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 Python

Getting 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

Predictive 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

Mentoring Machine Learning Architect

Applied 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