AWS SageMaker Practical for Beginners | Build 6 Projects

Master AWS SageMaker: Build & Deploy AI/ML Models

Unlock the power of machine learning (ML) and deep learning (DL) with this comprehensive course focused on AWS SageMaker. Dive into practical applications of AI/ML, a rapidly growing field transforming industries from banking and healthcare to transportation and technology. AWS is a leading cloud platform, trusted by Fortune 500 companies, and SageMaker provides a fully managed environment to accelerate your AI/ML journey.

This course equips you with the skills to create, train, test, and deploy AI/ML models quickly and efficiently using AWS SageMaker. Through hands-on projects covering business, healthcare, and tech applications, you'll gain mastery of key concepts.

You will learn to:

1. Data Engineering & Feature Engineering: Master data types, essential Python libraries (pandas, Numpy, scikit-learn, Matplotlib, Seaborn), and techniques for data distribution analysis and feature engineering (imputation, binning, encoding, normalization).
2. AI/ML Model Selection: Understand how to choose the right machine learning model for your specific business challenges.
3. AWS SageMaker Algorithm Selection: Learn to select appropriate AWS SageMaker algorithms to effectively solve real-world problems.
4. Model Building, Training & Deployment: Develop proficiency in building, training, and deploying robust AI/ML models.
5. Model Optimization & Hyperparameter Tuning: Optimize model performance through effective hyperparameter tuning.

Core Topics Covered:

• Data Engineering: Data types, Python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions, feature engineering (imputation, binning, encoding, and normalization).
• AWS Services & Algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio, and AutoML.
• Machine & Deep Learning Basics: Artificial Neural Networks (ANNs) – feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU, hyperbolic tangent), supervised/unsupervised learning, gradient descent, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 & 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.

Hands-on Projects Include:

• Project #1: Predict employee salaries using AWS SageMaker Linear Learner (simple regression).
• Project #2: Predict medical insurance premiums with a multiple linear regression model.
• Project #3: Predict retail store sales using XGboost regression and SageMaker Hyperparameter Tuning.
• Project #4: Perform dimensionality reduction using SageMaker PCA and build a cardiovascular disease classifier with XGBoost.
• Project #5: Develop a traffic sign classifier model using SageMaker and TensorFlow.
• Project #6: Deep dive into SageMaker Studio, AutoML, and model debugging techniques.

Who is this course for?

• Beginner Data Scientists: Advance your career and build a strong portfolio.
• Seasoned Consultants: Transform businesses with AI/ML using SageMaker.
• Tech Enthusiasts: New to data science & AI? Gain practical experience with AWS SageMaker.

Recommended: Basic knowledge of Machine Learning, Python programming, and AWS cloud.

Enroll today and start building your AI/ML future with AWS SageMaker!