--- a
+++ b/README.md
@@ -0,0 +1,782 @@
+# AI for Healthcare
+
+#### NANODEGREE PROGRAM SYLLABUS
+
+
+## Overview
+
+```
+Play a critical role in enhancing clinical decision-making with machine learning to build the treatments of
+the future. Learn to build, evaluate, and integrate predictive models that have the power to transform
+patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis
+and then move on to modeling patient outcomes with electronic health records to optimize clinical trial
+testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the
+wearer’s pulse rate in the presence of motion.
+```
+```
+A graduate of this program will be able to:
+```
+- Recommend appropriate imaging modalities for common clinical applications of 2D medical imaging
+- Perform exploratory data analysis (EDA) on 2D medical imaging data to inform model training and
+explain model performance
+- Establish the appropriate ‘ground truth’ methodologies for training algorithms to label medical images
+- Extract images from a DICOM dataset
+- Train common CNN architectures to classify 2D medical images
+- Translate outputs of medical imaging models for use by a clinician
+- Plan necessary validations to prepare a medical imaging model for regulatory approval
+- Detect major clinical abnormalities in a DICOM dataset
+- Train machine learning models for classification tasks using real-world 3D medical imaging data
+- Integrate models into a clinician’s workflow and troubleshoot deployments
+- Build machine learning models in a manner that is compliant with U.S. healthcare data security and
+privacy standards
+- Use the TensorFlow Dataset API to scalably extract, transform, and load datasets that are aggregated
+at the line, encounter, and longitudinal (patient) data levels
+- Analyze EHR datasets to check for common issues (data leakage, statistical properties, missing values,
+high cardinality) by performing exploratory data analysis with TensorFlow Data Analysis and Validation
+library
+- Create categorical features from Key Industry Code Sets (ICD, CPT, NDC) and reduce dimensionality for
+high cardinality features
+- Use TensorFlow feature columns on both continuous and categorical input features to create derived
+features (bucketing, cross-features, embeddings)
+- Use Shapley values to select features for a model and identify the marginal contribution for each
+selected feature
+- Analyze and determine biases for a model for key demographic groups
+- Use the TensorFlow Probability library to train a model that provides uncertainty range predictions in
+order to allow for risk adjustment/prioritization and triaging of predictions
+- Preprocess data (eliminate “noise”) collected by IMU, PPG, and ECG sensors based on mechanical,
+physiology and environmental effects on the signal.
+- Create an activity classification algorithm using signal processing and machine learning techniques
+- Detect QRS complexes using one-dimensional time series processing techniques
+- Evaluate algorithm performance without ground truth labels
+- Generate a pulse rate algorithm that combines information from the PPG and IMU sensor streams
+
+
+```
+Prerequisites :
+Intermediate
+Python, and
+Experience with
+Machine Learning
+```
+**Flexible Learning** :
+Self-paced, so
+you can learn on
+the schedule that
+works best for you.
+
+**Estimated Time** :
+4 Months at
+15 hours / week
+
+```
+Need Help?
+udacity.com/advisor
+Discuss this program
+with an enrollment
+advisor.
+```
+
+## Course 1: Applying AI to 2D Medical Imaging
+
+## Data
+
+2D imaging, such as X-ray, is widely used when making critical decisions about patient care and accessible by
+most healthcare centers around the world. With the advent of deep learning for non-medical imaging data
+over the past half decade, the world has quickly turned its attention to how AI could be specifically applied to
+medical imaging to improve clinical decision-making and to optimize workflows. Learn the fundamental skills
+needed to work with 2D medical imaging data and how to use AI to derive clinically-relevant insights from
+data gathered via different types of 2D medical imaging such as x-ray, mammography, and digital pathology.
+Extract 2D images from DICOM files and apply the appropriate tools to perform exploratory data analysis
+on them. Build different AI models for different clinical scenarios that involve 2D images and learn how to
+position AI tools for regulatory approval.
