Often you are simply required to write a one-liner to implement a formula that has been given in the above cell. Machine Learning Yearning is a deeplearning. Andrew Ng is currently writing, teaches you how to structure Machine Learning projects. Although it is not explicitly built on top of the introductory course I think it is a very good idea finishing that one first before starting the specialization in fact Andrew Ng. At the end of the day, sharing is caring. What are their strengths and weaknesses? How can their outputs be interpreted? Well, for me summarizing in my own words what I have learned is a good opportunity to consolidate my knowledge and refine my thoughts. Most of the time you end up with a working application that you can try out with your own data.
They provide an example of what an application of the theory might look like if put into practice. If you would like to receive a draft of each chapter as it is finished, please sign up for the mailing list. Deep Learning Specialization 16 weeks This is a 16-week specialization with a focus on Deep Learning including all kinds of variations of Neural Networks. This is the new book by Andrew Ng, still in progress. You can sign up for the newsletter at. Plus, with a written summary I also get a searchable reference for later use that I can go through without having to watch the videos again. The idea of being able to teach my computer something without having to explicitly program it is electrifying.
If you find mistakes typos, wrong formulas, wrong explanations, … or the descriptions seem unclear to you, please do not hesitate to send me a correction. Or you implement a Neural Network that is able to generate music - man I love this course. The book focuses on machine learning models for tabular data also called relational or structured data and less on computer vision and natural language processing tasks. It is fun to e. This helps me improving the quality of this site. Or you will build your own Alexa-style trigger-word detection system - how cool is that! This book is about making machine learning models and their decisions interpretable. If you have to write more than one line, the approximate number of lines is always stated in the comments.
In contrast to the introductory course all assignments are in Python 3. The book will be around 100 pages, and contain many easy-to-read 1-2 page chapters. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. They are not always challenging, i. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. Unless your accuracy is very near to 0 or 1, you're going to need more like a million samples to have a good chance of detecting a difference of 0. All interpretation methods are explained in depth and discussed critically.
. It is free of charge which might attribute to its immense popularity. At this stage, this book is an introduction to introduction to introduction to machine learning. For me, those assignments were still some of the highlights of this course. Basically the book discusses common problems and how to deal with them.
× M achine Learning has fascinated me since I first heard about it. Machine Learning 5 Minute Read This page uses. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable. If the assumption is not an underlying binomial, can you explain why? Ng is also an early pioneer in online learning - which led to the co-founding of Coursera. You can annotate or highlight text directly on this page by expanding the bar on the right. Preface Machine learning has great potential for improving products, processes and research. You will never have to write whole blocks of code, so you are not going to get a whole lot of experience implementing something from scratch.
Mail: Website: Follow me on Twitter! If you are interested in improving the interpretability of your machine learning models, do not hesitate to contact me! The explanation are very comprehensive and illustrated with real-world examples. This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. Each course consists of a series of videos with occasional ungraded mini-quizzes inside them. Andrew will be releasing more draft chapters in the next week. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. The instructions in those assignments are always very extensive and clear and go over some of the theory again to make sure you get the context.