| Natural Language Processing with Classification and Vector Spaces |
Link |
Course |
- Sentiment Analysis with Logistic Regression
- Sentiment Analysis with Naïve Bayes
- Vector Space Models
- Machine Translation and Document Search
|
Finished in Summer 2022 |
DeepLearning.AI |
| Natural Language Processing with Probabilistic Models |
Link |
Course |
- Autocorrect, using Minimum Edit Distance and Dynamic Programming.
- POS Tagging and Hidden Markov Models
- N-gram models and Autocomplete.
- Word embeddings with Neural Networks.
|
Finished in Summer 2022 |
DeepLearning.AI |
| Natural Language Processing with Sequence Models |
Link |
Course |
- Neural Networks for Sentiment Analysis
- Recurrent Neural Networks for Language Modeling
- LSTMs and Named Entity Recognition
- Siamese Networks
|
Finished in Summer 2022 |
DeepLearning.AI |
| Natural Language Processing with Attention Models |
Link |
Course |
- Neural Machine Translation
- Text Summarization
- Question Answering
- Chatbot
|
Finished in Summer 2022 |
DeepLearning.AI |
| Neural Networks and Deep Learning |
Link |
Course |
- Introduction to Deep Learning
- Neural Networks Basics
- Shallow Neural Networks
- Deep Neural Networks
|
Finished in Summer 2022 |
DeepLearning.AI |
| Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization |
Link |
Course |
- Practical Aspects of Deep Learning
- Optimization Algorithms
- Hyperparameter Tuning, Batch Normalization and Programming Frameworks
|
Finished in Summer 2022 |
DeepLearning.AI |
| Structuring Machine Learning Projects |
Link |
Course |
- ML Strategy
- ML Strategy
|
Finished in Summer 2022 |
DeepLearning.AI |
| Convolutional Neural Networks |
Link |
Course |
- Foundations of Convolutional Neural Networks
- Deep Convolutional Models: Case Studies
- Object Detection
- Special Applications: Face recognition & Neural Style Transfer
|
Finished in Fall 2022 |
DeepLearning.AI |
| Sequence Models |
Link |
Course |
- Recurrent Neural Networks
- Natural Language Processing & Word Embeddings
- Sequence Models & Attention Mechanism
- Transformer Network
|
Finished in Fall 2022 |
DeepLearning.AI |
| Supervised Machine Learning: Regression and Classification |
Link |
Course |
- Introduction to Machine Learning
- Regression with multiple input variables
- Classification
|
Finished in Fall 2022 |
DeepLearning.AI & Stanford University |
| Advanced Learning Algorithms |
Link |
Course |
- Neural Networks
- Neural network training
- Advice for applying machine learning
- Decision trees
|
Finished in Fall 2022 |
DeepLearning.AI & Stanford University |
| Unsupervised Learning, Recommenders, Reinforcement Learning |
Link |
Course |
- Unsupervised learning
- Recommender systems
- Reinforcement learning
|
Finished in Winter 2022 |
DeepLearning.AI & Stanford University |
| Build Basic Generative Adversarial Networks (GANs) |
Link |
Course |
- Intro to GANs
- Deep Convolutional GANs
- Wasserstein GANs with Gradient Penalty
- Conditional GAN & Controllable Generation
|
Finished in Winter 2022 |
DeepLearning.AI |
| Build Better Generative Adversarial Networks (GANs) |
Link |
Course |
- Evaluation of GANs
- GAN Disadvantages and Bias
- StyleGAN and Advancements
|
Finished in Winter 2022 |
DeepLearning.AI |
| Apply Generative Adversarial Networks (GANs) |
Link |
Course |
- GANs for Data Augmentation and Privacy
- Image-to-Image Translation with Pix2Pix
- Unpaired Translation with CycleGAN
|
Finished in Winter 2022 |
DeepLearning.AI |
| Natural Language Processing using Tensorflow |
Link |
Course |
- Sentiment in Text
- Word embeddings
- Sequence models
- Sequence models and literature
|
Finished in Summer 2023 |
DeepLearning.AI |
| Sequences, Time Series and Prediction |
Link |
Course |
- Sequences and Prediction
- Deep Neural Networks for Time Series
- Recurrent Neural Network for Time Series
- Real-world time series data
|
Finished in Summer 2023 |
DeepLearning.AI |
| Convolutional Neural Networks in TensorFlow |
Link |
Course |
- Exploring a Larger Dataset
- Augmentation: A technique to avoid overfitting
- Transfer Learning
- Multiclass Classification
|
Finished in Summer 2023 |
DeepLearning.AI |
| Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning |
Link |
Course |
- A new Programming Paradigm
- Introduction to Computer Vision
- Enhancing Vision with Convolutional Neural Networks
- Using real-world images
|
Finished in Summer 2023 |
DeepLearning.AI |
| Introduction to Machine Learning in Production |
Link |
Course |
- Overview of the ML lifecycle and deployment
- Select and train a model
- Data definition and Baseline
|
Finished in Summer 2023 |
DeepLearning.AI |
| Machine Learning data lifecycle in Production |
Link |
Course |
- Collecting, Labeling and Validating data
- Feature Engineering, Transformation and Selection
- Data Journey and Data Storage
- Advanced Labeling, Augmentation and Data Preprocessing
|
Finished in Summer 2023 |
DeepLearning.AI |
