Machine Learning Engineer for Microsoft Azure
Concepts Covered: Azure Machine Learning, Azure Machine Learning SDK, Automation with Pipelines, Automated ML, Machine Learning Operations
The School of
Artificial Intelligence
AI is one of the fastest-growing and most transformational technologies of our time, having added over 2.3 million new jobs in the past few years. Spend 10 hours per week to advance your career.
Programs Starting Soon
AI for Healthcare
Concepts Covered: Artificial Intelligence, 2D Medical Imaging, 3D Medical Imaging, Electronic Health Record Data, Deep Learning, Wearable Device Data, Supervised Learning
Intro to Machine Learning with TensorFlow
Concepts Covered: TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
AI Product Manager
Concepts Covered: AI Products, Training ML Models, Annotating Datasets, Prototyping a Product
Intro to Machine Learning with PyTorch
Concepts Covered: Introduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
- Top Rated!
AI Programming with Python
Concepts Covered: Python, NumPy, Pandas, Matplotlib, PyTorch
Artificial Intelligence for Trading
Concepts Covered: Momentum Trading Strategy, Smart Beta, Alpha Factors, Natural Language Processing, Deep Learning, Neural Networks
Computer Vision
Concepts Covered: Convolutional Neural Networks, Recurrent Neural Networks, Simultaneous Localization and Mapping, Object Tracking, Image Classification, Deep Learning
Natural Language Processing
Concepts Covered: Machine Learning, Speech Recognition, Sentiment Analysis, Machine Translation, Part of Speech Tagging
Deep Reinforcement Learning
Concepts Covered: Reinforcement Learning, Neural Networks, PyTorch, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG)
Artificial Intelligence
Concepts Covered: Constraint Propagation, AI Algorithms, Constraint Satisfaction Problems, Backtracking Search, Optimization Algorithms, Search Algorithms, Minimax Search, Bayesian networks, Planning, Pattern Recognition, Time-Series Analysis with ML
- New!
Deep Learning
Concepts Covered: Neural Networks, Perceptron, Autoencoders, Convolutional Neural Networks, PyTorch, Object Detection, Recurrent Neural Networks, Long-short term Memory Networks, Backpropagation, Generative Adversarial Networks, Deep Learning Techniques, Image Generation, Hyperparameter Tuning
AI for Business Leaders
Concepts Covered: Artificial Intelligence, Machine Learning, Business Strategy, Data Labeling, Data Modeling
- New!
Machine Learning DevOps Engineer
Concepts Covered: API development, Automated model scoring, CI/CD, Model testing, Data Version Control, Machine Learning Pipelines
- New!
Digital Freelancer
Concepts Covered: Portfolio Websites, Professional Networking, Resumes, Social Media Presence, Non-Disclosure Agreements, Service Agreements, Freelance Pricing Frameworks, Invoicing, Brand Design, Elevator pitches, Formal Written Communication, Project management tools, Project scoping
- New!
AWS Machine Learning Engineer
Concepts Covered: Feature Engineering, Machine Learning Fluency, Data Loading with SageMaker, Amazon S3, Neural Network Basics, Deep Learning Fluency, Hyperparameter Tuning, Machine Learning Framework Fundamentals, SageMaker JumpStart, Cloud Resource Allocation, Distributed Model Training with SageMaker, AWS Lambda
- New!
AI Engineer using Microsoft Azure
Concepts Covered: Object Detection, Image Classification, Form Recognition, Facial Recognition, Face Detection, Azure Cognitive Services, Microsoft Bot Framework, Azure Conversational AI Agents, Architecture Diagramming, Requirements Gathering, Intent Recognition, Key Phrase Extraction, Azure Form Recognizer, Bounding Boxes
Your path to the right job
- Machine Learning Engineer
- Deep Learning Engineer
- Artificial Intelligence Specialist
- Quantitative Analyst
Machine Learning Engineer
Machine learning is becoming a fundamental skill as software development is entering a new era. This path will enable you to start a career as a Machine Learning Engineer. First learn the fundamentals of programming in Python, linear algebra, and neural networks, and then move on to core Machine Learning concepts.
Recommended Programs
Intro to Machine Learning with TensorFlow
Step 1
Concepts Covered
TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
intermediateIntro to Machine Learning with PyTorch
Step 2
Concepts Covered
Introduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
intermediateAI Programming with Python
Step 3
Concepts Covered
Python, NumPy, Pandas, Matplotlib, PyTorch
beginnerAWS Machine Learning Engineer
Step 4
Concepts Covered
Feature Engineering, Machine Learning Fluency, Data Loading with SageMaker, Amazon S3, Neural Network Basics, Deep Learning Fluency, Hyperparameter Tuning, Machine Learning Framework Fundamentals, SageMaker JumpStart, Cloud Resource Allocation, Distributed Model Training with SageMaker, AWS Lambda
intermediate
Machine Learning Engineer
Machine learning is becoming a fundamental skill as software development is entering a new era. This path will enable you to start a career as a Machine Learning Engineer. First learn the fundamentals of programming in Python, linear algebra, and neural networks, and then move on to core Machine Learning concepts.
