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AWS SageMaker Practical for Beginners | Build 6 Projects


AWS SageMaker Practical for Beginners | Build 6 Projects


AWS SageMaker Practical for Beginners | Build 6 Projects Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML.

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What you'll learn

  • Train and deploy AI/ML models using AWS SageMaker
  • Optimize model parameters using hyperparameters optimization search.
  • Develop, train, test and deploy linear regression model to make predictions.
  • Deploy production level multi-polynomial regression model to predict store sales based on the given features.
  • Develop a deploy deep learning-based model to perform image classification.
  • Develop time series forecasting models to predict future product prices using DeepAR.
  • Develop and deploy sentiment analysis model using SageMaker.
  • Deploy trained NLP model and interact/make predictions using secure API.
  • Train and evaluate Object Detection model using SageMaker built-in algorithms.


  • Basic knowledge of programming
  • Basic knowledge in AWS
  • Basic knowledge in machine learning


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.

Online Course CoupoNED
Online Course CoupoNED I am very happy that there are bloggers who can help my business

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