About this course
The goal of the Machine Learning Engineer Nano degree program is to help you learn the key skills they need to perform well as a machine learning engineer.
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. It is one of the most exciting technologies that one would have ever come across.
“Machine Learning: A computer is able to learn from experience without being specifically programmed.”
After implicit of this program will be able to
- Test Python code and build a Python package of its own.
- Build predictive models using a variety of unsupervised and supervised machine learning techniques.
- Use Amazon SageMaker to deploy machine learning models to production environments, such as a web application or piece of hardware.
- A/B test two different deployed models and evaluate their performance.
- Utilize an API to deploy a model to a website such that it responds to user input, dynamically.
- Update a deployed model, in response to changes in the underlying data source.
Also, this program is comprised of 4 courses and 4 projects.
What you will learn from the Machine Learning Engineer course?
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Also, you will be able to gain practical experience using Amazon Sage Maker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry.
Why should you enroll in this course?
As more and more companies are looking to build machine learning products, there is a growing demand for engineers who are able to deploy machine learning models to global audiences. In this program, you will learn how to create an end-to-end machine learning product. You will deploy machine learning models to a production environment, such as a web application, and evaluate and update that model according to performance metrics. This program is designed to give you the advanced skills you need to become a machine learning engineer.
Syllabus of the Machine Learning Engineer Nano Degree
The machine learning engineer program consist of 4 courses:
Course 1: Machine Learning Engineer: Software Engineering Fundamentals
LESSON ONE: Software Engineering Practices
- Write clean, modular, and well-documented code.
- Refactor code for efficiency.
- Create unit tests to test programs.
- Tracking actions and results of processes with logging.
LESSON TWO: Programming
- Understand when to use object-oriented programming.
LESSON THREE: Upload a Package to PyPI
- Portfolio Exercise: Build your own Python package.
Course 2: Machine Learning Engineer: Machine Learning in Production
LESSON ONE: Introduction to Deployment
- Gain familiarity with cloud and deployment terminology.
- Understand the machine learning workflow in production.
- Learn about workplace use cases of machine learning.
LESSON TWO: Deploy a Model
- Deploy a model within SageMaker and Predict housing prices in Boston using XGBoost on SageMaker.
- Determine movie review sentiment using XGBoost on SageMaker.
LESSON THREE: Web Hosting
- Learn to provide access to an endpoint from a website.
- Use API Gateway and Lambda to integrate ML models into a web app.
LESSON FOUR: Model Monitoring
- Learn how to monitor the behavior of your models over time.
- Tune hyperparameters of an XG Boost model using Sage Maker’s automatic hyperparameter tuning tools.
- Run an A/B test on Sage Maker to compare the tuned model to the untuned model.
LESSON FIVE: Updating a Model
- Update your model to account for changes in the data that were discovered during model monitoring.
- Explore how to handle new phrases introduced to your model during your sentiment analysis.
Course 3: Machine Learning Engineer: Machine Learning Case Studies
LESSON ONE: Population Segmentation with SageMaker
- Learn the breadth of algorithms available using AWS Sage Maker.
- Understand how you can use unsupervised algorithms to analyze data with Sage Maker.
- Deploy an unsupervised model using Sage Maker.
- Draw insights about your data by extracting model attributes.
LESSON TWO: Detecting Credit Card Fraud
- Build and improve a linear model to identify cases of payment fraud.
- Handle cases of class imbalance in the training data.
- Tune a model in Sage Maker to improve its performance according to a specific metric.
LESSON THREE: Deploying Custom Models
- Deploy a custom PyTorch model using SageMaker.
- Write a custom training script to train a model of your own design.
LESSON FOUR: Time-Series Forecasting
- Process time-series data and format it for training a machine learning model.
- Use SageMaker’s DeepAR algorithm for time-series forecasting.
- Deploy a model and use it to predict future data points.
Course 4: Machine Learning Engineer Capstone Project
LESSON ONE: Elective 1: Starbucks
- Use purchasing habits to arrive at discount measures to obtain and retain customers.
- Identify groups of individuals that are most likely to be responsive to rebates.
LESSON TWO: Elective 2: Arvato Financial Services
- Work through a real-world dataset and challenge provided by Arvato Financial Services, a Bertelsmann company.
- Top performers have a chance at an interview with Arvato or another Bertelsmann company!
LESSON THREE: Elective 3: Convolutional Neural Network
- Complete a project to identify dog breeds based on images.
LESSON FOUR: Elective 4: Your Choice
- Build a new project entirely of your own choosing.
This Nano-degree Program Includes:
- Experienced Project reviews.
- Technical mentor support.
- Personal career services.
How is the Nano-degree program structured?
The Machine Learning Engineer Nanodegree program is comprised of content and curriculum to support four (4) projects. They estimate that students can complete the program in three (3) months, working 10 hours per week.
About Project description
Throughout this machine learning engineer program, you will have the opportunity to prove your skills by building the following projects:
- Build a Python Package: Write a Python package on your own using software engineering best practices for writing production-level code. This project is optional and will not be graded.
- Deploy a Sentiment Analysis Model: Using Sage Maker, deploy your own PyTorch sentiment analysis model, which is trained to recognize the sentiment of movie reviews (positive or negative).
- Plagiarism Detector: Engineer features that can help identify cases of plagiarism in text and deploy a trained plagiarism detection model using Amazon Sage Maker.
- Capstone Project & Proposal: Complete a final project—choosing from a few, provided options or a project of your own design—that involves data exploration and machine learning. In the sections below, you’ll find detailed descriptions of each project along with the course material that presents the skills required to complete the project.
Also, you will find detailed descriptions of each project along with the course material that presents the skills required to complete the project.
Intermediate 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.
Intermediate Python programming knowledge, including:
- Supervised learning models, such as linear regression and Unsupervised models, such as k-means clustering.
- Deep learning models, such as neural networks (ideally in PyTorch).
If you do not meet the requirements to enroll, What should you do?
To succeed in this program, you are expected to know foundational machine learning algorithms. If you’d like to learn more about common unsupervised and supervised techniques, it is suggested that you take the Intro to Machine Learning Nano-degree program.
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- Amazon Web Services Kaggle
- 1-3 Months
- Paid Course (Paid certificate)
- Data Engineering Data Science with 'Python' Deep learning Machine learning Natural language processing