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001 - Course Introduction.mp4
40.88MB
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002 - About the Training Architect.mp4
21.76MB
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003 - About the Exam.mp4
152.23MB
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004 - Artificial Intelligence.mp4
21.17MB
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005 - What Is Machine Learning?.mp4
40.75MB
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006 - What Is Deep Learning?.mp4
38.57MB
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007 - Section Introduction.mp4
11.25MB
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008 - Machine Learning Lifecycle.mp4
59.8MB
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009 - Supervised, Unsupervised, and Reinforcement.mp4
40.67MB
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010 - Optimization.mp4
31.59MB
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011 - Regularization.mp4
15.99MB
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012 - Hyperparameters.mp4
24.14MB
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013 - Validation.mp4
14.97MB
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014 - Section Introduction.mp4
9.75MB
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015 - Feature Selection and Engineering.mp4
35.56MB
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016 - Principal Component Analysis (PCA).mp4
40.5MB
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017 - Missing and Unbalanced Data.mp4
43.4MB
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018 - Label and One Hot Encoding.mp4
14.79MB
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019 - Splitting and Randomization.mp4
16.63MB
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020 - RecordIO Format.mp4
21.1MB
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021 - Section Introduction.mp4
8.12MB
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022 - Logistical Regression.mp4
32.31MB
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023 - Linear Regression.mp4
24.26MB
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024 - Support Vector Machines.mp4
21.53MB
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025 - Decision Trees.mp4
38.69MB
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026 - Random Forests.mp4
25.95MB
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027 - K-Means.mp4
33.84MB
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028 - K-Nearest Neighbour.mp4
13.83MB
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029 - Latent Dirichlet Allocation (LDA) Algorithm.mp4
38.9MB
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030 - Section Introduction.mp4
7.99MB
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031 - Neural Networks.mp4
71.87MB
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032 - Convolutional Neural Networks (CNN).mp4
45.07MB
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033 - Recurrent Neural Networks (RNN).mp4
41.96MB
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034 - Section Introduction.mp4
17.04MB
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035 - Confusion Matrix.mp4
49MB
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036 - Sensitivity and Specificity.mp4
84.23MB
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037 - Accuracy and Precision.mp4
31.64MB
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038 - ROC_AUC.mp4
69.45MB
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039 - Gini Impurity.mp4
35.11MB
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040 - F1 Score.mp4
25.38MB
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041 - Introduction to Jupyter Notebooks.mp4
66.91MB
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042 - ML and DL Frameworks.mp4
40.28MB
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043 - TensorFlow.mp4
83.18MB
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044 - PyTorch.mp4
39.69MB
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045 - MXNet.mp4
32.39MB
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046 - Scikit-learn.mp4
94.28MB
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047 - S3.mp4
90.6MB
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048 - Glue.mp4
83.87MB
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049 - Athena.mp4
86.56MB
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050 - QuickSight.mp4
47.42MB
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051 - Kinesis, Streams, Firehose, Video, and Analytics.mp4
82.27MB
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052 - EMR with Spark.mp4
32.85MB
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053 - EC2 for ML.mp4
60.82MB
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054 - Amazon ML.mp4
13.03MB
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055 - Section Introduction.mp4
37.5MB
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056 - Amazon Rekognition (Images) Part 1.mp4
90.78MB
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057 - Amazon Rekognition (Images) Part 2 - the API.mp4
161.34MB
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058 - Amazon Rekognition (Video).mp4
48.47MB
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059 - Amazon Polly.mp4
52.04MB
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060 - Amazon Transcribe.mp4
53.32MB
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061 - Amazon Translate.mp4
88.63MB
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062 - Amazon Comprehend.mp4
71.78MB
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063 - Amazon Lex.mp4
77.77MB
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064 - Amazon Service Chaining with AWS Step Functions.mp4
66.14MB
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065 - Section Introduction.mp4
19.05MB
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066 - What is Amazon SageMaker?.mp4
39.73MB
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067 - The Three Stages.mp4
13.36MB
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068 - Control (Console_SDK_Notebooks).mp4
84.27MB
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069 - SageMaker Notebooks.mp4
95.83MB
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070 - Data Preprocessing.mp4
59.61MB
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071 - Ground Truth.mp4
40.26MB
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072 - Preprocessing Image Data (Pinehead NotPinehead).mp4
192.2MB
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073 - Algorithms.mp4
69.62MB
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074 - SageMaker Algorithms - Architecture 1.mp4
52.83MB
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075 - SageMaker Algorithms - Architecture 2.mp4
48.95MB
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076 - SageMaker Algorithms - Architecture 3.mp4
23.65MB
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077 - Training an Image Classifier - Part 1 (Pinehead NotPinehead).mp4
126.75MB
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078 - Training an Image Classifier - Part 2 (Pinehead NotPinehead).mp4
31.65MB
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079 - Hyperparameter Tuning.mp4
57.57MB
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080 - Inference Pipelines.mp4
18.2MB
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081 - Real-Time and Batch Inference.mp4
24.18MB
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082 - Deploy an Image Classifier (Pinehead, NotPinehead).mp4
110.49MB
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083 - Accessing Inference from Apps.mp4
16.97MB
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084 - Create a custom API for inference - Part 1 (Pinehead NotPinehead).mp4
65.2MB
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085 - Create a custom API for inference - Part 2 (Pinehead NotPinehead).mp4
105.93MB
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086 - Securing SageMaker Notebooks.mp4
112.56MB
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087 - SageMaker and the VPC.mp4
23.12MB
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088 - Section Introduction.mp4
10.16MB
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089 - DeepLens - Part 1.mp4
171.78MB
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090 - DeepLens - Part 2.mp4
50.88MB
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091 - DeepRacer - Part 1.mp4
175.45MB
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092 - DeepRacer - Part 2.mp4
38.74MB
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093 - How to Answer Questions.mp4
119.63MB
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094 - How to Prepare.mp4
62.68MB
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095 - Goodbye!.mp4
40.43MB
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