+
+##### Course Project
+
+##### Pneumonia Detection
+
+##### from Chest X-Rays
+
+```
+Chest X-ray exams are one of the most frequent and cost-effective
+types of medical imaging examinations. Deriving clinical diagnoses
+from chest X-rays can be challenging, however, even by skilled
+radiologists. When it comes to pneumonia, chest X-rays are the best
+available method for point-of-care diagnosis. More than 1 million
+adults are hospitalized with pneumonia and around 50,000 die
+from the disease every year in the US alone. The high prevalence
+of pneumonia makes it a good candidate for the development of a
+deep learning application for two reasons: 1) Data availability in a
+high enough quantity for training deep learning models for image
+classification 2) Opportunity for clinical aid by providing higher
+accuracy image reads of a difficult-to-diagnose disease and/or reduce
+clinical burnout by performing automated reads of very common
+scans. In this project, you will analyze data from the NIH Chest
+X-ray dataset and train a CNN to classify a given chest X-ray for the
+presence or absence of pneumonia. First, you’ll curate training and
+testing sets that are appropriate for the clinical question at hand from
+a large collection of medical images. Then, you will create a pipeline
+to extract images from DICOM files that can be fed into the CNN for
+model training. Lastly, you’ll write an FDA 501(k) validation plan that
+formally describes your model, the data that it was trained on, and a
+validation plan that meets FDA criteria in order to obtain clearance of
+the software being used as a medical device.
+```
+
+###### LEARNING OUTCOMES
+
+###### LESSON ONE
+
+```
+Introduction to
+AI for 2D Medical
+Imaging
+```
+- Explain what AI for 2D medical imaging is and why it is relevant.
+
+###### LESSON TWO
+
+```
+Clinical
+Foundations of 2D
+Medical Imaging
+```
+- Learn about different 2D medical imaging modalities and their
+clinical applications
+- Understand how different types of machine learning
+algorithms can be applied to 2D medical imaging
+- Learn how to statistically assess an algorithm’s performance
+- Understand the key stakeholders in the 2D medical imaging
+space.
+
+###### LESSON THREE
+
+```
+2D Medical Imaging
+Exploratory Data
+Analysis
+```
+- Learn what the DICOM standard it is and why it exists
+- Use Python tools to explore images extracted from DICOM files
+- Apply Python tools to explore DICOM header data
+- Prepare a DICOM dataset for machine learning
+- Explore a dataset in preparation for machine learning
+
+###### LESSON FOUR
+
+```
+Classification
+Models of 2D
+Medical Images
+```
+- Understand architectures of different machine learning and
+deep learning models, and the differences between them
+- Split a dataset for training and testing an algorithm
+- Learn how to define a gold standard
+- Apply common image pre-processing and augmentation
+techniques to data
+- Fine-tune an existing CNN architecture for transfer learning
+with 2D medical imaging applications
+- Evaluate a model’s performance and optimize its parameters
+
+###### LESSON FIVE
+
+```
+Translating AI
+Algorithms for
+Clinical Settings
+with the FDA
+```
+- Learn about the FDA’s risk categorization for medical devices
+and how to define an Intended Use statement
+- Identify and describe algorithmic limitations for the FDA
+- Translate algorithm performance statistics into clinically
+meaningful information that can trusted by professionals
+- Learn how to create an FDA validation plan
+
+
+## Course 2: Applying AI to 3D Medical Imaging
+
+## Data
+
+3D medical imaging exams such as CT and MRI serve as critical decision-making tools in the clinician’s
+everyday diagnostic armamentarium. These modalities provide a detailed view of the patient’s anatomy and
+potential diseases, and are a challenging though highly promising data type for AI applications. Learn the
+fundamental skills needed to work with 3D medical imaging datasets and frame insights derived from the
+data in a clinically relevant context. Understand how these images are acquired, stored in clinical archives, and
+subsequently read and analyzed. Discover how clinicians use 3D medical images in practice and where AI holds
+most potential in their work with these images. Design and apply machine learning algorithms to solve the
+challenging problems in 3D medical imaging and how to integrate the algorithms into the clinical workflow.
