Program focusing on the end‑to‑end ML workflow using AWS SageMaker—covering data prep, feature engineering, model training (XGBoost, AutoGluon), tuning, deployment, and deep learning (CV & NLP).

Comprehensive intro to Python for AI—with NumPy, Pandas, Matplotlib, and foundational neural network implementation using PyTorch.

Covers fundamental data analysis, visualization, and analytics tools—designed to make data‑driven business decisions.

Hands‑on anomaly detection workshop using GPU‑accelerated XGBoost, deep autoencoders, and GANs to detect patterns in real‑world datasets.

Introductory ML course exploring supervised learning, model evaluation, and practical implementation using Python and scikit‑learn.

Fundamental course covering Python basics—variables, loops, functions, data types, and file I/O geared for data analysis.

Learn essential SQL querying—SELECT, JOINs, aggregation, and filtering—to extract and analyze data from relational databases.

Hands‑on exposure to core Google Cloud services—compute, storage, networking, and ML APIs—through practical labs.

Explores deep learning models for medical imaging applications—classification and segmentation using convolutional neural networks.

Professional certificate covering cloud concepts and GCP services to support digital transformation and business use‑cases.

Validated skills in building and training TensorFlow models for vision, NLP, and time series tasks.

Begins with TensorFlow basics and progresses through model training for computer vision using Keras and TF pipelines.

Covers essential IT support skills including troubleshooting, networking, OS management, system administration, and security.

Fundamental deep learning course introducing neural networks, CNNs, and hands‑on model implementation using PyTorch/TensorFlow.

Project‑based bootcamp covering supervised learning techniques, classification, and model evaluation fundamentals.

Introduction to key tech career skills—cloud computing, software development basics, and digital tools.

Intensive GCP challenge focused on deploying cloud solutions and APIs over a two‑month period.

Introduces ML workflows and model development using IBM Watson Studio—including supervised and unsupervised learning.