What's included

• Machine Learning Foundations

  This course provides a comprehensive introduction to the fundamental concepts and techniques of Machine Learning. It is designed to help students develop a solid foundation in understanding the principles that drive machine learning algorithms, including both supervised and unsupervised learning. Throughout the course, students will explore key algorithms, model evaluation techniques, and practical applications of machine learning. Hands-on programming exercises will enable students to apply the concepts they learn in real-world scenarios using Python and Jupyter Notebooks.

• Advanced Machine Learning

  This course jumps into advanced machine learning techniques, expanding upon the foundational concepts covered in the "Machine Learning Foundations" course. Students will explore complex algorithms such as Support Vector Machines (SVMs), Ensemble Methods (Bagging, Boosting, Stacking), and Generalized Linear Models (Ridge, LASSO, ElasticNet, Polynomial Regression). The course also covers the underlying optimization techniques that drive these models, equipping students with the skills needed to implement and fine-tune sophisticated machine learning models for a variety of applications. Hands-on programming exercises will reinforce theoretical concepts, providing practical experience in applying advanced machine learning techniques using Python and Jupyter Notebooks.

• End-to-End Machine Learning Pipelines

  This course focuses on designing, building, and deploying end-to-end machine learning pipelines. It covers the complete lifecycle of a machine learning project, from data ingestion and preprocessing to model training, validation, deployment, and monitoring. Students will learn how to automate and streamline these processes using modern tools and frameworks, ensuring scalability, reproducibility, and efficiency in real-world applications. The course emphasizes practical implementation through hands-on exercises, enabling students to create and manage machine learning pipelines using Python, Jupyter Notebooks, and popular libraries such as scikit-learn, PyTorch, and Apache Airflow.

• Machine Learning Prediction Thresholds with the Expected Value Framework

  This course provides a deep dive into the use of the Expected Value Framework (EVF) for optimizing prediction thresholds in machine learning models. The EVF connects machine learning classification models directly to business ROI, enabling data scientists to make more informed decisions by evaluating the costs and benefits associated with different threshold settings. Students will learn how to calculate expected savings, perform threshold optimization, and conduct sensitivity analyses to ensure robust and profitable model deployment. The course emphasizes practical application through hands-on exercises using Python and Jupyter Notebooks.

• MLOps Foundations

  This course provides a foundational understanding of MLOps (Machine Learning Operations), a discipline that combines machine learning, DevOps, and data engineering practices to streamline the deployment, monitoring, and maintenance of machine learning models in production. Students will explore the key principles, tools, and techniques used in MLOps, focusing on automating and managing the end-to-end machine learning lifecycle. Through hands-on exercises, participants will learn how to build robust MLOps pipelines, ensure model reproducibility, and maintain model performance over time using Python, Jupyter Notebooks, and industry-standard tools like Docker, Kubernetes, and MLflow.

• Deep Learning Foundations

  This course offers an in-depth introduction to deep learning, focusing on the foundational concepts, architectures, and techniques that form the basis of modern deep learning models. Students will explore essential topics such as neural networks, backpropagation, and optimization, as well as more advanced architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The course emphasizes practical implementation and hands-on experience, allowing students to build and train deep learning models using Python and popular deep learning libraries such as PyTorch.

• Natural Language Processing Foundations

  This course provides an introduction to Natural Language Processing (NLP), focusing on the fundamental techniques and algorithms used to process and analyze human language. Students will learn about essential concepts such as text preprocessing, tokenization, and vectorization, as well as more advanced topics like language modeling, word embeddings, and sequence-to-sequence models. The course is designed to offer hands-on experience in building NLP models using Python and popular NLP libraries like NLTK, spaCy, and Hugging Face's Transformers. By the end of this course, students will be equipped with the skills needed to develop and evaluate NLP models for various applications, including text classification, sentiment analysis, and machine translation.

