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My specialization certificates.

Generative Adversarial Networks Specialization Certificate

Coursera, May 2021

3 courses, approximately 3 months at 10 hours a week to complete

• Build Basic Generative Adversarial Networks (GANs)
• Build Better Generative Adversarial Networks (GANs)
• Apply Generative Adversarial Networks (GANs)

Accomplishments:

• Understand GAN components, build basic GANs using PyTorch and advanced DCGANs using convolutional layers, control your GAN and build conditional GAN
• Compare generative models, use FID method to assess GAN fidelity and diversity, learn to detect bias in GAN, and implement StyleGAN techniques
• Use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation

Natural Language Processing Specialization Certificate

Coursera, November 2020

4 courses, approximately 4 months at 8 hours a week to complete

• Natural Language Processing with Classification and Vector Spaces
• Natural Language Processing with Probabilistic Models
• Natural Language Processing with Sequence Models
• Natural Language Processing with Attention Models

Accomplishments:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.
• Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
• Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.
• Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

IBM AI Engineering Professional Certificate

Coursera, June 2020

6 courses, approximately 8 months at 3 hours a week to complete

• Deep Neural Networks with PyTorch
• AI Capstone Project with Deep Learning
• Machine Learning with Python
• Introduction to Deep Learning & Neural Networks with Keras
• Building Deep Learning Models with TensorFlow
• Scalable Machine Learning on Big Data using Apache Spark

Accomplishments:

• Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction
• Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn
• Deploy machine learning algorithms and pipelines on Apache Spark
• Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

Deep Learning Specialization Certificate

Coursera, June 2018

5 courses, approximately 4 months at 8 hours a week to complete

• Sequence Models
• Neural Networks and Deep Learning
• Convolutional Neural Networks
• Structuring Machine Learning Projects
• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Accomplishments:

• Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
• Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
• Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
• Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Machine Learning Specialization Certificate

Coursera, June 2014

3 courses, approximately 3 months at 5 hours a week to complete

• Supervised Machine Learning: Regression and Classification
• Advanced Learning Algorithms
• Unsupervised Learning, Recommenders, Reinforcement Learning

Accomplishments:

• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model