification - 9:47
- Classification accuracy evaluation - 9:42
- Random Forest (RF) For Regression - 9:20

- Supervised Learning
- What is this section about? - 10:10
- Logistic regression with classification - 8:26
- Random Forest (RF) For Classification - 12:02
- Linear Support Vector Machine (SVM) Classification - 3:10
- Non-Linear Support Vector Machine (SVM) Classification - 2:06
- Support Vector Regression - 4:30
- kNN Classification - 7:46
- kNN Regression - 3:48
- Gradient Boosting Machine (GBM) Classification - 5:54
- GBM Classification
- Gradient Boosting Regression (GBR)
- Voting Classifier - 4:00
- Conclusion to Section 11 - 2:46

- Artificial Neural Networks (ANN) and Deep Learning
- Introduction
- Perceptrons for Binary Classification - 4:27
- Getting Started with ANN-binary classification - 3:26
- Multi-label classification with MLP - 4:53
- Regression with MLP - 3:48
- MLP with PCA on a Large Dataset - 7:33
- Start With Deep Neural Network (DNN)
- Start with H20 - 4:14
- Default H2O Deep Learning Algorithm - 3:20
- Specify the Activation Function - 2:06
- Deep Learning Predictions - 5:02
- Conclusion to section 12 - 2:03

Access

Lifetime

Content

5 hours

Lessons

52

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**

**Important Details**

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

**Requirements**

- Internet required

- 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
- 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

Access

Lifetime

Content

5 hours

Lessons

51

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!

- 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

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- 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

Access

Lifetime

Content

7.5 hours

Lessons

66

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 66 lectures & 7.5 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**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Course
- Welcome to the Course and Instructor Info
- Data and Code
- Install R and RStudio - 6:36
- Basic Data Cleaning 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
- Remove Missing Values - 17:12
- More 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 exploratory data analysis - 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
- How is Machine Learning Different From Statistical Modelling? - 5:36
- What is Machine Learning? - 5:32

- Unsupervised Learning 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

- Feature/Dimension Reduction
- Theory behind dimension reduction - 3:17
- Principal Component Analysis (PCA) - 13:10
- More PCA - 4:27

- Feature Selection
- 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

- Supervised Learning Theory
- Some Basic Supervised Learning Concepts - 10:10
- Prepare data for ML analysis - 3:31

- Supervised Learning: Classification
- Generalised Linear Models (GLMs) - 5:25
- Logistic Regression Models as Binary Classifiers
- Logistic Regression Models as Binary Classifiers - 9:10
- Binary Classifier with PCA - 6:29
- Some Pointers on Evaluating Accuracy - 9:42
- Obtain Binary Classification Accuracy Metrics - 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

46

By Minerva Singh | in Online Courses

In this course, youâ€™ll start by absorbing the most valuable Python Data Science basics and techniques. You'll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You'll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.

- Access 46 lectures & 4 hours of content 24/7
- Harness the power of Anaconda/iPython for practical data science
- Carry out basic data pre-processing & wrangling in Python
- Implement dimensional reduction techniques (PCA) & feature selection
- Explore neural network & deep learning based classification

**Instructor**

**Important Details**

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

**Requirements**

- Internet required

- Introduction to the Course
- What is Data Science? - 3:37
- Data and Scripts for the Course
- Introduction to the Python Data Science Tool - 10:57
- For Mac Users - 4:05
- Introduction to the Python Data Science Environment - 19:15
- Some Miscellaneous IPython Usage Facts - 5:25
- Online iPython Interpreter - 3:26

- Introduction to Pandas
- What are Pandas? - 12:06
- Read CSV Data in Python - 5:42
- Read in Excel File - 5:31
- Read HTML Data - 12:06
- Read JSON Data - 9:14

- Data Pre-Processing/Wrangling
- Remove NA Values - 10:28
- Basic Data Handling: Starting with Conditional Data Selection - 5:24
- Subset and Index Data - 9:44
- Basic Data Grouping Based on Qualitative Attributes - 9:47
- Rank and Sort Data - 8:03
- Concatenate - 8:16
- Merge - 10:47

- Unsupervised Learning: Clustering and Dimensionality Reduction
- Some Basic Pointers - 1:38
- kmeans-theory - 2:31
- KMeans-implementation on the iris data
- Quantifying KMeans Clustering Performance - 3:53
- kmeans clustering on real data - 4:16
- How Do We Select the Number of Clusters? - 5:38
- Theory of hierarchical clustering - 4:10
- Implement hierarchical clustering - 9:19
- Theory of Principal Component Analysis (PCA) - 2:37
- Implement PCA - 3:52

- Supervised Learning
- What is this section about? - 10:10
- Logistic regression with classification - 8:26
- Random Forest (RF) For Classification - 12:02
- Linear Support Vector Machine (SVM) Classification - 3:10
- Non-Linear Support Vector Machine (SVM) Classification - 2:06
- kNN Classification - 7:46
- kNN Regression - 3:48
- Gradient Boosting Machine (GBM) Classification - 5:54
- GBM Classification
- Voting Classifier - 4:00

- Artificial Neural Networks (ANN) and Deep Learning
- Introduction
- Perceptrons for Binary Classification - 4:27
- Getting Started with ANN-binary classification - 3:26
- Multi-label classification with MLP - 4:53
- Start with H20 - 4:14
- Specify the Activation Function - 2:06
- Deep Learning Predictions - 5:02

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