The Deep Learning & Data Analysis Certification Bundle

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

• 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 CSV Files - 9:56

• 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: 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

• 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

• 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

• Data Transformations
• Robust Regression: Deal With Outliers - 6:58
• Deal With Heteroelasticity - 7:12
• Conclusion to Section 5 - 1:12

• 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

• 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

• 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

• 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

• Read CSV - 5:42
• Read Excel - 5:31
• Basic Data Exploration With Pandas - 11:20

• 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 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

• 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

• 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

• 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

• 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

• 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

• What is Machine Learning? - 5:32

• 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

• Brief Introduction to Supervised Learning - 10:10
• Implement SVM to Classify Digits - 7:00
• Accuracy Assessment - 9:42
• rf - 4:19

• 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

• 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

• What is Transfer Learning? - 7:41
• Implement a Pre-Built Transfer Learning Model - 6:57

• Simple Autoencoders - 5:43
• Add Sparsity Constraint - 4:32

• 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

• 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

• What is Machine Learning? - 5:32
• Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks) - 9:17

• 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

• 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

• DNN Classifier With Keras - 3:30
• DNN Classifier With Keras-Example 2 - 4:23

• What are CNNs? - 11:25
• Implement a CNN With Keras - 4:04
• CNN on Image Data with Keras-Part 2 - 5:05

• Autoencoders With CNN-Tensorflow - 7:15
• Autoencoders With CNN- Keras - 4:46

• Introduction to RNN - 5:40
• LSTM for Time Series - 6:24
• LSTM for Stock Prices - 7:21

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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