This course guides learners through the structured development of predictive models using Random Forest techniques in R, specifically applied to employee attrition data. The course is divided into two comprehensive modules. The first module introduces the foundational concepts of classification and Random Forest algorithms, guiding learners to explain, identify, and prepare … [Read more...] about R: Design & Evaluate Random Forests for Attrition
Data Science
Data Science Fundamentals Part 2: Unit 3
This course takes a step-by-step approach to the process of building robust models to predict real-world outcomes and uncover valuable insights from your data. You’ll start with a solid foundation in probability and statistical distributions, learning how to estimate parameters and fit models using industry-standard libraries such as SciPy and NumPy. You'll dive into the theory … [Read more...] about Data Science Fundamentals Part 2: Unit 3
Apache Spark: Apply & Evaluate Big Data Workflows
This course introduces beginners to the foundational and intermediate concepts of distributed data processing using Apache Spark, one of the most powerful engines for large-scale analytics. Through two progressively structured modules, learners will identify Spark’s architecture, describe its core components, and demonstrate key programming constructs such as Resilient … [Read more...] about Apache Spark: Apply & Evaluate Big Data Workflows
Learning Deep Learning
Guided by real-world programming examples in TensorFlow and PyTorch, you’ll master neural network fundamentals, convolutional and recurrent architectures, and cutting-edge topics like transformers, large language models, and multimodal AI. By the end of this specialization, you’ll be equipped to build, train, and deploy deep learning models for image classification, language … [Read more...] about Learning Deep Learning
Introduction to Transformer Models for NLP
This course provides practical instruction on transformer architectures such as BERT, GPT, and T5. You will learn about attention mechanisms, transfer learning, and model fine-tuning through coding exercises and case studies. By the end, you will be able to build and optimize NLP models for various applications. … [Read more...] about Introduction to Transformer Models for NLP
