Machine Learning Engineer Nanodegree v5.0.0

Machine Learning Engineer Nanodegree

Nanodegree key: nd009t

Version: 5.0.0

Locale: en-us

This course concentrates on training the learner to become a machine learning engineer and apply predictive models to massive data sets in fields like education, finance, healthcare, or robotics.

Content

Part 01 : Welcome to the Nanodegree program

Module 01: Welcome to the Nanodegree program
- Lesson 01: Welcome to the Nanodegree program
  Welcome to Nanodegree program!
- Lesson 02: Get Help with Your Account
  What to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support

Part 02 : Software Engineering Fundamentals

Module 01: Software Engineering
- Lesson 01: Introduction to Software Engineering
  Welcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.
- Lesson 02: Software Engineering Practices Pt I
  Learn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.
- Lesson 03: Software Engineering Practices Pt II
  Learn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.
- Lesson 04: Introduction to Object-Oriented Programming
  Learn the basics of object-oriented programming so that you can build your own Python package.
- Lesson 05: Portfolio Exercise: Upload a Package to PyPi
  Create your own Python package and upload your package to PyPi.

Part 03 : Machine Learning in Production

Module 01: Deployment
- Lesson 01: Introduction to Deployment
  This lesson will familiarizing the student with cloud and deployment terminology along with demonstrating how deployment fits within the machine learning workflow.
- Lesson 02: Building a Model using SageMaker
  Learn how to use Amazon’s SageMaker service to predict Boston housing prices using SageMaker’s built-in XGBoost algorithm.
- Lesson 03: Deploying and Using a Model
  In this lesson students will learn how to deploy a model using SageMaker and how to make use of their deployed model with a simple web application.
- Lesson 04: Hyperparameter Tuning
  In this lesson students will see how to use SageMaker’s automatic hyperparameter tuning tools on the Boston housing prices model from lesson 2 and with a sentiment analysis model.
- Lesson 05: Updating a Model
  In this lesson students will learn how to update their model to account for changes in the underlying data used to train their model.
Module 02: Project: Deploying a Sentiment Analysis Model
- Lesson 01: Project: Deploying a Sentiment Analysis Model
  Project Description - Deploy a Sentiment Analysis Model
  
  Project Rubric - Deploy a Sentiment Analysis Model

Part 04 : Machine Learning, Case Studies

Module 01: Machine Learning, Case Studies
- Lesson 01: Population Segmentation
  Train and deploy unsupervised models (PCA and k-means clustering) to group US counties by similarities and differences. Visualize the trained model attributes and interpret the results.
- Lesson 02: Payment Fraud Detection
  Train a linear model to do credit card fraud detection. Improve the model by accounting for class imbalance in the training data and tuning for a specific performance metric.
- Lesson 03: Interview Segment: SageMaker as a Tool & the Future of ML
  If you're interested in how SageMaker has developed to serve businesses and learners, take a look at this short interview segment with Dan Mbanga.
- Lesson 04: Deploying Custom Models
  Design and train a custom PyTorch classifier by writing a training script. This is an especially useful skill for tasks that cannot be easily solved by built-in algorithms.
- Lesson 05: Time-Series Forecasting
  Learn how to format time series data into context (input) and prediction (output) data, and train the built-in algorithm, DeepAR; this uses an RNN to find recurring patterns in time series data.
Module 02: Project: Plagiarism Detector
- Lesson 01: Project
  Project Description - Plagiarism Detector
  
  Project Rubric - Plagiarism Detector
Module 03: Career Support: GitHub Review
- Lesson 01: Get Quick Feedback on Your GitHub Profile
  Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile
Module 04: Career Support: LinkedIn Review
- Lesson 01: Take 30 Min to Improve your LinkedIn
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 05 : Build Your Own Machine Learning Portfolio Project

Module 01: Capstone Project
- Lesson 01: Machine Learning Engineer Capstone Project
  Put your Machine Learning Engineer skills to the test by solving a real-world problem using all that you have learned throughout the program.
- Lesson 02: Writing up a Capstone Proposal
  Project Description - Capstone Proposal
  
  Project Rubric - Capstone Proposal
  - Concept 01: Capstone Proposal
- Lesson 03: Machine Learning Capstone
  Project Description - Capstone Project
  
  Project Rubric - Capstone Project
  - Concept 01: Capstone project

Part 06 : Congratulations!

Module 01: Congratulations!
- Lesson 01: Congratulations!
  You've completed the Machine Learning Engineer Nanodegree program!
  - Concept 01: Congratulations!

**Part 07 (Elective):** Additional Materials: NLP Fundamentals

Module 01: Introduction to NLP
- Lesson 01: Introduction to NLP
  Learn how text is represented in natural language models; transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE.
Module 02: Implementing RNNs
- Lesson 01: Implementation of RNN & LSTM
  Learn how to represent memory in code. Then define and train RNNs in PyTorch and apply them to tasks that involve sequential data.
- Lesson 02: Sentiment Prediction RNN
  Implement a sentiment prediction RNN for predicting whether a movie review is positive or negative!

**Part 08 (Elective):** Additional Materials: Convolutional Neural Networks

Module 01: Convolutional Neural Networks
- Lesson 01: Convolutional Neural Networks
  Convolutional Neural Networks allow for spatial pattern recognition. Alexis and Cezanne go over how they help us dramatically improve performance in image classification.
- Lesson 02: GPU Workspaces Demo
  See a demonstration of GPU workspaces in the Udacity classroom.
- Lesson 03: Cloud Computing
  Take advantage of Amazon's GPUs to train your neural network faster. In this lesson, you'll setup an instance on AWS and train a neural network on a GPU.
- Lesson 04: Transfer Learning
  Learn how to apply a pre-trained network to a new problem with transfer learning.
- Lesson 05: Weight Initialization
  In this lesson, you'll learn how to find good initial weights for a neural network. Having good initial weights can place the neural network closer to the optimal solution.
- Lesson 06: Autoencoders
  Autoencoders are neural networks used for data compression, image de-noising, and dimensionality reduction. Here, you'll build autoencoders using PyTorch.

**Part 09 (Elective):** Additional Materials: Web Deployment with Flask

Module 01: Web Deployment with Flask
- Lesson 01: Web Development
  Develop a data dashboard using Flask, Bootstrap, Plotly and Pandas.
- Lesson 02: Portfolio Exercise: Deploy a Data Dashboard
  Customize the data dashboard from the previous lesson to make it your own. Upload the dashboard to the web.

**Part 10 (Elective):** Additional Materials: Version Control

Here is a link to our free course on Version Control.

Module 01: Version Control
- Lesson 01: What is Version Control?
  Version control is an incredibly important part of a professional programmer's life. In this lesson, you'll learn about the benefits of version control and install the version control tool Git!
- Lesson 02: Create a Git Repo
  Now that you've learned the benefits of Version Control and gotten Git installed, it's time you learn how to create a repository.
- Lesson 03: Review A Repo's History
  Knowing how to review an existing Git repository's history of commits is extremely important. You'll learn how to do just that in this lesson.
- Lesson 04: Add Commits to A Repo
  A repository is nothing without commits. In this lesson, you'll learn how to make commits, write descriptive commit messages, and verify the changes you're about to save to the repository.
- Lesson 05: Tagging, Branching, and Merging
  Being able to work on your project in isolation from other changes will multiply your productivity. You'll learn how to do this isolated development with Git's branches.
- Lesson 06: Undoing Changes
  Help! Disaster has struck! You don't have to worry, though, because your project is tracked in version control! You'll learn how to undo and modify changes that have been saved to the repository.