Machine Learning Engineer Nanodegree v1.0.0

Nanodegree key: nd009t

Version: 1.0.0

Locale: en-us

Become a machine learning engineer and apply predictive models to massive data sets in fields like education, finance, healthcare or robotics.

Content

Part 01 : Machine Learning Foundations

Welcome to the Machine Learning Engineer Nanodegree program! In this first part, you'll meet the instructors and the career services team. You'll also learn about the program structure and get your first lesson on the basics of machine learning. Join the ML community! --> mlnd.slack.com

Module 01: Introduction to the Nanodegree
- Lesson 01: Welcome to Machine Learning
  Welcome to the Machine Learning Nanodegree program!
- Lesson 02: What is Machine Learning?
  Explore some of the many machine learning concepts in just a few bite-sized lectures!
- Lesson 03: Introductory Practice Project
  In this practice project, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age.
Module 02: Careers Orientation
- Lesson 01: Career Services Available to You
  The Careers team at Udacity is here to help you land your dream job - whether it's a new role or growing at your current company. Learn more about how we'll support you in your career growth.

Part 02 : Model Evaluation and Validation

Module 01: Training and Testing Models
- Lesson 01: Training and Testing Models
  Learn to train and test models with NumPy and Pandas.
Module 02: Evaluation Metrics
- Lesson 01: Evaluation Metrics
  Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!
Module 03: Detecting and Fixing Errors
- Lesson 01: Model Selection
  Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.
Module 04: Practice Assessment
- Lesson 01: NumPy and pandas Assessment
  Test your NumPy and pandas skills with a quick assessment.
  - Concept 01: Assessment
- Lesson 02: Model Evaluation and Validation Assessment
  Test your Model Evaluation and Validation concepts with a quick assessment.
  - Concept 01: Model Evaluation and Validation assessment
Module 05: Predicting Housing Prices
- Lesson 01: Predicting Boston Housing Prices
  Put all you've learned into practice by building and optimizing a model to predict housing prices!
  
  Project Description - Predicting Boston Housing Prices
  
  Project Rubric - Predicting Boston Housing Prices

Part 03 : Supervised Learning

Learn how Supervised Learning models such as Decision Trees, SVMs, etc. are trained to model and predict labeled data.

Module 01: Supervised Learning
- Lesson 01: Linear Regression
  Linear regression is a very effective algorithm to predict numerical data.
- Lesson 02: Perceptron Algorithm
  The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.
- Lesson 03: Decision Trees
  Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
- Lesson 04: Naive Bayes
  Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.
- Lesson 05: Support Vector Machines
  Support vector machines are very effective models used for classification.
- Lesson 06: Ensemble Methods
  Bagging and boosting are two common ensemble methods for improving the accuracy of supervised learning approaches.
- Lesson 07: Supervised Learning Assessment
  Test your Supervised Learning concepts with a quick assessment.
  - Concept 01: Supervised Learning Assessment
- Lesson 08: Supervised Learning Project
  You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!
  
  Project Description - Finding Donors for CharityML
  
  Project Rubric - Finding Donors for CharityML

Part 04 : Unsupervised Learning

Learn how to find patterns and structures in unlabeled data, perform feature transformations and improve the predictive performance of your models.

Module 01: Introduction to Unsupervised Learning
Module 02: Clustering
- Lesson 01: Clustering
  Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
- Lesson 02: Clustering Mini-Project
  In this mini-project, you will use K-means to cluster movie ratings and use those clusters to provide movie recommendations.
- Lesson 03: Hierarchical and Density-based Clustering
  We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
- Lesson 04: Gaussian Mixture Models and Cluster Validation
  In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.
Module 03: Feature Scaling
- Lesson 01: Feature Scaling
  Feature scaling is an important pre-processing step when performing unsupervised learning to allow multiple features to be analyzed together.
Module 04: Dimensionality Reduction
- Lesson 01: PCA
  PCA, principal component analysis, is a method for feature selection that turns a set of correlated variables into the underlying set of orthogonal variables.
Module 05: PCA Mini-Project
- Lesson 01: PCA Mini-Project
  In this mini-project, you'll apply principal component analysis to facial recognition.
  - Concept 01: PCA Mini-Project
Module 06: Random Projection and ICA
- Lesson 01: Random Projection and ICA
  In this lesson, we will look at two methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA)
Module 07: Unsupervised Learning Assessment
- Lesson 01: Unsupervised Learning Assessment
  Test your understanding of unsupervised learning with a quick assessment.
  - Concept 01: Assessment
Module 08: Unsupervised Learning Project
- Lesson 01: Creating Customer Segments
  Project Description - Creating Customer Segments
  
  Project Rubric - Creating Customer Segments

Part 05 : Deep Learning

In this section, we will learn about TensorFlow, Neural Networks and Convolutional Neural Networks.

