# Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot.
# Update 23/04/2021 - Updated code scripts and addressed Q&A bugs.
Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.
AWS is one of the most widely used ML cloud computing platforms worldwide – several Fortune 500 companies depend on AWS for their business operations.
SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.
In this course, students will learn how to create AI/ML models using AWS SageMaker.
Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning.
The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way:
Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).
AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML.
Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent), machine learning training strategies (supervised/ unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, feature detectors, pooling, batch normalization, vanishing gradient problem, confusion matrix, precision, recall, F1-score, root mean squared error (RMSE), ensemble learning, decision trees, and random forest.
We teach SageMaker’s vast range of ML and DL tools with practice-led projects. Delve into:
Project #1: Train, test and deploy simple regression model to predict employees’ salary using AWS SageMaker Linear Learner
Project #2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium.
Project #3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker Hyperparameters tuning tool.
Project #4: Perform Dimensionality reduction Using SageMaker built-in PCA algorithm and build a classifier model to predict cardiovascular disease using XGBoost Classification model.
Project #5: Develop a traffic sign classifier model using Sagemaker and Tensorflow.
Project #6: Deep Dive in AWS SageMaker Studio, AutoML, and model debugging.
The course is targeted towards beginner developers and data scientists wanting to get fundamental understanding of AWS SageMaker and solve real world challenging problems. Basic knowledge of Machine Learning, python programming and AWS cloud is recommended. Here’s a list of who is this course for:
Beginners Data Science wanting to advance their careers and build their portfolio.
Seasoned consultants wanting to transform businesses by leveraging AI/ML using SageMaker.
Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience using AWS SageMaker.
Enroll today and I look forward to seeing you inside.