+
+###### LEARNING OUTCOMES
+
+```
+LESSON ONE Introduction to
+AI for 3D Medical
+Imaging
+```
+- Explain what AI for 3D medical imaging is and why it is
+relevant
+
+##### Course Project
+
+##### Hippocampal Volume
+
+##### Quantification in
+
+##### Alzheimer’s Progression
+
+```
+Hippocampus is one of the major structures of the human brain
+with functions that are primarily connected to learning and
+memory. The volume of the hippocampus may change over time,
+with age, or as a result of disease. In order to measure hippocampal
+volume, a 3D imaging technique with good soft tissue contrast is
+required. MRI provides such imaging characteristics, but manual
+volume measurement still requires careful and time consuming
+delineation of the hippocampal boundary. In this project, you will
+go through the steps that will have you create an algorithm that will
+help clinicians assess hippocampal volume in an automated way
+and integrate this algorithm into a clinician’s working environment.
+First, you’ll prepare a hippocampal image dataset to train the U-net
+based segmentation model, and capture performance on the test
+data. Then, you will connect the machine learning execution code
+into a clinical network, create code that will generate reports based
+on the algorithm output, and inspect results in a medical image
+viewer. Lastly, you’ll write up a validation plan that would help
+collect clinical evidence of the algorithm performance, similar to
+that required by regulatory authorities.
+```
+
+###### LESSON TWO
+
+```
+3D Medical
+Imaging - Clinical
+Fundamentals
+```
+- Identify medical imaging modalities that generate 3D images
+- List clinical specialties who use 3D images to influence clinical
+decision making
+- Describe use cases for 3D medical images
+- Explain the principles of clinical decision making
+- Articulate the basic principles of CT and MR scanner operation
+- Perform some of the common 3D medical image analysis
+tasks such as windowing, MPR and 3D reconstruction
+
+###### LESSON THREE
+
+```
+3D Medical
+Imaging
+Exploratory Data
+Analysis
+```
+- Describe and use DICOM and NIFTI representations of 3D
+medical imaging data
+- Explain specifics of spatial and dimensional encoding of 3D
+medical images
+- Use Python-based software packages to load and inspect 3D
+medical imaging volumes
+- Use Python-based software packages to explore datasets
+of 3D medical images and prepare it for machine learning
+pipelines
+- Visualize 3D medical images using open software packages
+
+###### LESSON FOUR
+
+```
+3D Medical
+Imaging - Deep
+Learning Methods
+```
+- Distinguish between classification and segmentation
+problems as they apply to 3D imaging
+- Apply 2D, 2.5D and 3D convolutions to a medical imaging
+volume
+- Apply U-net algorithm to train an automatic segmentation
+model of a real-world CT dataset using PyTorch
+- Interpret results of training, measure efficiency using Dice and
+Jaccard performance metrics
+
+###### LESSON FIVE
+
+```
+Deploying AI
+Algorithms in the
+Real World
+```
+- Identify the components of a clinical medical imaging network
+and integration points as well as DICOM protocol for medical
+image exchange
+- Define the requirements for integration of AI algorithms
+- Use tools for modeling of clinical environments so that
+it is possible to emulate and troubleshoot real-world AI
+deployments
+- Describe regulatory requirements such as FDA medical device
+framework and HIPAA required for operating AI for clinical
+care
+- Provide input into regulatory process, as a data scientist
+
+
+## Course 3: Applying AI to EHR Data
+
+```
+With the transition to electronic health records (EHR) over the last decade, the amount of EHR data has increased
+exponentially, providing an incredible opportunity to unlock this data with AI to benefit the healthcare system.
