https://github.com/tallamjr/coursera

Links to repositories containing material for completed (and work-in-progress) of coursers I have attempted on the Coursera platform
https://github.com/tallamjr/coursera

coursera coursera-specialization

Last synced: 24 days ago
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Links to repositories containing material for completed (and work-in-progress) of coursers I have attempted on the Coursera platform

• Host: GitHub
• URL: https://github.com/tallamjr/coursera
• Owner: tallamjr
• Created: 2016-10-24T17:00:55.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2021-02-15T11:17:29.000Z (almost 4 years ago)
• Last Synced: 2024-10-30T08:19:59.261Z (2 months ago)
• Topics: coursera, coursera-specialization
• Language: HTML
• Homepage: https://www.coursera.org/
• Size: 1.69 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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**](https://github.com/tallamjr/cape-town-scientific-julia)

* [North-Western University: Digital Image and Video Processing](https://github.com/tallamjr/nw-image-video-processing)

* [Stanford: Machine Learning](https://github.com/tallamjr/stanford-ml)

### Specializations

* [Deeplearning.ai: TensorFlow: Data and Deployment](https://github.com/tallamjr/tensorflow-data-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](https://github.com/tallamjr/advanced-tensorflow)

    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](https://github.com/tallamjr/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]](https://github.com/tallamjr/deeplearning-ai)

    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](https://github.com/tallamjr/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](https://github.com/tallamjr/univ-alberta-RL)

    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](https://github.com/tallamjr/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](https://github.com/tallamjr/epfl-dsp)

    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](https://github.com/tallamjr/stanford-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](https://github.com/tallamjr/imperial-tf2-deeplearning)

    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](https://github.com/tallamjr/imperial-ml-mathematics)

    1. Mathematics for Machine Learning: Linear Algebra

    2. Mathematics for Machine Learning: Multivariate Calculus

    3. Mathematics for Machine Learning: PCA