AWS SageMaker Practical for Beginners | Build 6 Projects

AWS SageMaker Practical for Beginners | Build 6 Projects

Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Picture Classification) & Be taught SageMaker Studio & AutoML

What you’ll study

  • Prepare and deploy AI/ML fashions utilizing AWS SageMaker
  • Optimize mannequin parameters utilizing hyperparameters optimization search.
  • Develop, practice, check and deploy linear regression mannequin to make predictions.
  • Deploy manufacturing degree multi-polynomial regression mannequin to foretell retailer gross sales primarily based on the given options.
  • Develop a deploy deep learning-based mannequin to carry out picture classification.
  • Develop time sequence forecasting fashions to foretell future product costs utilizing DeepAR.
  • Develop and deploy sentiment evaluation mannequin utilizing SageMaker.
  • Deploy skilled NLP mannequin and work together/make predictions utilizing safe API.
  • Prepare and consider Object Detection mannequin utilizing SageMaker built-in algorithms.



Machine and deep studying are the most popular matters in tech! Various fields have adopted ML and DL methods, from banking to healthcare, transportation to expertise.

AWS is among the most generally used ML cloud computing platforms worldwide – a number of Fortune 500 firms rely upon AWS for their business operations.

SageMaker is a totally managed service inside AWS that permits information scientists and AI practitioners to coach, check, and deploy AI/ML fashions rapidly and effectively.

On this course, college students will discover ways to create AI/ML fashions utilizing AWS SageMaker.

Tasks will cowl varied matters from enterprise, healthcare, and Tech. On this course, college students will be capable to grasp many matters in a sensible method reminiscent of: (1) Information Engineering and Characteristic Engineering, (2) AI/ML Fashions choice, (3) Acceptable AWS SageMaker Algorithm choice to resolve enterprise drawback, (4) AI/ML fashions constructing, coaching, and deployment, (5) Mannequin optimization and Hyper-parameters tuning.

The course covers many matters reminiscent of information engineering, AWS companies and algorithms, and machine/deep studying fundamentals in a sensible method:

  • Information engineering: Information varieties, key python libraries (pandas, Numpy, scikit Be taught, MatplotLib, and Seaborn), information distributions and have engineering (imputation, binning, encoding, and normalization).
  • AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage companies, gradient boosted timber (XGBoost), picture classification, principal part evaluation (PCA), SageMaker Studio and AutoML.
  • Machine and deep studying fundamentals: Sorts of synthetic neural networks (ANNs) reminiscent of feedforward ANNs, convolutional neural networks (CNNs), activation features (sigmoid, RELU and hyperbolic tangent), machine studying coaching methods (supervised/ unsupervised), gradient descent algorithm, studying price, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, function detectors, pooling, batch normalization, vanishing gradient drawback, confusion matrix, precision, recall, F1-score, root imply squared error (RMSE), ensemble studying, determination timber, and random forest.

We educate SageMaker’s huge vary of ML and DL instruments with practice-led tasks. Delve into:

  • Undertaking #1: Prepare, check and deploy easy regression mannequin to foretell workers’ wage utilizing AWS SageMaker Linear Learner
  • Undertaking #2: Prepare, check and deploy a a number of linear regression machine studying mannequin to foretell medical insurance coverage premium.
  • Undertaking #3: Prepare, check and deploy a mannequin to foretell retail retailer gross sales utilizing XGboost regression and optimize mannequin hyperparameters utilizing SageMaker Hyperparameters tuning device.
  • Undertaking #4: Carry out Dimensionality discount Utilizing SageMaker built-in PCA algorithm and construct a classifier mannequin to foretell heart problems utilizing XGBoost Classification mannequin.
  • Undertaking #5: Develop a visitors signal classifier mannequin utilizing Sagemaker and Tensorflow.
  • Undertaking #6: Deep Dive in AWS SageMaker Studio, AutoML, and mannequin debugging.

The course is focused in direction of newbie builders and information scientists eager to get basic understanding of AWS SageMaker and clear up actual world difficult issues. Primary information of Machine Studying, python programming and AWS cloud is advisable. Right here’s an inventory of who is that this course for:

  • Inexperienced persons Information Science eager to advance their careers and construct their portfolio.
  • Seasoned consultants wanting to rework companies by leveraging AI/ML utilizing SageMaker.
  • Tech lovers who’re passionate and new to Information science & AI and need to achieve sensible expertise utilizing AWS SageMaker.

Enroll in the present day and I sit up for seeing you inside.

Who this course is for:

  • AI practitioners
  • Aspiring information scientists
  • Tech lovers
  • Information science consultants

Course content material

eight sections • 99 lectures • 14h 43m complete size
  • Introduction, Success Ideas & Finest Practices and Key Studying Outcomes
  • Introduction to AI/ML, AWS and Cloud Computing
  • Undertaking #1 – Worker Wage Predictions Utilizing AWS SageMaker Linear Learner
  • Undertaking #2 – Medical Insurance coverage Premium Prediction
  • Undertaking #3 – Retail Gross sales Prediction Utilizing AWS SageMaker XGBoost (Regression)
  • Undertaking #4 – Predict Cardiovascular Illness Utilizing PCA & XGBoost (Classification)
  • Undertaking #5 – Deep Studying for Visitors Signal Classification Utilizing AWS SageMaker
  • Undertaking #6 – SageMaker Studio DeepDive and AutoML
Final up to date 2/2021
English [Auto]
Direct Obtain Out there
(470 scores)
3,048 college students

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