AWS SageMaker Practical for Beginners | Build 6 Projects
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.
Requirements
- Basic knowledge of programming
- Basic knowledge in AWS
- Basic knowledge in machine learning
Description
# 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.
Who this course is for:
- AI practitioners
- Aspiring data scientists
- Tech enthusiasts
- Data science consultants
Instructors
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business.
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.
Hi there,
We are the Ligency team. You will be hearing from us when new Ligency courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
Ligency Team!
Mitch is a Canadian filmmaker from Harrow Ontario, Canada. In 2016 he graduated from Dakota State University with a B.S, in Computer Graphics specializing in Film and Cinematic Arts.
Currently, Mitch operates as the Chairman of Red Cape Studios, Inc. where he continues his passion for filmmaking. He is also the Host of Red Cape Learning and Produces / Directs content for Red Cape Films.
He has reached over 490,000 + Students on Udemy and Produced more than 3X Best-Selling Courses.
Mitch is currently working Producing Online Educational Courses thru Red Cape Studios Inc.
Winning several awards at Dakota State University such as "1st Place BeadleMania", "Winner College 10th Anniversary Dordt Film Festival" as well as "Outstanding Artist Award College of Arts and Sciences".
Mitch has been Featured on CBC's "Windsors Shorts" Tv Show and was also the Producer/Director for TEDX Windsor, featuring speakers from across the Country.
Hi there,
We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
The Real People at Ligency