Recommended Programs
Intro to Machine Learning with TensorFlow
Step 1
Concepts Covered
TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
intermediateIntro to Machine Learning with PyTorch
Step 2
Concepts Covered
Introduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
intermediateAI Programming with Python
Step 3
Concepts Covered
Python, NumPy, Pandas, Matplotlib, PyTorch
beginnerAWS Machine Learning Engineer
Step 4
Concepts Covered
Feature Engineering, Machine Learning Fluency, Data Loading with SageMaker, Amazon S3, Neural Network Basics, Deep Learning Fluency, Hyperparameter Tuning, Machine Learning Framework Fundamentals, SageMaker JumpStart, Cloud Resource Allocation, Distributed Model Training with SageMaker, AWS Lambda
intermediate
Deep Learning Engineer
Deep learning is driving advances in artificial intelligence that are changing our world. To join this field, start by learning Python fundamentals and neural networks, move on to core machine learning concepts, and then apply deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.
Recommended Programs
AI Programming with Python
Step 1
Concepts Covered
Python, NumPy, Pandas, Matplotlib, PyTorch
beginnerAWS Machine Learning Engineer
Step 2
Concepts Covered
Feature Engineering, Machine Learning Fluency, Data Loading with SageMaker, Amazon S3, Neural Network Basics, Deep Learning Fluency, Hyperparameter Tuning, Machine Learning Framework Fundamentals, SageMaker JumpStart, Cloud Resource Allocation, Distributed Model Training with SageMaker, AWS Lambda
intermediateDeep Learning
Step 3
Concepts Covered
Neural Networks, Perceptron, Autoencoders, Convolutional Neural Networks, PyTorch, Object Detection, Recurrent Neural Networks, Long-short term Memory Networks, Backpropagation, Generative Adversarial Networks, Deep Learning Techniques, Image Generation, Hyperparameter Tuning
intermediate
Artificial Intelligence Specialist
Artificial Intelligence is expected to be a $60 billion industry by 2025. Join the industry by learning specialized skills in the most transformative AI fields; Computer Vision, Natural Language Processing, Deep Reinforcement Learning, or core AI Algorithms. Each of these programs are advanced topics, building on your existing skills in programming, deep learning, and machine learning.
Recommended Programs
Computer Vision
Step 1
Concepts Covered
Convolutional Neural Networks, Recurrent Neural Networks, Simultaneous Localization and Mapping, Object Tracking, Image Classification, Deep Learning
advancedNatural Language Processing
Step 2
Concepts Covered
Machine Learning, Speech Recognition, Sentiment Analysis, Machine Translation, Part of Speech Tagging
advancedDeep Reinforcement Learning
Step 3
Concepts Covered
Reinforcement Learning, Neural Networks, PyTorch, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG)
advancedArtificial Intelligence
Step 4
Concepts Covered
Constraint Propagation, AI Algorithms, Constraint Satisfaction Problems, Backtracking Search, Optimization Algorithms, Search Algorithms, Minimax Search, Bayesian networks, Planning, Pattern Recognition, Time-Series Analysis with ML
advanced
Quantitative Analyst
Data-driven traders are now responsible for more than 30% of all US stock trades by investors (or about $1 trillion USD worth of investments). Build programming and linear algebra skills, then learn to analyze real data and build financial models for trading.
Recommended Programs
Free Courses
- AWS Machine Learning Foundations Course
- Linear Algebra Refresher Course
- Artificial Intelligence
- Intel® Edge AI Fundamentals with OpenVINO™
- AWS DeepRacer
- Intro to Artificial Intelligence
- Artificial Intelligence for Robotics
- Eigenvectors and Eigenvalues
- Introduction to Machine Learning Course
- Secure and Private AI
- Intro to TensorFlow for Deep Learning
- Intro to Deep Learning with PyTorch
- Machine Learning
- Real-Time Analytics with Apache Storm
- Knowledge-Based AI: Cognitive Systems
- Machine Learning for Trading
- Data Visualization and D3.js
- Reinforcement Learning
- Intro to Hadoop and MapReduce
- Data Analysis with R
- Machine Learning: Unsupervised Learning
- Big Data Analytics in Healthcare
- Introduction to Computer Vision
- Model Building and Validation
- A/B Testing
- Introduction to TensorFlow Lite
- AI Fundamentals
- Introduction to Machine Learning using Microsoft Azure