artificial intelligence

This Nanodegree trains the learner about foundational topics in the exciting field of deep learning, the technology behind state-of-the-art artificial intelligence.

Deep Learning

Learn Python, NumPy, Pandas, Matplotlib, PyTorch, and Linear Algebra—the foundations for building your own neural network. 

AI Programming with Python

Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.

AI for Trading

Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

Natural Language Processing

Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.

Computer Vision

The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

Deep Reinforcement Learning

The Intro to Machine Learning with Pytorch program covers machine learning concepts and techniques, with a focus on supervised and unsupervised learning. The program includes three courses and covers topics such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. The courses include projects that allow learners to apply these techniques to real-world problems, such as identifying potential donors for a charity and clustering customers based on their spending habits. The program uses Python and PyTorch for implementation and includes lessons on model evaluation and tuning.

Intro to Machine Learning with Pytorch

The Introduction to Machine Learning with TensorFlow program covers supervised and unsupervised learning methods for machine learning. Course 1 introduces regression, perceptron algorithms, decision trees, naive Bayes, support vector machines, and evaluation metrics. Course 2 focuses on neural networks and creating an image classifier. Course 3 covers unsupervised learning methods, including clustering and dimensionality reduction for customer segmentation. Key skills include data preprocessing, model selection, and evaluation using TensorFlow and Python.

Introduction to Machine Learning with TensorFlow

Become an expert in the core concepts of artificial intelligence and learn how to apply them to real-life problems.

Artificial Intelligence

The AI for Healthcare program offers two courses that apply AI to 2D and 3D medical imaging data. The first course covers fundamental skills needed to work with 2D imaging data, such as extracting images from DICOM files, building AI models, and obtaining regulatory approval. The course project involves training a CNN to classify chest X-rays for the presence of pneumonia and writing an FDA validation plan. The second course covers 3D imaging data, including clinical fundamentals, imaging modalities, and common analysis tasks. It also explores how AI can be integrated into the clinical workflow. Both courses are designed to teach students how to derive clinically relevant insights from medical imaging data using AI.

AI for Healthcare

Executive Leadership

Formulate and evaluate proposals grounded in first principles when assessing opportunities to embed machine learning and artificial intelligence into a corporate strategy. 

AI For Business Leaders

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to. 


AWS Machine Learning Engineer Nanodegree

The Machine Learning DevOps Engineer Nanodegree program focuses on the software engineering fundamentals required to successfully streamline the deployment of data and machine-learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.

Machine Learning DevOps Engineer

Using Azure Machine Learning’s MLOps capabilities, students will gain experience in understanding ML models, protecting people and their data, and controlling the end-to-end ML lifecycle at scale.


Machine Learning Engineer with Microsoft Azure

Discover Artificial Intelligence and Machine Learning, learn about how they work, and find out how they are beings used to solve problems in new ways that were never possible in the past.

Discovering Artificial Intelligence and Machine Learning

In this course, you will learn fundamental topics of artificial intelligence and machine learning. You will learn about what they are, different algorithms/methods, and their applications.

AI fluency

Introduction to AI/ML Fluency

The AI Engineer using Microsoft Azure Nanodegree program will equip you with the skills required to become an Azure AI engineer or an AI engineer with expertise in Azure AI and machine learning services. This program will teach you how to implement machine learning models, design and build an end-to-end AI solution with Azure Cognitive Services, and how to deploy, monitor, and manage continuous improvement of an Azure AI solution.

AI Engineer using Microsoft Azure

This learning experience will guide you through using ChatGPT, a large language model developed by OpenAI.  You'll gain in-depth knowledge of the underlying technology that powers ChatGPT and master the art of prompt design while exploring its capabilities, limitations, and training.  Whether you're a digital professional, AI enthusiast, or an aspiring AI practitioner, this course equips you with the skills to craft effective prompts, generate high-quality responses, and interpret the outputs of the model. Through industry-relevant hands-on practice, you'll learn to harness ChatGPT's conversational AI capabilities to generate content, analyze data, and create nuanced AI tools.

ChatGPT Fluency

How to Use ChatGPT Fluency

In this course, we will create a custom Q&A bot powered by OpenAI! Along the way, you'll learn how OpenAI works and how to leverage its powerful language processing capabilities to build a functional Q&A bot that can provide insightful answers to your questions and impress your friends with its knowledge.