+Learn the fundamental skills of working with EHR data in order to build and evaluate compliant, interpretable
+machine learning models that account for bias and uncertainty using cutting-edge libraries and tools including
+TensorFlow Probability, Aequitas, and Shapley. Understand the implications of key data privacy and security
+standards in healthcare. Apply industry code sets (ICD10-CM, CPT, HCPCS, NDC), transform datasets at different
+EHR data levels, and use TensorFlow to engineer features.
+```
+###### LEARNING OUTCOMES
+
+###### LESSON ONE
+
+```
+EHR Data Security
+and Analysis
+```
+- Understand U.S. healthcare data security and privacy best
+practices (e.g. HIPAA, HITECH) and how they affect utilizing
+protected health information (PHI) data and building
+models
+- Analyze EHR datasets to check for common issues
+(data leakage, statistical properties, missing values, high
+cardinality) by performing exploratory data analysis
+
+```
+LESSON TWO EHR Code Sets
+```
+- Understand the usage and structure of key industry code
+sets (ICD, CPT, NDC).
+- Group and categorize data within EHR datasets using code
+sets.
+
+##### Course Project
+
+##### Patient Selection for
+
+##### Diabetes Drug Testing
+
+```
+EHR data is becoming a key source of real-world evidence (RWE)
+for the pharmaceutical industry and regulators to make decisions
+on clinical trials. In this project, you will act as a data scientist
+for an exciting unicorn healthcare startup that has created a
+groundbreaking diabetes drug that is ready for clinical trial
+testing. Your task will be to build a regression model to predict the
+estimated hospitalization time for a patient in order to help select/
+filter patients for your study. First, you will perform exploratory
+data analysis in order to identify the dataset level and perform
+feature selection. Next, you will build necessary categorical and
+numerical feature transformations with TensorFlow. Lastly, you will
+build a model and apply various analysis frameworks, including
+TensorFlow Probability and Aequitas, to evaluate model bias and
+uncertainty.
+```
+
+###### LESSON THREE
+
+```
+EHR Transformations
+& Feature
+Engineering
+```
+- Use the TensorFlow Dataset API to scalably extract,
+transform, and load datasets
+- Build datasets aggregated at the line, encounter, and
+longitudinal(patient) data levels
+- Create derived features (bucketing, cross-features,
+embeddings) utilizing TensorFlow feature columns on both
+continuous and categorical input features
+
+###### LESSON FOUR
+
+```
+Building, Evaluating,
+and Interpreting
+Models
+```
+- Analyze and determine biases for a model for key
+demographic groups by evaluating performance metrics
+across groups by using the Aequitas framework.
+- Train a model that provides an uncertainty range with the
+TensorFlow Probability library
+- Use Shapley values to select features for a model and
+identify the marginal contribution for each selected feature
+
+
+## Course 4: Applying AI to Wearable Device Data
+
+Wearable devices are an emerging source of physical health data. With continuous, unobtrusive monitoring
+they hold the promise to add richness to a patient’s health information in remarkable ways. Understand the
+functional mechanisms of three sensors (IMU, PPG, and ECG) that are common to most wearable devices
+and the foundational signal processing knowledge critical for success in this domain. Attribute physiology
+and environmental context’s effect on the sensor signal. Build algorithms that process the data collected by
+multiple sensor streams from wearable devices to surface insights about the wearer’s health.
+
+###### LEARNING OUTCOMES
+
+###### LESSON ONE
+
+```
+Intro to Digital
+Sampling & Signal
+Processing
+```
+- Describe how to digitally sample analog signals
+- Apply signal processing techniques (eg. filtering,
+resampling, interpolation) to time series signals.
+- Apply frequency domain techniques (eg. FFT, STFT,
+spectrogram) to time series signals
+- Use matplotlib’s plotting functionality to visualize signals
+
+###### LESSON TWO
+
+```
+Introduction to
+Sensors
+```
+- Describe how sensors convert a physical phenomenon into
+an electrical one.
