AWS SageMaker Practical for Beginners | Build 6 Projects

Course Name : AWS SageMaker Practical for Beginners | Build 6 Projects
Students : 9,461
Duration : 16 hrs
Avg Rating : 4.5
Original Price : $119.99
Discount Price : $9.99
Instructor / Provider : Ryan Ahmed, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML
Instructed by Ryan Ahmed 16 hours on-demand video, 2 articles & 2 downloadable resources

After completing the course, you will…

• 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.

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.

Who this course is for:

• AI practitioners
• Aspiring data scientists
• Tech enthusiasts
• Data science consultants

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