• Advanced Natural Language Processing and Large Language Models (LLMs)

  This advanced course focuses on cutting-edge techniques and models in Natural Language Processing (NLP), with a particular emphasis on the architectures and methodologies that underpin large language models (LLMs). Building on foundational NLP concepts, this course exploreso advanced topics such as text embedding techniques, vector search, and the architecture of transformer models. Students will gain a deep understanding of how LLMs process input and generate coherent, context-aware output. Through hands-on programming exercises, students will implement and experiment with state-of-the-art models and techniques, preparing them for work on sophisticated NLP tasks and applications.

• Extending Large Language Model (LLM) Use Cases: Fine-Tuning and Retrieval Augmented Generation (RAG)

  This course explores advanced techniques for extending the capabilities of Large Language Models (LLMs) through fine-tuning and Retrieval Augmented Generation (RAG). Students will learn how to customize pre-trained LLMs for specific tasks using fine-tuning methods and integrate external knowledge sources through RAG to enhance model performance. The course emphasizes practical implementation, allowing students to build and optimize models that are capable of handling specialized tasks and generating contextually accurate responses by leveraging large datasets and retrieval mechanisms.

• Computer Vision Foundations

  This course offers a comprehensive introduction to the field of Computer Vision, focusing on the fundamental concepts, techniques, and algorithms used to analyze and interpret visual data. Students will explore key topics such as image processing, feature extraction, and object detection, as well as more advanced concepts like convolutional neural networks (CNNs) and image segmentation. The course emphasizes hands-on programming exercises using Python and popular libraries such as OpenCV and PyTorch, enabling students to build and apply computer vision models to real-world tasks.

• Developing an Organizational Data Strategy

  This course provides a comprehensive guide to developing and implementing a data strategy within an organization. It covers the key components of a successful data strategy, including data governance, data quality, data architecture, and data lifecycle management. Students will learn how to align data strategy with business objectives, create a strategic roadmap, and foster a data-driven culture within their organization. The course emphasizes practical approaches to ensuring reliable data, compliance with regulations, and collaboration across teams. Hands-on programming exercises will allow students to apply concepts using Python and Jupyter Notebooks, focusing on data governance frameworks, data quality assessment, and metadata management.

• Building Agentic AI Applications with LangChain

  This course introduces students to the development of agentic AI applications using LangChain. Agentic AI refers to autonomous AI agents that can perceive their environment, make decisions, and take actions to achieve specific goals. Students will learn how to leverage LangChain, a framework for developing language model-driven applications. The course covers fundamental concepts, architectural patterns, and practical implementations, enabling students to build sophisticated AI agents capable of complex reasoning, task automation, and interaction with users.

• Exploring the LangChain Ecosystem: LangSmith, LangServe, and LangGraph

  This course offers a deep dive into the LangChain ecosystem, focusing on three core components: LangSmith, LangServe, and LangGraph. Students will learn how to leverage these tools to build, deploy, and manage sophisticated AI-driven applications. LangSmith provides a framework for developing smart language models, LangServe enables the deployment and serving of AI models, and LangGraph allows for the visualization and structuring of AI workflows. By the end of the course, students will be proficient in creating and managing end-to-end AI applications using the full capabilities of the LangChain ecosystem.

• Hugging Face Foundations

  This course offers an in-depth introduction to the Hugging Face ecosystem, which provides powerful tools and libraries for building and deploying state-of-the-art natural language processing (NLP) models. Students will explore the fundamentals of using Hugging Face Transformers, Datasets, and the Hub to develop, fine-tune, and deploy NLP models. The course covers key concepts such as model tokenization, fine-tuning pre-trained models, managing datasets, and deploying models in production environments. Through hands-on programming exercises, students will gain practical experience in leveraging Hugging Face tools to build robust NLP applications.

• Quantization Methods for Large Language Models (LLMs)

  This course provides a deep dive into the quantization techniques used to optimize Large Language Models (LLMs) for efficient deployment and inference. As LLMs grow in size and complexity, quantization offers a powerful approach to reducing their memory footprint and computational requirements while maintaining acceptable levels of accuracy. Students will learn about different quantization methods, their trade-offs, and how to implement these techniques using popular machine learning frameworks. The course will include practical exercises where students will apply quantization to LLMs and evaluate the impact on performance and efficiency.