+- Understand the signal and noise characteristics of the IMU
+and PPG signals
+
+##### Course Project
+
+##### Motion Compensated
+
+##### Pulse Rate Estimation
+
+```
+Wearable devices have multiple sensors all collecting information
+about the same person at the same time. Combining these
+data streams allows us to accomplish many tasks that would be
+impossible from a single sensor. In this project, you will build an
+algorithm which combines information from two of the sensors
+that are covered in this course -- the IMU and PPG sensors -- that
+can estimate the wearer’s pulse rate in the presence of motion.
+First, you’ll create and evaluate an activity classification algorithm
+by building signal processing features and a random forest model.
+Then, you will build a pulse rate algorithm that uses the activity
+classifier and frequency domain techniques, and also produces
+an associated confidence metric that estimates the accuracy
+of the pulse rate estimate. Lastly, you will evaluate algorithm
+performance and iterate on design until the desired accuracy is
+achieved.
+```
+
+**LESSON THREE Activity Classification**
+
+- Perform exploratory data analysis to understand class
+imbalance and subject imbalance
+- Gain an intuitive understanding signal characteristics and
+potential feature performance
+- Write code to implement features from literature
+- Recognize the danger overfitting of technique (esp.
+on small datasets), not simply of model parameters or
+hyperparameters
+
+**LESSON FOUR ECG Signal Processing**
+
+- Understand the electrophysiology of the heart at a basic
+level
+- Understand the signal and noise characteristics of the ECG
+- Understand how atrial fibrillation manifests in the ECG
+- Build a QRS complex detection algorithm
+- Build an arrhythmia detection algorithm from a wearable
+ECG signal
+- Understand how models can be cascaded together to
+achieve higher-order functionality
+
+
+## Our Classroom Experience
+
+###### REAL-WORLD PROJECTS
+
+```
+Build your skills through industry-relevant projects. Get
+personalized feedback from our network of 900+ project
+reviewers. Our simple interface makes it easy to submit
+your projects as often as you need and receive unlimited
+feedback on your work.
+```
+###### KNOWLEDGE
+
+```
+Find answers to your questions with Knowledge, our
+proprietary wiki. Search questions asked by other students,
+connect with technical mentors, and discover in real-time
+how to solve the challenges that you encounter.
+```
+###### STUDENT HUB
+
+```
+Leverage the power of community through a simple, yet
+powerful chat interface built within the classroom. Use
+Student Hub to connect with your fellow students in your
+Executive Program.
+```
+###### WORKSPACES
+
+```
+See your code in action. Check the output and quality of
+your code by running them on workspaces that are a part
+of our classroom.
+```
+###### QUIZZES
+
+```
+Check your understanding of concepts learned in the
+program by answering simple and auto-graded quizzes.
+Easily go back to the lessons to brush up on concepts
+anytime you get an answer wrong.
+```
+###### CUSTOM STUDY PLANS
+
+```
+Preschedule your study times and save them to your
+personal calendar to create a custom study plan. Program
+regular reminders to keep track of your progress toward
+your goals and completion of your program.
+```
+###### PROGRESS TRACKER
+
+```
+Stay on track to complete your Nanodegree program with
+useful milestone reminders.
+```
+
+## Learn with the Best
+
+### Nikhil Bikhchandani
+
+```
+DATA SCIENTIST
+AT VERILY LIFE SCIENCES
+Nikhil spent five years working with
+wearable devices at Google and Verily Life
+Sciences. His work with wearables spans
+many domains including cardiovascular
+disease, neurodegenerative diseases, and
+diabetes. Before Alphabet, he earned a
+B.S. and M.S. in Electrical Engineering and
+Computer Science at Carnegie Mellon.
