Machine Learning Engineer for Microsoft Azure
Concepts Covered: Azure Machine Learning, Azure Machine Learning SDK, Automation with Pipelines, Automated ML, Machine Learning Operations
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.
Concepts Covered: Azure Machine Learning, Azure Machine Learning SDK, Automation with Pipelines, Automated ML, Machine Learning Operations
Concepts Covered: Artificial Intelligence, 2D Medical Imaging, 3D Medical Imaging, Electronic Health Record Data, Deep Learning, Wearable Device Data, Supervised Learning
Concepts Covered: TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
Concepts Covered: AI Products, Training ML Models, Annotating Datasets, Prototyping a Product
Concepts Covered: Introduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
Concepts Covered: Python, NumPy, Pandas, Matplotlib, PyTorch
Concepts Covered: Momentum Trading Strategy, Smart Beta, Alpha Factors, Natural Language Processing, Deep Learning, Neural Networks
Concepts Covered: Convolutional Neural Networks, Recurrent Neural Networks, Simultaneous Localization and Mapping, Object Tracking, Image Classification, Deep Learning
Concepts Covered: Machine Learning, Speech Recognition, Sentiment Analysis, Machine Translation, Part of Speech Tagging
Concepts Covered: Reinforcement Learning, Neural Networks, PyTorch, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG)
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
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
Concepts Covered: Artificial Intelligence, Machine Learning, Business Strategy, Data Labeling, Data Modeling
Concepts Covered: API development, Automated model scoring, CI/CD, Model testing, Data Version Control, Machine Learning Pipelines
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
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
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
Concepts Covered: AI Fairness, AI Governance, AI Transparency, Model Bias Analysis, Model Bias Mitigation, Explainable AI, Ethical AI
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.
TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
intermediateIntroduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
intermediatePython, NumPy, Pandas, Matplotlib, PyTorch
beginnerFeature 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
intermediateMachine 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.
TensorFlow, Deep Learning, scikit-learn, Supervised Learning, Unsupervised Learning
intermediateIntroduction to Machine Learning, Supervised Learning, Deep Learning, Unsupervised Learning
intermediatePython, NumPy, Pandas, Matplotlib, PyTorch
beginnerFeature 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 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.
Python, NumPy, Pandas, Matplotlib, PyTorch
beginnerFeature 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
intermediateNeural 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
intermediateArtificial 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.
Convolutional Neural Networks, Recurrent Neural Networks, Simultaneous Localization and Mapping, Object Tracking, Image Classification, Deep Learning
advancedMachine Learning, Speech Recognition, Sentiment Analysis, Machine Translation, Part of Speech Tagging
advancedReinforcement Learning, Neural Networks, PyTorch, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG)
advancedConstraint Propagation, AI Algorithms, Constraint Satisfaction Problems, Backtracking Search, Optimization Algorithms, Search Algorithms, Minimax Search, Bayesian networks, Planning, Pattern Recognition, Time-Series Analysis with ML
advancedData-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.