Section 1 : INTRODUCTION, DATAML LINGO, AWS DATA STORAGE
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Lecture 1 | What makes this course unique | 00:04:57 Duration |
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Lecture 2 | AWS Machine Learning Exam Overview | 00:09:06 Duration |
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Lecture 3 | Course Outline | 00:10:13 Duration |
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Lecture 4 | BONUS Learning Path | |
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Lecture 5 | Guidelines and Best Practices | 00:01:56 Duration |
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Lecture 6 | Section Introduction | |
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Lecture 7 | What is Machine Learning and AI - Part 1 | 00:09:49 Duration |
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Lecture 8 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 9 | Amazon Web Services | 00:06:24 Duration |
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Lecture 10 | AIML Data Lingo - Labeled vs | 00:14:04 Duration |
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Lecture 11 | AIML Data Lingo - Data Types | 00:10:04 Duration |
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Lecture 12 | Database vs | |
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Lecture 13 | AWS Storage S3 DynamoDB RDS | 00:11:02 Duration |
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Lecture 14 | GET YOUR BONUS MATERIALS | |
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Lecture 15 | Section 1 Slides |
Section 2 : AMAZON S3
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Lecture 1 | Section Introduction | 00:02:40 Duration |
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Lecture 2 | Amazon S3 Partitions and Tags | 00:13:44 Duration |
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Lecture 3 | S3 Storage Tiers and LifeCycle Polices | 00:07:48 Duration |
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Lecture 4 | S3 Encryption | 00:05:05 Duration |
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Lecture 5 | S3 Security - Part 1 | 00:09:01 Duration |
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Lecture 6 | S3 Security - Part 2 | 00:05:12 Duration |
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Lecture 7 | Additional Information | 00:03:23 Duration |
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Lecture 8 | GET YOUR BONUS MATERIALS | |
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Lecture 9 | Section 2 Slides |
Section 3 : AWS DATA MIGRATION, GLUE, PIPELINE, STEP and BATCH
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Lecture 1 | Section Introduction | 00:03:18 Duration |
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Lecture 2 | AWS Glue – part #1 | |
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Lecture 3 | AWS Glue – part #2 | 00:08:40 Duration |
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Lecture 4 | AWS Data Pipeline | 00:07:34 Duration |
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Lecture 5 | AWS Data Migration Service DMS | 00:03:33 Duration |
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Lecture 6 | AWS Batch | 00:02:57 Duration |
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Lecture 7 | AWS Step Function | 00:08:31 Duration |
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Lecture 8 | GET YOUR BONUS MATERIALS | |
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Lecture 9 | Section 3 Slides |
Section 4 : DATA STREAMING AND KINESIS
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Lecture 1 | Section Introduction | 00:05:06 Duration |
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Lecture 2 | Kinesis Overview | 00:07:57 Duration |
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Lecture 3 | AWS Kinesis Video Streams - Part 1 | 00:05:43 Duration |
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Lecture 4 | AWS Kinesis Video Streams - Part 2 | 00:07:43 Duration |
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Lecture 5 | AWS Kinesis Data Streams - Part 1 | 00:05:21 Duration |
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Lecture 6 | AWS Kinesis Data Streams - Part 2 | 00:06:57 Duration |
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Lecture 7 | AWS Kinesis Firehose | 00:06:05 Duration |
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Lecture 8 | AWS Kinesis Analytics - Part 1 | 00:03:20 Duration |
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Lecture 9 | AWS Kinesis Analytics - Part 2 | 00:07:54 Duration |
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Lecture 10 | GET YOUR BONUS MATERIALS | |
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Lecture 11 | Section 4 Slides |
Section 5 : JUPYTER NOTEBOOK, SCIKIT-LEARN, PYTHON PACKAGES, AND DISTRIBUTIONS
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Lecture 1 | Section Introduction | 00:06:42 Duration |
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Lecture 2 | Jupyter Notebooks and Scikit Learn | 00:06:33 Duration |
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Lecture 3 | Python Packages (Pandas, Numpy, Matplotlib and Seaborn) | |
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Lecture 4 | Data Visualization | 00:07:09 Duration |
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Lecture 5 | Distributions (Normal, Standard, Poisson, Bernoulli) | 00:10:12 Duration |
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Lecture 6 | Time Series | 00:02:35 Duration |
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Lecture 7 | GET YOUR BONUS MATERIALS | |
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Lecture 8 | Section 5 Slides |
Section 6 : ATHENA, QUICKSIGHT, EMR
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Lecture 1 | Section Introduction | 00:03:13 Duration |
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Lecture 2 | Athena - Part 1 | 00:08:55 Duration |
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Lecture 3 | Athena - Part 2 | 00:07:55 Duration |
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Lecture 4 | Amazon Quicksight - Part 1 | 00:04:56 Duration |
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Lecture 5 | Amazon Quicksight - Part 2 | 00:11:27 Duration |
