Coursera Courses

Links to repositories containing material for completed (and work-in-progress) of coursers I have attempted on the Coursera platform

N.B

• Bold indicates the individual course has been completed.
• Hyperlinks are to repositories, not to course webpages.
• If there are broken links, this more likely means the repository is temporarily private.

Individual Courses

• University of Cape Town: Julia Scientific Programming
• North-Western University: Digital Image and Video Processing
• Stanford: Machine Learning

Specializations

• Deeplearning.ai: TensorFlow: Data and Deployment
  1. Browser-based Models with TensorFlow.js
  2. Device-based Models with TensorFlow Lite
  3. Data Pipelines with TensorFlow Data Services
  4. Advanced Deployment Scenarios with TensorFlow
• Deeplearning.ai: TensorFlow: Advanced Techniques
  1. Custom Models, Layers, and Loss Functions with TensorFlow
  2. Custom and Distributed Training with TensorFlow
  3. Advanced Computer Vision with TensorFlow
  4. Apply Generative Adversarial Networks (GANs)
• Deeplearning.ai: TensorFlow in Practice
  1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  2. Convolutional Neural Networks in TensorFlow
  3. Natural Language Processing in TensorFlow
  4. Sequences, Time Series and Prediction
• Deeplearning.ai: Deep Learning [TF1.x]
  1. Neural Networks and Deep Learning
  2. Improving Deep Neural Networks
  3. Structuring Machine Learning Projects
  4. Convolutional Neural Networks
  5. Sequence Models
• IBM: Advanced Data Science
  1. Fundamentals of Scalable Data Science
  2. Advanced Machine Learning and Signal Processing
  3. Applied AI with DeepLearning
  4. Advanced Data Science Capstone
• University of Alberta: Reinforcement Learning
  1. Fundamentals of Reinforcement Learning
  2. Sample-based Learning Methods
  3. Prediction and Control with Function Approximation
  4. A Complete Reinforcement Learning System (Capstone)
• EPFL: Functional Scala
  1. Functional Programming Principles in Scala
  2. Functional Program Design in Scala
  3. Parallel programming
  4. Big Data Analysis with Scala and Spark
  5. Functional Programming in Scala Capstone
• EPFL: Digital Signal Processing
  1. Digital Signal Processing 1: Basic Concepts and Algorithms
  2. Digital Signal Processing 2: Filtering
  3. Digital Signal Processing 3: Analog vs Digital
  4. Digital Signal Processing 4: Applications
• Stanford: Probabilistic Graphical Models
  1. Probabilistic Graphical Models 1: Representation
  2. Probabilistic Graphical Models 2: Inference
  3. Probabilistic Graphical Models 3: Learning
• Imperial College London: Deep Learning in TensorFlow 2.x
  1. Getting started with TensorFlow 2
  2. Customising your models with TensorFlow 2
  3. Probabilistic Deep Learning with TensorFlow 2
• Imperial College London: Mathematics for Machine Learning
  1. Mathematics for Machine Learning: Linear Algebra
  2. Mathematics for Machine Learning: Multivariate Calculus
  3. Mathematics for Machine Learning: PCA