Machine Learning Engineer Nanodegree  Google
UdacityKostenlos
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 Kurs
 Online
 Wann:
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Become a machine learning engineer and apply predictive models to massive data sets in fields like education, finance, healthcare or robotics.
Beginn  Lage 

Freie Auswahl 
Online

Was lernen Sie in diesem Kurs?
Algorithms  Performance  Finance  Project  
healthcare  Housing 
Themenkreis
Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence.
This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
 Project P0: Titanic Survival Exploration
In this 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. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program.
Project P0: Titanic Survival ExplorationIn this 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. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program.
 Project P1: Predicting Boston Housing Prices
The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for your client’s home.
Supporting CoursesIntro to Descriptive Statistics
Intro to Data Science
Project P1: Predicting Boston Housing PricesThe Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for your client’s home.
 Project P2: Build a Student Intervention System
As education has grown to rely more and more on technology, more and more data is available for examination and prediction. Logs of student activities, grades, interactions with teachers and fellow students, and more are now available in real time. Educators are after new ways to predict success and failure early enough to stage effective interventions, as well as to identify the effectiveness of different interventions. Toward that end, your goal is to model the factors that predict how likely a student is to pass their high school final exam.
Project P2: Build a Student Intervention SystemAs education has grown to rely more and more on technology, more and more data is available for examination and prediction. Logs of student activities, grades, interactions with teachers and fellow students, and more are now available in real time. Educators are after new ways to predict success and failure early enough to stage effective interventions, as well as to identify the effectiveness of different interventions. Toward that end, your goal is to model the factors that predict how likely a student is to pass their high school final exam.
 Project P3: Creating Customer Segments
Most of the data one collects doesn’t necessarily fit into nice, labeled categories. Many times not only is data not labeled, but categories are unknown! In this project we will take unstructured data, and then attempt to understand the patterns and natural categories that the data fits into. First you’ll learn about methods that are useful for dealing with data without labels, then you’ll apply this to a dataset of your choice, learning what natural categories sit inside it.
Project P3: Creating Customer SegmentsMost of the data one collects doesn’t necessarily fit into nice, labeled categories. Many times not only is data not labeled, but categories are unknown! In this project we will take unstructured data, and then attempt to understand the patterns and natural categories that the data fits into. First you’ll learn about methods that are useful for dealing with data without labels, then you’ll apply this to a dataset of your choice, learning what natural categories sit inside it.
 Project P4: Train a Smartcab to Drive
A smartcab is a selfdriving car from the notsodistant future that ferries people from one arbitrary location to another. In this project, you will use reinforcement learning to train a smartcab how to drive.
Supporting CoursesReinforcement Learning
Project P4: Train a Smartcab to DriveA smartcab is a selfdriving car from the notsodistant future that ferries people from one arbitrary location to another. In this project, you will use reinforcement learning to train a smartcab how to drive.
 Project P5: Capstone Project
In this capstone project, you will leverage what you’ve learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values. Finally, you will construct conclusions about your results, and discuss whether your implementation adequately solves the problem.
Supporting CoursesDeep Learning
Artificial Intelligence for Robotics
Machine Learning for Trading
Project P5: Capstone ProjectIn this capstone project, you will leverage what you’ve learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it.
You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values. Finally, you will construct conclusions about your results, and discuss whether your implementation adequately solves the problem.