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Lecture 6 | Elastic Map Reduce - Part 1 | 00:10:03 Duration |
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Lecture 7 | Elastic Map Reduce - Part 2 | 00:11:21 Duration |
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Lecture 8 | EMR and Hadoop | 00:07:20 Duration |
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Lecture 9 | EMR and Spark | 00:05:34 Duration |
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Lecture 10 | GET YOUR BONUS MATERIALS | |
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Lecture 11 | Section 6 Slides |
Section 7 : FEATURE ENGINEERING
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Lecture 1 | Introduction to Feature Engineering | 00:02:31 Duration |
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Lecture 2 | Feature Engineering Overview | 00:08:37 Duration |
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Lecture 3 | Amazon SageMaker GroundTruth | 00:09:07 Duration |
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Lecture 4 | Feature Selection | 00:05:25 Duration |
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Lecture 5 | Scaling | 00:09:28 Duration |
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Lecture 6 | Imputation | 00:10:21 Duration |
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Lecture 7 | Outliers | 00:05:08 Duration |
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Lecture 8 | One Hot Encoding | 00:03:42 Duration |
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Lecture 9 | Binning | 00:05:27 Duration |
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Lecture 10 | Log Transformation | 00:03:32 Duration |
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Lecture 11 | Shuffling, Feature Splitting, Unbalanced Datasets | 00:06:25 Duration |
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Lecture 12 | Text Feature Engineering overview | 00:03:40 Duration |
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Lecture 13 | Bag of words, punctuation, and dates (easy ones!) | 00:04:47 Duration |
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Lecture 14 | Term Frequency Inverse Document Frequency (TF-IDF) | 00:06:44 Duration |
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Lecture 15 | N-Grams (Unigram vs | 00:05:37 Duration |
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Lecture 16 | Orthogonal Sparse Bigram (OSB) | 00:03:14 Duration |
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Lecture 17 | Cartesian Product Transformation | 00:03:36 Duration |
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Lecture 18 | GET YOUR BONUS MATERIALS | |
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Lecture 19 | Section 7 Slides |
Section 8 : MACHINE AND DEEP LEARNING BASICS - PART #1
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Lecture 1 | Section Introduction | 00:08:53 Duration |
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Lecture 2 | Artificial Neural Networks Basics Single Neuron Model | 00:07:02 Duration |
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Lecture 3 | Activation Functions | 00:05:01 Duration |
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Lecture 4 | Multi-Layer Perceptron Model | 00:07:31 Duration |
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Lecture 5 | How do Artificial Neural Networks Train | 00:16:24 Duration |
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Lecture 6 | ANN Parameters Tuning – Learning rate and batch size | 00:10:49 Duration |
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Lecture 7 | Tensorflow playground | 00:14:40 Duration |
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Lecture 8 | Gradient Descent and Backpropagation | 00:09:03 Duration |
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Lecture 9 | Overfitting and Under fitting | 00:06:09 Duration |
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Lecture 10 | How to overcome overfitting | 00:09:28 Duration |
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Lecture 11 | Bias Variance Trade-off | 00:09:51 Duration |
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Lecture 12 | L2 Regularization | 00:07:48 Duration |
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Lecture 13 | L1 Regularization | 00:04:33 Duration |
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Lecture 14 | GET YOUR BONUS MATERIALS | |
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Lecture 15 | Section 8 Slides |
Section 9 : MACHINE AND DEEP LEARNING BASICS - PART #2
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Lecture 1 | Section Introduction | 00:02:39 Duration |
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Lecture 2 | Artificial Neural Networks Architectures | |
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Lecture 3 | Convolutional Neural Networks | 00:18:54 Duration |
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Lecture 4 | Recurrent Neural Networks | 00:09:51 Duration |
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Lecture 5 | Vanishing Gradient Problem | 00:07:16 Duration |
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Lecture 6 | Long Short Term Memory (LSTM) Networks | 00:07:10 Duration |
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Lecture 7 | Model Performance Assessment – Confusion Matrix | 00:07:55 Duration |
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Lecture 8 | Model Performance Assessment – Precision, recall, F1-score | 00:16:43 Duration |
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Lecture 9 | Model Performance Assessment – ROC, AUC, Heatmap, and RMSE | 00:09:35 Duration |
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Lecture 10 | Transfer Learning | 00:11:36 Duration |
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Lecture 11 | Ensemble Learning - Bagging and Boosting | 00:09:57 Duration |
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Lecture 12 | K Fold Cross Validation | 00:02:15 Duration |
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Lecture 13 | GET YOUR BONUS MATERIALS | |
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Lecture 14 | Section 9 Slides |
Section 10 : MACHINE AND DEEP LEARNING IN AWS - PART #1
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Lecture 1 | Section Introduction | 00:03:46 Duration |