+```
+### Mazen Zawaideh
+
+```
+RADIOLOGIST
+AT UNIVERSITY OF WASHINGTON
+Mazen Zawaideh is a Neuroradiology
+Fellow at the University of Washington,
+where he focuses on advanced diagnostic
+imaging and minimally invasive
+therapeutics. He also served as a Radiology
+Consultant for Microsoft Research for AI
+applications in oncologic imaging.
+```
+### Emily Lindemer
+
+```
+DIRECTOR OF DATA SCIENCE &
+ANALYTICS AT WELLFRAME
+Emily is an expert in AI for both medical
+imaging and digital healthcare. She holds
+a PhD from Harvard-MIT’s Health Sciences
+& Technology division and founded her
+own digital health company in the opioid
+space. She now runs the data science
+division of a digital healthcare company in
+Boston called Wellframe.
+```
+### Ivan Tarapov
+
+```
+SR. PROGRAM MANAGER
+AT MICROSOFT RESEARCH
+At Microsoft Research, Ivan works on robust
+auto-segmentation algorithms for MRI and CT
+images. He has worked with Physio-Control,
+Stryker, Medtronic, and Abbott, where he
+helped develop external and internal cardiac
+defibrillators, insulin pumps, telemedicine,
+and medical imaging systems.
+```
+
+## Learn with the Best
+
+### Michael Dandrea
+
+```
+PRINCIPAL DATA SCIENTIST
+AT GENENTECH
+```
+```
+Michael is on the Pharma Development
+Informatics team at Genentech (part of
+the Roche Group), where he works on
+improving clinical trials and developing
+safer, personalized treatments with
+clinical and EHR data. Previously, he was
+a Lead Data Scientist on the AI team at
+McKesson’s Change Healthcare.
+```
+
+## All Our Nanodegree Programs Include:
+
+###### EXPERIENCED PROJECT REVIEWERS
+
+```
+REVIEWER SERVICES
+```
+- Personalized feedback & line by line code reviews
+- 1600+ Reviewers with a 4.85/5 average rating
+- 3 hour average project review turnaround time
+- Unlimited submissions and feedback loops
+- Practical tips and industry best practices
+- Additional suggested resources to improve
+
+###### TECHNICAL MENTOR SUPPORT
+
+```
+MENTORSHIP SERVICES
+```
+- Questions answered quickly by our team of
+technical mentors
+- 1000+ Mentors with a 4.7/5 average rating
+- Support for all your technical questions
+
+###### PERSONAL CAREER SERVICES
+
+```
+CAREER COACHING
+```
+- Personal assistance in your job search
+- Monthly 1-on-1 calls
+- Personalized feedback and career guidance
+- Interview preparation
+- Resume services
+- Github portfolio review
+- LinkedIn profile optimization
+
+
+## Frequently Asked Questions
+
+PROGRAM OVERVIEW
+
+**WHY SHOULD I ENROLL?**
+Artificial Intelligence has revolutionized many industries in the past decade,
+and healthcare is no exception. In fact, the amount of data in **healthcare has
+grown 20x in the past 7 years** , causing an expected surge in the Healthcare AI
+market from **$2.1 to $36.1 billion by 2025** at an annual growth rate of 50.4%. AI
+in Healthcare is transforming the way patient care is delivered, and is impacting
+all aspects of the medical industry, including early detection, more accurate
+diagnosis, advanced treatment, health monitoring, robotics, training, research and
+much more.
+
+By leveraging the power of AI, providers can deploy more precise, efficient,
+and impactful interventions at exactly the right moment in a patient’s care. In
+light of the worldwide COVID-19 pandemic, there has never been a better time
+to understand the possibilities of artificial intelligence within the healthcare
+industry and learn how you can make an impact to better the world’s healthcare
+infrastructure.
+
+###### WHAT JOBS WILL THIS PROGRAM PREPARE ME FOR?