Module 01: Deep Learning
- Lesson 01: Neural Networks
  Luis will give you an overview of logistic regression, gradient descent, and the building blocks of neural networks.
- Lesson 02: 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 03: Deep Neural Networks
  A deeper dive into backpropagation and the training process of neural networks, including techniques to improve the training.
- Lesson 04: Convolutional Neural Networks
  Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
- Lesson 05: Deep Learning for Cancer Detection with Sebastian Thrun
  In this lesson, Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with convolutional neural networks.
- Lesson 06: Deep Learning Assessment
  Test your Deep Learning concepts with a quick assessment.
  - Concept 01: Assessment
- Lesson 07: Deep Learning Project
  CNN project to recognize dog breeds.
  
  Project Description - Dog Breed Classifier
  
  Project Rubric - Dog Breed Classifier
  - Concept 01: Dog Breed Recognition Project
  - Concept 02: Dog Breed Workspace

Part 06 : Reinforcement Learning

Use Reinforcement Learning algorithms like Q-Learning to train artificial agents to take optimal actions in an environment.

Module 01: Reinforcement Learning
- Lesson 01: Introduction to RL
  Reinforcement learning is a type of machine learning where the machine or software agent learns how to maximize its performance at a task.
- Lesson 02: The RL Framework: The Problem
  Learn how to mathematically formulate tasks as Markov Decision Processes.
- Lesson 03: The RL Framework: The Solution
  In reinforcement learning, agents learn to prioritize different decisions based on the rewards and punishments associated with different outcomes.
- Lesson 04: Dynamic Programming
  The dynamic programming setting is a useful first step towards tackling the reinforcement learning problem.
- Lesson 05: Monte Carlo Methods
  Write your own implementation of Monte Carlo control to teach an agent to play Blackjack!
- Lesson 06: Temporal-Difference Methods
  Learn about how to apply temporal-difference methods such as Sarsa, Q-Learning, and Expected Sarsa to solve both episodic and continuous tasks.
- Lesson 07: Solve OpenAI Gym's Taxi-v2 Task
  With reinforcement learning now in your toolbox, you're ready to explore a mini project using OpenAI Gym!
Module 02: Deep Reinforcement Learning
- Lesson 01: RL in Continuous Spaces
  Review the fundamental concepts of reinforcement learning, and learn how to adapt traditional algorithms to work with continuous spaces.
- Lesson 02: Deep Q-Learning
  Extend value-based reinforcement learning methods to complex problems using deep neural networks.
- Lesson 03: Policy-Based Methods
  Policy-based methods try to directly optimize for the optimal policy. Learn how they work, and why they are important, especially for domains with continuous action spaces.
- Lesson 04: Actor-Critic Methods
  Learn how to combine value-based and policy-based methods, bringing together the best of both worlds, to solve challenging reinforcement learning problems.
- Lesson 05: Teach a Quadcopter How to Fly
  Build a quadcopter flying agent that learns to take off, hover and land using reinforcement learning.
  
  Project Description - Teach a Quadcopter How to Fly
  
  Project Rubric - Teach a Quadcopter How to Fly
Module 03: Practice Assessment
- Lesson 01: Reinforcement Learning Assessment
  Test your understanding of reinforcement learning with a quick assessment.
  - Concept 01: Assessment

Part 07 : Machine Learning Capstone

Have an idea of a problem in the real world that can be solved using machine learning? Here you have the opportunity to do just that using a dataset of your choice.

Module 01: Capstone Proposal
- Lesson 01: Writing up a Capstone Proposal
  Before working on a machine learning problem, write up a proposal of your project to get valuable feedback!
  
  Project Description - Capstone Proposal
  
  Project Rubric - Capstone Proposal
Module 02: Capstone Project
- Lesson 01: Machine Learning Capstone Project
  Now you will put your Machine Learning skills to the test by solving a real world problem using the algorithms you have learned in the program so far.
  
  Project Description - Capstone Project
  
  Project Rubric - Capstone Project

**Part 08 (Career):** Career: Job Search Strategies

Opportunity can come when you least expect it, so when your dream job comes along, you want to be ready.

Module 01: Conduct a Job Search
- Lesson 01: Conduct a Job Search
  Learn how to search for jobs effectively through industry research, and targeting your application to a specific role.
Module 02: Refine Your Resume
- Lesson 01: Refine Your Entry-Level Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 0-3 years of work experience in any industry.
  
  Project Description - Resume Review Project (Entry-level)
  
  Project Rubric - Resume Review Project (Entry-level)
- Lesson 02: Refine Your Career Change Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in an unrelated field.
  
  Project Description - Resume Review Project (Career Change)
  
  Project Rubric - Resume Review Project (Career Change)
- Lesson 03: Refine Your Prior Industry Experience Resume
  Receive a personalized review of your resume. This resume review is best suited for applicants who have 3+ years of work experience in a related field.
  
  Project Description - Resume Review Project (Prior Industry Experience)
  
  Project Rubric - Resume Review Project (Prior Industry Experience)
Module 03: Write an Effective Cover Letter
- Lesson 01: Craft Your Cover Letter
  Get a personalized review of your cover letter. A successful cover letter will convey your enthusiasm, specific technical qualifications, and communication skills applicable to the position.
  
