Distance learningML Track 1: ML ProgrammerML Track 2: DL Programmer ML Track 3: ML Engineer ML Track 4: ML Architect Subject breakdown Machine Learning Track 1: ML Programmer NLP for ML with Python: NLP Using Python & NLTK NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn Linear Algebra and Probability: Fundamentals of Linear Algebra Linear Algebra & Probability: Advanced Linear Algebra Linear Regression Models: Introduction to Linear Regression Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras Linear Regression Models: Multiple and Parsimonious Linear Regression Linear Regression Models: An Introduction to Logistic Regression Linear Regression Models: Simplifying Regression and Classification with Estimators Computational Theory: Language Principle & Finite Automata Theory Computational Theory: Using Turing, Transducers, & Complexity Classes Model Management: Building Machine Learning Models & Pipelines Model Management: Building & Deploying Machine Learning Models in Production Bayesian Methods: Bayesian Concepts & Core Components Bayesian Methods: Implementing Bayesian Model and Computation with PyMC Bayesian Methods: Advanced Bayesian Computation Model Reinforcement Learning: Essentials Reinforcement Learning: Tools & Frameworks Math for Data Science & Machine Learning Building ML Training Sets: Introduction Building ML Training Sets: Preprocessing Datasets for Linear Regression Building ML Training Sets: Preprocessing Datasets for Classification Linear Models & Gradient Descent: Managing Linear Models Linear Models & Gradient Descent: Gradient Descent and Regularization Final Exam: ML Programmer ML Programming with PythonMachine Learning Track 2: DL ProgrammerGetting Started with Neural Networks: Biological & Artificial Neural Networks Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms Building Neural Networks: Development Principles Building Neural Networks: Artificial Neural Networks Using Frameworks Training Neural Networks: Implementing the Learning Process Training Neural Networks: Advanced Learning Algorithms Improving Neural Networks: Neural Network Performance Management Improving Neural Networks: Loss Function & Optimization Improving Neural Networks: Data Scaling & Regularization ConvNets: Introduction to Convolutional Neural Networks ConvNets: Working with Convolutional Neural Networks Convolutional Neural Networks: Fundamentals Convolutional Neural Networks: Implementing & Training Convo Nets for Visual Recognition: Filters & Feature Mapping in CNN Convo Nets for Visual Recognition: Computer Vision & CNN Architectures Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling Fundamentals of Sequence Model: Language Model & Modeling Algorithms Build & Train RNNs: Neural Network Components Build & Train RNNs: Implementing Recurrent Neural Networks ML Algorithms: Multivariate Calculation & Algorithms ML Algorithms: Machine Learning Implementation Using Calculus & Probability DL Programming with Python Final Exam: DL Programmer Machine Learning Track 3: ML EngineerPredictive Modeling: Predictive Analytics & Exploratory Data Analysis Predictive Modeling: Implementing Predictive Models Using Visualizations Predictive Modelling Best Practices: Applying Predictive Analytics Planning AI Implementation Automation Design & Robotics ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel Enterprise Services: Enterprise Machine Learning with AWS Enterprise Services: Machine Learning Implementation on Microsoft Azure Enterprise Services: Machine Learning Implementation on Google Cloud Platform Architecting Balance: Designing Hybrid Cloud Solutions Enterprise Architecture: Architectural Principles & Patterns Enterprise Architecture: Design Architecture for Machine Learning Applications Architecting Balance: Hybrid Cloud Implementation with AWS & Azure Refactoring ML/DL Algorithms: Techniques & Principles Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms Architecting ML/DL Apps with Python Final Exam: ML Engineer Machine Learning Track 4: ML ArchitectApplied Predictive Modeling Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools Implementing Deep Learning: Optimized Deep Learning Applications Applied Deep Learning: Unsupervised Data Applied Deep Learning: Generative Adversarial Networks and Q-Learning Advanced Reinforcement Learning: Principles Advanced Reinforcement Learning: Implementation ML/DL Best Practices: Machine Learning Workflow Best Practices ML/DL Best Practices: Building Pipelines with Applied Rules Research Topics in ML and DL Deep Learning with Keras Architecting Advanced ML/DL Apps with Python Final Exam: ML Architect |