Autoplay
Autocomplete
HTML5
Flash
Player
Speed
Previous Lecture
Complete and continue
AWS Machine Learning Specialty Certification Course (EARLY ACCESS)
INTRODUCTION TO THE COURSE
EARLY ACCESS Course Introduction (3:14)
About Machine Learning (13:57)
About the AWS Certification - AWS MLS-C01 (16:53)
About This Course (7:20)
Resources & Support (4:11)
INTRODUCTION TO MACHINE LEARNING
My First Model (13:25)
Problem Space (7:26)
Machine Learning Life Cycle (6:27)
Types of Machine Learning (11:03)
Build: Machine Learning Environment - Part 1 (9:10)
Build: Machine Learning Environment - Part 2 (11:09)
Build: My First Model - Part 1 (12:40)
Build: My First Model - Part 2 (11:27)
Build: My First Model - Part 3 (10:05)
DATA
Where to Find Data (10:03)
Build: Loading Sample Data with scikit-learn “California Housing” (25:44)
Build: Loading Sample Data with scikit-learn “MNIST hand-written digits” (11:43)
Build: Create Sample Data with scikit-learn “Random regression problem.” (12:03)
Build: Loading Data from S3 into a Notebook (10:21)
Data Exploration: Useless Data (5:04)
Data Exploration: Binary & Continuous (4:18)
Data Exploration: Categorical (6:34)
Data Exploration: Text & Temporal (3:35)
Feature Encoding (9:42)
Build: Feature Encoding in Jupyter (14:37)
Text Encoding (7:44)
Missing Data (8:28)
Build: Imputation with Jupyter (14:44)
Unbalanced Data (7:50)
Feature Engineering (6:59)
ALGORITHMS
Updating the Machine Learning Environment (4:03)
Logistic Regression (10:35)
Build: Logistic Regression with scikit-learn (13:51)
Linear Regression & Stochastic Gradient Descent (12:30)
Build: Linear Regression (13:54)
Build: Linear Regression & SGD (5:39)
Support Vector Machines (10:54)
Build: Support Vector Machines 1 (13:06)
Build: Support Vector Machines 2 (12:55)
Decision Trees Part 1 (12:22)
Decision Trees Part 2 (10:57)
Random Forests (7:19)
Build: Random Forests (19:38)
K-means Clustering (13:51)
Build: K-means Clustering (12:17)
K-Nearest Neighbours (3:17)
Build: K-nearest Neighbors (7:07)
Latent Dirichlet Allocation (LDA) (15:51)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 1 (9:45)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 2 (10:29)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 3 (6:25)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 4 (11:03)
Principal Component Analysis (PCA) (12:11)
Build: Principal Component Analysis (PCA) (9:48)
Introduction to Neural Networks (12:46)
Inside the Neuron (16:53)
Training a Neural Network (19:47)
Build: My First Neural Network - Part 1 (9:29)
Build: My First Neural Network - Part 2 (13:33)
Build: MNIST Handwritten Dataset (12:27)
Matchbox Tic-Tac-Toe (11:22)
CNN (Convolutional Neural Networks) Part 1 (16:28)
CNN (Convolutional Neural Networks) Part 2 (10:44)
Build: CNN Lego Sorting - Part 1 (14:27)
Build: CNN Lego Sorting - Part 2 (18:27)
RNN (Recurrent Neural Networks) (12:08)
Build: RNN (Recurrent Neural Networks) (15:10)
Word2vec (15:30)
Demo: Word2vec with the (tiny) h2o dataset (6:33)
Build: Word2vec - Part 1 (13:36)
Build: Word2vec: Part 2 (12:30)
Seq2seq (6:57)
TRAINING
Prepare Data for Training (18:05)
K-Fold Cross Validation (6:12)
Bias and Variance (6:27)
Regularization, L1 & L2 (10:46)
Hyperparameters! (15:31)
Optimizers (11:43)
Transfer Learning (7:32)
Training Compute Architecture (7:58)
TESTING & PERFORMANCE
Confusion Matrix (16:53)
Accuracy (6:29)
Precision & Recall (14:07)
Specificity & False Positive Rate (2:03)
ROC AUC - Part 1 (12:00)
ROA AUC - Part 2 (12:30)
F1 Score (5:27)
Confusion Matrix Practice Tool (5:19)
HOSTING & INFERENCE
Inference (Coming soon...)
Build: (Coming soon...)
TOOLS & FRAMEWORKS
Frameworks (Coming soon...)
Build: Example datasets from frameworks (Coming soon...)
AWS AI SERVICES (Coming soon...)
Placeholder
AMAZON SAGEMAKER (Coming soon...)
Placeholder
OTHER AWS SERVICES (Coming soon...)
Placeholder
Feature Engineering
Lecture content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock