Download and Learn AWS Machine Learning Engineer Udacity Nanodegree Course 2023 for free with google drive download link.
Meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights.
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What you will learn in AWS Machine Learning Engineer Nanodegree
AWS Machine Learning Engineer
Estimated 5 months to complete
You’ll master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker.
AWS Machine Learning Engineer Nanodegree Intro:
Basic knowledge of machine learning algorithms and Python programming.
See detailed requirements Below ????
A well prepared student will be familiar with Python programming knowledge, including:
- At least 40 hours of programming experience
- Familiarity with data structures like dictionaries and lists
- Experience with libraries like NumPy and pandas
- Knowledge of functions, variables, loops, and classes
- Exposure to Python through Jupyter Notebooks is recommended
- Experience with constructing and calling HTTP API endpoints is recommended
Basic knowledge of machine learning algorithms, including:
- Basic understanding of the machine learning workflow
- Basic theoretical understanding of ML algorithms such as linear regression, logistic regression, neural network
- Basic understanding of model training and testing processes
- Basic knowledge of commonly used metrics for ML models evaluation such as accuracy, precision, recall, and mean square error (MSE)
Introduction to Machine Learning
In this course, you’ll start learning about machine learning through high level concepts through AWS SageMaker. You’ll begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you’ll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.
Project – Predict Bike Sharing Demand with AutoGluon
In this project, students will apply the knowledge and methods they learned in the Introduction to Machine Learning course to compete in a Kaggle competition. Using the AutoGluon framework, students will first train a baseline model, then improve their model through feature engineering and hyperparameter tuning. Finally, they’ll submit their optimized model for a public Kaggle rank and write a report on their findings to showcase their work.
Developing Your First ML Workflow
In this course you will learn how to create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline.
Project – Build an ML Workflow on SageMaker
In this project, students will develop an end-to-end ML Workflow on SageMaker, Lambda, and Step Functions. Students will showcase their model deployment capabilities with SageMaker Model Endpoints and Lambda, and their workflow monitoring capabilities with SageMaker Model Monitor and Step Functions. At the end of the project, students will be able to demonstrate building a scalable ML workflow on SageMaker.
Deep Learning Topics within Computer Vision and NLP
In this course you will learn how to train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.
Project – Image Classification using AWS SageMaker
In this project, students will be using AWS Sagemaker to finetune a pretrained model that can perform image classification. Students will have to use Sagemaker profiling, debugger, hyperparameter tuning, and other good ML engineering practices to finish this project. To finish this project, students will have to perform tasks and use tools that a typical ML engineer does as a part of their job.
Operationalizing Machine Learning Projects on SageMaker
This course covers advanced topics related to deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs. You will also learn how to deploy projects that can handle high traffic and how to work with especially large datasets.
Project – Operationalizing an AWS ML Project
In this project, students will start with a machine learning project that accomplishes computer vision tasks. Students will deploy the project on AWS and add several important features: cost minimization, security, and redeployment on a separate server. This project will prepare students to successfully deploy professional projects in industrial applications.
CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers
Distribution centers often use robots to move objects as a part of their operations. Objects are carried in bins where each bin can contain multiple objects. In this project, students will have to build a model that can count the number of objects in each bin. A system like this can be used to track inventory and make sure that delivery consignments have the correct number of items. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.
According to Glassdoor, the national average salary for Machine Learning Engineer is US $131,001 per year in United States.
All our programs include:
Real-world projects from industry experts
With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
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