| Machine Learning modeling pipelines in Production |
Link |
Course |
- Neural Architecture Search
- Model Resource Management Techniques
- High-performance modeling
- Model Analysis
- Interpretability
|
Finished in Summer 2023 |
DeepLearning.AI |
| Deploying Machine Learning Models in Production |
Link |
Course |
- Model Serving: Introduction
- Model Serving: Patterns and Infrastructure
- Model Management and Delivery
- Model Monitoring and Logging
|
Finished in Fall 2023 |
DeepLearning.AI |
| Custom Models, Layers, and Loss Functions with TensorFlow |
Link |
Course |
- Functional APIs
- Custom Loss Functions
- Custom Layers
- Custom Models
- Bonus Content - Callbacks
|
Finished in Fall 2023 |
DeepLearning.AI |
| Browser-based Models with TensorFlow.js |
Link |
Course |
- Introduction to Tensorflow.js
- Image Classification In the Browser
- Converting Models to JSON Format
- Transfer Learning with Pre-Trained Models
|
Finished in Fall 2023 |
DeepLearning.AI |
| Analyze Datasets and Train ML Models using AutoML |
Link |
Course |
- Explore the Use Case and Analyze the Dataset
- Data Bias and Feature Importance
- Use Automated Machine Learning to train a Text Classifier
- Built-in algorithms
|
Finished in Fall 2023 |
DeepLearning.AI & AWS |
| Custom and Distributed Training with TensorFlow |
Link |
Course |
- Differentiation and Gradients
- Custom Training
- Graph Mode
- Distributed Training
|
Finished in Winter 2023 |
DeepLearning.AI |
| Advanced Computer Vision with TensorFlow |
Link |
Course |
- Introduction to Computer Vision
- Object Detection
- Image Segmentation
- Visualization and Interpretability
|
Finished in Winter 2023 |
DeepLearning.AI |
| Generative Deep Learning with TensorFlow |
Link |
Course |
- Week 1: Style Transfer
- Week 2: AutoEncoders
- Week 3: Variational AutoEncoders
- Week 4: GANs
|
Finished in Winter 2023 |
DeepLearning.AI |
| Device-based Models with TensorFlow Lite |
Link |
Course |
- Device-based models with TensorFlow Lite
- Running a TF model in an Android App
- Building the TensorFlow model on IOS
- TensorFlow Lite on devices
|
Finished in Winter 2023 |
DeepLearning.AI |
| Generative AI with Large Language Models |
Link |
Course |
- Generative AI use cases, project lifecycle, and model pre-training
- Fine-tuning and evaluating large language models
- Reinforcement learning and LLM-powered applications
|
Finished in Winter 2023 |
DeepLearning.AI & AWS |
| Build, Train, and Deploy ML Pipelines using BERT |
Link |
Course |
- Feature Engineering and Feature Store
- Train, Debug and Profile a Machine Learning Model
- Deploy End-To-End Machine Learning pipelines
|
Finished in Winter 2023 |
DeepLearning.AI & AWS |
| Optimize ML Models and Deploy Human-in-the-Loop Pipelines |
Link |
Course |
- Advanced model training, tuning and evaluation
- Advanced model deployment and monitoring
- Data labeling and human-in-the-loop pipelines
|
Finished in Winter 2023 |
DeepLearning.AI & AWS |
| Data Pipelines with TensorFlow Data Services |
Link |
Course |
- Data Pipelines with TensorFlow Data Services
- Splits and Slices API for Datasets in TF
- Exporting Your Data into the Training Pipeline
- Performance
|
Finished in Winter 2023 |
DeepLearning.AI |
| Advanced Deployment Scenarios with TensorFlow |
Link |
Course |
- TensorFlow Extended
- Sharing pre-trained models with TensorFlow Hub
- Tensorboard: tools for model training
- Federated Learning
|
Finished in Winter 2023 |
DeepLearning.AI |
| Divide and Conquer, Sorting and Searching, and Randomized Algorithms |
Link |
Course |
- Introduction; “big-oh” notation and asymptotic analysis.
- Divide-and-conquer basics; the master method for analyzing divide and conquer algorithms.
- The QuickSort algorithm and its analysis; probability review.
- Linear-time selection; graphs, cuts, and the contraction algorithm.
|
Finished in Spring 2024 |
Stanford |
| Graph Search, Sortest Path, and Data Structures |
Link |
Course |
- Breadth-first and depth-first search; computing strong components; applications.
- Dijkstra’s shortest-path algorithm.
- Heaps; balanced binary search trees.
- Hashing; bloom filters.
|
Finished in Spring 2024 |
Stanford |
| Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming |
Link |
Course |
- Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim’s MST algorithm.
- Kruskal’s MST algorithm and applications to clustering; advanced union-find (optional).
- Huffman codes; introduction to dynamic programming.
- Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
|
Finished in Spring 2024 |
Stanford |
| Shortest Paths Revisited, NP-Complete Problems and What To Do About Them |
Link |
Course |
- The Bellman-Ford algorithm; all-pairs shortest paths.
- NP-complete problems and exact algorithms for them.
- Approximation algorithms for NP-complete problems.
- Local search algorithms for NP-complete problems; the wider world of algorithms.
|
Finished in Winter 2024 |
Stanford |