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Lecture 2 | AWS SageMaker | 00:11:07 Duration |
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Lecture 3 | AWS SageMaker Part 2 | 00:12:11 Duration |
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Lecture 4 | Deep Learning on AWS | 00:02:44 Duration |
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Lecture 5 | SageMaker Built-in algorithms overview | 00:06:36 Duration |
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Lecture 6 | Object Detection | 00:09:09 Duration |
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Lecture 7 | Image classification | 00:06:28 Duration |
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Lecture 8 | Semantic Segmentation | 00:08:07 Duration |
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Lecture 9 | Linear Learner | 00:06:58 Duration |
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Lecture 10 | Factorization Machines | 00:04:32 Duration |
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Lecture 11 | XGboost | 00:03:34 Duration |
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Lecture 12 | Seq2Seq | 00:05:55 Duration |
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Lecture 13 | DeepAR | 00:08:15 Duration |
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Lecture 14 | Blazing Text | 00:09:52 Duration |
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Lecture 15 | GET YOUR BONUS MATERIALS | |
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Lecture 16 | Section 10 Slides |
Section 11 : MACHINE AND DEEP LEARNING IN AWS - PART #2
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Lecture 1 | Section Introduction | 00:03:11 Duration |
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Lecture 2 | SageMaker Built-in Algorithms Overview | 00:04:34 Duration |
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Lecture 3 | Random Cut Forest | 00:07:45 Duration |
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Lecture 4 | K Nearest Neighbors KNN | 00:09:09 Duration |
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Lecture 5 | K Means | 00:04:35 Duration |
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Lecture 6 | Principal Component Analysis PCA | 00:03:51 Duration |
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Lecture 7 | IP Insights | 00:05:43 Duration |
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Lecture 8 | Reinforcement Learning | 00:09:26 Duration |
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Lecture 9 | Neural Topic Model NTM | 00:03:24 Duration |
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Lecture 10 | LDA | 00:03:37 Duration |
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Lecture 11 | Object2Vec | 00:06:23 Duration |
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Lecture 12 | Multi Model | 00:01:55 Duration |
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Lecture 13 | Automatic Model Tuning | 00:08:40 Duration |
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Lecture 14 | GET YOUR BONUS MATERIALS | |
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Lecture 15 | Section 11 Slides |
Section 12 : AWS HIGH LEVEL AIML SERVICES
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Lecture 1 | Section Introduction | 00:03:47 Duration |
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Lecture 2 | SageMaker AIML High Level Services | 00:02:42 Duration |
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Lecture 3 | Top 5 AIML Services | 00:15:05 Duration |
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Lecture 4 | ReKognition | 00:05:54 Duration |
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Lecture 5 | Amazon Comprehend and Comprehend Medical | 00:05:43 Duration |
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Lecture 6 | Translate | 00:05:46 Duration |
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Lecture 7 | Transcribe | 00:06:25 Duration |
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Lecture 8 | Polly | 00:02:04 Duration |
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Lecture 9 | Forecast | 00:05:45 Duration |
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Lecture 10 | Lex | 00:05:00 Duration |
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Lecture 11 | Personalize | 00:03:41 Duration |
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Lecture 12 | Textract | 00:02:31 Duration |
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Lecture 13 | AWS DeepLens | 00:04:12 Duration |
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Lecture 14 | AWS DeepRacer | 00:02:38 Duration |
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Lecture 15 | GET YOUR BONUS MATERIALS | |
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Lecture 16 | Section 12 Slides |
Section 13 : ML IMPLEMENTATION AND OPERATION
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Lecture 1 | Introduction | 00:06:57 Duration |
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Lecture 2 | SageMaker Components Review | 00:08:15 Duration |
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Lecture 3 | SageMaker Model Deployment | 00:05:16 Duration |
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Lecture 4 | Resources and Instance Types | 00:13:04 Duration |
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Lecture 5 | Online vs | 00:09:09 Duration |
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Lecture 6 | Production Variants and Canary Deployment | 00:04:58 Duration |
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Lecture 7 | SageMaker Neo | 00:05:09 Duration |
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Lecture 8 | AWS IoT Greengrass | 00:03:40 Duration |
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Lecture 9 | Docker Containers | 00:07:28 Duration |
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Lecture 10 | AWS Security Overview | 00:05:53 Duration |
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Lecture 11 | In-Transit and Rest Encryption | 00:03:41 Duration |
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Lecture 12 | AWS CloudWatch | 00:04:15 Duration |
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Lecture 13 | AWS CloudTrail | 00:02:55 Duration |
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Lecture 14 | Section 13 Slides |