+
+This program will help you apply your Data Science and Machine Learning
+expertise in roles including Physician Data Scientist; Healthcare Data Scientist;
+Healthcare Data Scientist, Machine Learning; Healthcare Machine Learning
+Engineer, Research Scientist, Machine Learning, and more roles in the healthcare
+and health tech industries that necessitate knowledge of AI and machine learning
+techniques.
+
+###### HOW DO I KNOW IF THIS PROGRAM IS RIGHT FOR ME?
+
+If you are interested in applying your data science and machine learning
+experience in the healthcare industry, then this program is right for you.
+
+Additional job titles and backgrounds that could be helpful include Data Scientist,
+Machine Learning Engineer, AI Specialist, Deep Learning Research Engineer, and AI
+Scientist. This program is also a good fit for Researchers, Scientists, and Engineers
+who want to make an impact in the medical field.
+
+ENROLLMENT AND ADMISSION
+
+###### DO I NEED TO APPLY? WHAT ARE THE ADMISSION CRITERIA?
+
+There is no application. This Nanodegree program accepts everyone, regardless of
+experience and specific background.
+
+
+## FAQs Continued
+
+###### WHAT ARE THE PREREQUISITES FOR ENROLLMENT?
+
+To be best prepared to succeed in this program, students should be able to:
+
+Intermediate Python:
+
+- Read, understand, and write code in Python, including language constructs
+such as functions and classes.
+- Read code using vectorized operations with the NumPy library.
+
+Machine Learning:
+
+- Build a machine learning model for a supervised learning problem and
+understand basic methods to represent categorical and numerical features
+as inputs for this model
+- Perform simple machine learning tasks, such as classification and
+regression, from a set of features
+- Apply basic knowledge of Python data and machine learning frameworks
+(Pandas, NumPy, TensorFlow, PyTorch) to manipulate and clean data for
+consumption by different estimators/algorithms (e.g. CNNs, RNNs, tree-
+based models).
+
+**IF I DO NOT MEET THE REQUIREMENTS TO ENROLL, WHAT SHOULD I DO?**
+To best prepare for this program, we recommend the **AI Programming with
+Python Nanodegree program** and the **Deep Learning Nanodegree program** or
+the **Intro to Machine Learning with PyTorch Nanodegree program** or the **Intro
+to Machine Learning with TensorFlow Nanodegree program**.
+
+TUITION AND TERM OF PROGRAM
+
+**HOW IS THIS NANODEGREE PROGRAM STRUCTURED?**
+The AI for Healthcare Nanodegree program is comprised of content and
+curriculum to support four projects. Once you subscribe to a Nanodegree
+program, you will have access to the content and services for the length of time
+specified by your subscription. We estimate that students can complete the
+program in four months, working 15 hours per week.
+
+Each project will be reviewed by the Udacity reviewer network. Feedback will be
+provided and if you do not pass the project, you will be asked to resubmit the
+project until it passes.
+
+**HOW LONG IS THIS NANODEGREE PROGRAM?**
+Access to this Nanodegree program runs for the length of time specified in
+the payment card on the Nanodegree program overview page. If you do not
+graduate within that time period, you will continue learning with month to
+month payments. See the **Terms of Use** for other policies around the terms of
+access to our Nanodegree programs.
+
+
+## FAQs Continued
+
+###### CAN I SWITCH MY START DATE? CAN I GET A REFUND?
+
+Please see the Udacity Program **Terms of Use** and **FAQs** for policies on
+enrollment in our programs.
+
+SOFTWARE AND HARDWARE
+
+**WHAT SOFTWARE AND VERSIONS WILL I NEED IN THIS PROGRAM?**
+For this Nanodegree program, you will need a desktop or laptop computer
+running recent versions of Windows, Mac OS X, or Linux and an unmetered
+broadband Internet connection. For an ideal learning experience, a computer
+with Mac or Linux OS is recommended.
+
+You will use Python, PyTorch, TensorFlow, and Aequitas in this Nanodegree
+program.
+
+