Table of contents
Course Overview
1m 48s
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Course Overview | 1m 48s
Frame Business Problems as ML Problems
1h 21m 54s
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Understanding When to Use Machine Learning | 9m 22s
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Exploring Supervised Learning Classification | 8m 47s
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Exploring Supervised Learning Regression | 5m 42s
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Examining Supervised Learning Problem Types Time Series Forecasting | 5m 43s
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Exploring Unsupervised Learning Problem Types Clustering | 7m 22s
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Exploring Unsupervised Learning Problem Types Association | 5m 28s
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Understanding Reinforcement Learning | 6m 39s
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Exploring Deep Learning | 8m 17s
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Exploring Computer Vision Using Convolutional Neural Networks | 6m 49s
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Processing Sequential Data Using Recurrent Neural Networks | 6m 33s
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Employing Transfer Learning | 4m 33s
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Frame Business Problem as ML Problems Review | 6m 39s
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Frame Business Problems as ML Problems
Select the Appropriate Model(s) for a Given ML Problem
1h 58m 5s
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Introducing Amazon SageMaker | 4m 55s
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Reviewing SageMaker's XGBoost Algorithm | 5m 37s
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Exploring SageMaker's Linear Learner Algorithm | 3m 25s
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Understanding SageMaker's K Nearest Neighbors (k NN) Algorithm | 4m 27s
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Examining SageMaker's Factorization Machines Algorithm | 5m 15s
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Discovering SageMaker's DeepAR Forecasting Algorithm | 5m 37s
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Reviewing SageMaker's Principal Component Analysis (PCA) Algorithm | 3m 2s
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Exploring SageMaker's Random Cut Forest (RCF) Algorithm | 4m 3s
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Understanding SageMaker's IP Insights Algorithm | 3m 53s
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Examining SageMaker's K Means Algorithm | 3m 23s
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Discovering SageMaker's Object2Vec Algorithm | 4m 41s
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Reviewing SageMaker's Latent Dirichlet Allocation (LDA) Algorithm | 3m 43s
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Exploring SageMaker's Neural Topic Model (NTM) Algorithm | 3m 7s
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Understanding SageMaker's BlazingText Algorithm | 3m 48s
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Examining SageMaker's Sequence-to-Sequence Algorithm | 2m 47s
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Discovering SageMaker's Image Classification Algorithm | 3m 29s
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Reviewing SageMaker's Object Detection Algorithm | 2m 27s
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Exploring SageMaker's Semantic Segmentation Algorithm | 2m 53s
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Comparing Machine Learning Algorithms on a Single Dataset using Amazon SageMaker | 45m
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Select the Appropriate Model for a Given ML Problem Review | 2m 33s
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Select the Appropriate Model(s) for a Given ML Problem
Train ML Models
1h 51m 56s
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Exploring the SageMaker Infrastructure | 5m 21s
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Splitting, Shuffling, and Bootstrapping Data for Training | 4m 52s
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Optimization Techniques in Machine Learning Training | 4m 4s
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Training A SageMaker Model Using a Built In Algorithm | 7m 9s
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Training s SageMaker Model Using a Training Script | 5m 59s
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Using Apache Spark with Amazon SageMaker | 4m 40s
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Debugging ML Models Using Amazon SageMaker Debugger | 4m 2s
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Using Spot Instances for XGBoost Training | 3m 27s
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Distributed Training In Amazon Sagemaker | 3m 55s
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Retraining a ML Model Using Amazon SageMaker Canvas | 4m 45s
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Linear Regression Performed Using Amazon SageMaker | 1h 0m
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Train ML Models Review | 3m 42s
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Train ML Models
Perform Hyperparameter Optimization
54m 59s
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Understanding Overfitting and UnderFitting in Machine Learning | 8m 50s
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Understanding Hyperparameter Tuning | 6m 35s
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Using Regularization Techniques to Improve Accuracy | 6m 9s
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Prevent Overfitting Using Cross Validation Techniques | 4m 37s
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Optimizing Hyperparameters in a Linear Model | 6m 32s
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Optimizing Hyperparameters in a Tree Based Model | 5m 42s
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Understanding Neural Network Architecture | 3m 59s
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Optimizing Hyperparameters in a Neural Network | 6m 36s
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Creating a Hyperparameter Tuning Job Using Amazon SageMaker | 0m
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Perform Hyperparameter Optimization Review | 5m 59s
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Perform Hyperparameter Optimization
Evaluate ML Models
1h 26m 41s
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Evaluating a Binary and Multi-class Classification Model Using a Confusion Matrix | 3m 27s
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Evaluating a Classification Model Using Core Metrics | 6m 18s
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Assessing a Regression Model Using Core Metrics | 4m 32s
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Performing Online Model Evaluation | 3m 45s
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Comparing ML Models Using Production Parameters | 3m 54s
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Training Reports Utilized in SageMaker Debugger to Improve Your Models | 1h 0m
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Utilizing Training Reports in SageMaker Debugger to Improve Your Models | 0m
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Evaluate ML Models Review | 4m 45s
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Evaluate ML Models
Conclusion
2m 39s
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Course Summary | 2m 39s
About the author
Saravanan Dhandapani
I have worked in IT design, development, and architecture for over a decade for some of the top fortune 100 companies. I have designed and architected enterprise applications and developed scalable and portable software. I am a Google Certified Professional Architect. Critical areas where I have worked are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT, and so on, and cloud technologies, including AWS and GCP.
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