The following courses are selected for the quality of the content, the added value offered to business professionals given the time and money commitment, as well as the caliber, experience and teaching abilities of the instructors. To post a course or boot camp, or for sponsorship, contact Vincent Granville at vincentg@MLTechniques.com.
From our Partners
Applied Machine Learning. Instructor: Kirk Borne, Ph.D.
The best way to master Machine Learning is to understand the core concepts, be capable of explaining them with simple everyday examples, and be able to apply those to solving business problems. This course focuses on building up your theoretical and practical knowledge so that you can understand how to identify ML problems, choose the right algorithms for solving the problem at hand, build robust models in Python, and explain your models and communicate their results in common business language for colleagues, clients, and executives. You’ll learn from real world examples and apply your learnings to a variety of business problems. To register for the course, follow this link.
Synthetic Data and Interpretable Machine Learning. Instructor: Vincent Granville, Ph.D.
This live course is based on my book “Synthetic Data”, available here. Participants will receive a free copy of this book. The information below provides a brief overview of the course. For a full description, follow this link.
The course provides an overview of modern data generation techniques, and how they can be derived from and blended with real life data to improve performance of machine learning algorithms such as classification, clustering, regression, decision trees or neural networks. Synthetic data enhances training sets by allowing you to make predictions or assign a label to new observations that are significantly different from those in your data set. It is particularly useful if your training set is small or unbalanced. It also allows you to test the limits of your algorithms and find examples where it fails to work (for instance, failing to identify spam). Thus the word adversarial modeling often used in this context. We will show how to design rich, good quality synthetic data to meet these goals. Examples include tabular data and computer vision problems. New machine learning techniques are introduced, in particular a simple optimization algorithm unifying all regression methods — supervised and unsupervised.
The course starts with an introduction to synthetic data, explaining the benefits: increased interpretability of blackbox-generated decisions, reduction of algorithmic biases, elimination of data leakage, increased security and better compliance with laws about privacy and data protection.
Intuitive Machine Learning and Explainable AI. Instructor: Vincent Granville, Ph.D.
Solid machine learning foundations presented by a world leading expert. Full life cycle of machine learning development applied to enterprise-grade projects. Includes Python coding, scientific computing, optimization algorithms, explainable AI and state-of-the-art methods favoring simplicity, scalability, reusability, replicability, fast implementation, and easy maintenance. From data cleaning to model design, testing and feature selection, to great visualizations easy to “sell” to stakeholders and decision makers. Depending on the student background and interest, topics may cover augmented data, generative and mixture models, big data, deep neural networks, image processing, machine learning in GPU, graph models, curve and shape fitting, taxonomy creation (NLP) and more. Numerous regression methods including logistic or Lasso are unified and presented under a same umbrella.