Practical Data Science On AWS Cloud

During this specialization, i've performed :

• ✅ Datasets analysis and ML Models training using AutoML
• ✅ Building, Training, and Deployment of ML Pipelines using BERT
• ✅ ML Models Optimization and Human-in-the Loop Pipelines Deployment

Analyze Datasets and Train ML Models using AutoML

During this course, i've performed :

• ✅ Data Ingestion, Exploration and Visualization on Product Reviews Dataset for multi-class text classification.
• ✅ Statistical Bias detection and Dataset Balancing
• ✅ Inspection and comparison of models generated with AutoML.
• ✅ Training a text classifier with BlazingText with deployment as a real-time inference endpoint to serve predictions.

Build, Train, and Deploy ML Pipelines using BERT

During this course, i've performed :

• ✅ Transformation of a raw text dataset into machine learning features and stored them in a feature store.
• ✅ Fine-tuning, debugging, and profiling a pre-trained BERT model
• ✅ Orchestration of ML workflows and tracking model lineage and artifacts in an end-to-end machine learning pipeline.

Optimize ML Models and Deploy Human-in-the Loop Pipelines

During this course, i've performed :

• ✅ Training, tuning, and models evaluation using data-parallel and model-parallel strategies and automatic model tuning.
• ✅ Model deployment with A/B testing, monitor model performance, and drift detection from baseline metrics.
• ✅ Data labeling and scale using private human workforces and building human-in-the-loop pipelines.

Supervised Machine Learning: Regression and Classification

During this course, i've :

• ✅ Learned new concepts from industry experts
• ✅ Gain a foundational understanding of a subjects & tools
• ✅ Develop job-relevant skills with hands-on projects
• ✅ Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Neural Networks and Deep Learning

During this course, i've performed :

• ✅ Studying the rise of deep learning and its application domains
• ✅ Setting up ML problem with neural network mindset using vectorization
• ✅ Building a neural network with one hidden layer using forward propagation and backpropagation.
• ✅ Building and training deep neural networks for computer vision from scratch