We'll start by creating an unsupervised machine-learning workflow to match the user's question to the relevant context in our dataset. We'll use that workflow to send a custom prompt that includes this context to an OpenAI text completion model. The output will be a custom response that is better aligned with our user's need for accurate data about recent events.

Word embeddings

Text generation

Conversational AI

Prompt Engineering

Tokenization

OpenAI API

Building a Custom OpenAI Chatbot

In this course, students will learn how to approach and apply ethical AI. Coursework will enable practitioners to design and build models with enhanced fairness and limited bias to avoid unforeseen consequences and connect ethical AI concepts to critical issues in privacy, governance, transparency, and security. They’ll begin with acquiring ethical AI literacy skills that will enable them to engage in more meaningful discussions across AI disciplines and learn how to apply ethical AI principles to their organization. Students will then learn how to implement technical measures toward bias, fairness, and explainability to help ensure an ethical future for all.

Model bias mitigation

AI Transparency

AI Fairness

Explainable AI

Ethical AI

Model bias analysis

AI Governance

This course covers foundational deep learning theory and practice. We begin with how to think about deep learning and when it is the right tool to use. The course covers the fundamental algorithms of deep learning, deep learning architecture and goals, and interweaves the theory with implementation in PyTorch.

Deep learning tools

Training neural networks

Advanced probability

scikit-learn

Overfitting prevention

Gradient descent

PyTorch

Deep learning

Neural networks

Perceptron

Model performance metrics

Introduction to Deep Learning

Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

Image generation

Cyclegan

Hyperparameter tuning

Deep learning model optimization

Markov games

Deep learning techniques

Model evaluation

Generative adversarial networks

Building Generative Adversarial Networks

Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly. 

Python scripting

Docstrings

Loops

Python operators

Control flow

Python data structures

User input handling

File i/o

Built-in Python functions

Iterators

Python data types

Variable scope

Generators

List comprehension

Python function definition

Code debugging

Lambda expressions

Python exception handling

Introduction to Python for AI Programmers

This course covers multiple RNN architectures and discusses design patterns for those models.  You'll also learn about transformer architectures.

Word2vec

Long-short term memory networks

Bert

Recurrent neural networks

Gpt3

NLP transformers

Backpropagation

RNNs & Transformers

data science

programming and development

business

product management

cybersecurity

cloud computing

autonomous systems

Career resources

Data pipeline dags

Azure pipelines

Data pipeline maintenance

Data pipeline creation

Azure Data Factory

Professional presentations

Decision trees

Logistic regression

Spam detection

Linear regression

Quantitative data visualization

Support vector machines

Chart types

Statistical modeling fundamentals

Categorical data visualization

Convolutional kernels

Naive bayes classifiers

Ides

R syntax

Data structures

R function definition

Control flow in r

R data structures

Product management basics

Intrusion detection systems

Security information and event management

Cybersecurity incident response playbooks

Network traffic capture

Splunk

Cybersecurity incident remediation

Logging

Snort ids

Data fluency

Security architecture fundamentals

Cybersecurity risk management

Patch management

Cybersecurity business context

Email security

Threat intelligence

Cis controls

Data privacy regulations

Identity and access management fundamentals

Cybersecurity fluency

Exploit detection

Vulnerability management

Cybersecurity regulations and standards

Nist cybersecurity framework

Cybersecurity governance

Test-driven development

Typescript typing

Express web framework

Prettier

Typescript exception handling

Jasmine

Typescript async/await

Unit testing

JavaScript package management

Typescript promises

API testing

JavaScript event loop

Typescript syntax

Eslint

NodeJS

Typescript setup

Asynchronous JavaScript

Vulnerability assessment

Owasp

Cryptography

Threat modeling

Cybersecurity resource fluency

Application security best practices

Public key infrastructure

Cybersecurity compliance

Cybersecurity standards fluency

Public key certificates

Data encryption

Authentication

Report writing

Cybersecurity controls

Cybersecurity strategy

Non-repudiation

Cia triad

Security auditing

Cybersecurity grc

Hashing

all artificial intelligence Programs

all artificial intelligence Programs