  Project Description - Craft Your Cover Letter
  
  Project Rubric - Craft Your Cover Letter

**Part 09 (Career):** Career: Networking

Networking is a very important component to a successful job search. In the following lesson, you will learn how tell your unique story to recruiters in a succinct and professional but relatable way.

Module 01: Develop Your Personal Brand
- Lesson 01: Develop Your Personal Brand
  In this lesson, learn how to tell your unique story in a succinct and professional way. Communicate to employers that you know how to solve problems, overcome challenges, and achieve results.
- Lesson 02: LinkedIn Review
  Optimize your LinkedIn profile to show up in recruiter searches, build your network, and attract employers. Learn to read your LinkedIn profile through the lens of a recruiter or hiring manager.
  
  Project Description - LinkedIn Profile Review Project
  
  Project Rubric - LinkedIn Profile Review Project
  - Concept 01: Using LinkedIn
  - Concept 02: Resources in Your Career Portal
- Lesson 03: Udacity Professional Profile
  Update and personalize your Udacity Professional Profile as you complete your Nanodegree program, and make your Profile visible to Udacity hiring partners when you’re ready to start your job search.
  
  Project Description - Udacity Professional Profile Review
  
  Project Rubric - Udacity Professional Profile Review
Module 02: GitHub Profile Review
- Lesson 01: GitHub Review
  Review how your GitHub profile, projects, and code represent you as a potential job candidate. Learn to assess your GitHub profile through the eyes of a recruiter or hiring manager.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile

**Part 10 (Career):** Career: Machine Learning Interview Practice

Now that you've practiced your skills through your project work, learn how you can present your knowledge in an interview.

Module 01: Interview Practice (Machine Learning)
- Lesson 01: Ace Your Interview
  Learn strategies to prepare yourself for an interview.
- Lesson 02: Practice Behavioral Questions
  Practice answering behavioral questions and evaluate sample responses.
- Lesson 03: Interview Fails
  Some real-life examples of interviews that didn't go as expected - it happens all the time!
- Lesson 04: Land a Job Offer
  You're practiced a lot for the interview by now. Continue practicing, and you'll ace the interview!
  - Concept 01: Land a Job Offer
- Lesson 05: Interview Practice
  Watch a sample interview for a Machine Learning Engineer position and analyze the candidate's responses
  
  Project Description - ML Interview Practice
  
  Project Rubric - ML Interview Practice
Module 02: Data Structures & Algorithms with Python
- Lesson 01: Introduction and Efficiency
  Begin the section on data structures and algorithms, including Python and efficiency practice.
- Lesson 02: List-Based Collections
  Learn the definition of a list in computer science, and see definitions and examples of list-based data structures, arrays, linked lists, stacks, and queues.
- Lesson 03: Searching and Sorting
  Explore how to search and sort with list-based data structures, including binary search and bubble, merge, and quick sort. Learn how to use recursion.
- Lesson 04: Maps and Hashing
  Understand the concepts of sets, maps (dictionaries), and hashing. Examine common problems and approaches to hashing, and practice with examples.
- Lesson 05: Trees
  Learn the concepts and terminology associated with tree data structures. Investigate tree types, such as binary search trees, heaps, and self-balancing trees.
- Lesson 06: Graphs
  Examine the theoretical concept of a graph and understand common graph terms, coded representations, properties, traversals, and paths.
- Lesson 07: Case Studies in Algorithms
  Explore famous computer science problems, specifically the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem.
- Lesson 08: Technical Interview - Python
  Practice with five technical interviewing questions on topics discussed in the data structures and algorithms course and get a personalized review on both your code and solutions.
  
  Project Description - Technical Interview Practice
  
  Project Rubric - Technical Interview Practice

**Part 11 (Elective):** Deep Learning - Tensorflow

Module 01: Machine Learning to Deep Learning
- Lesson 01: Software and Tools
  How to setup TensorFlow and fetch assignment starter code
  - Concept 01: TensorFlow
- Lesson 02: Deep Learning
  Now that you've been exposed to various types of learning (supervised, unsupervised, and reinforcement), it's time to get a deeper understanding of machine learning with deep learning!
Module 02: Intro to TensorFlow
- Lesson 01: Intro to TensorFlow
  In this lesson, we'll cover the basics of TensorFlow and how to get started creating a simple classifier using this library.
Module 03: Intro to Neural Networks
- Lesson 01: Intro to Neural Networks
  In this lesson, you'll dive deeper into the intuition behind Logistic Regression and Neural Networks. You'll also implement gradient descent and backpropagation in python right here in the classroom.
Module 04: Deep Neural Networks
- Lesson 01: Deep Neural Networks
  Vincent walks you through how to go from a simple neural network to a deep neural network. You'll learn about why additional layers can help and how to prevent overfitting.
Module 05: Convolutional Neural Networks
- Lesson 01: Convolutional Neural Networks
  Vincent explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.