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Building Machine Learning Powered Applications: Going from Idea to Product (DOWNLOAD)

The Apache Wars: The Hunt for Geronimo, the Apache Kid, and the Captive Boy Who Started theLongest War in American History jUsefulTo me this book felt like a lot of bad Medium or Towards Data Science articles stacked on top of each other I don t think the author has built a machine learning powered application This book is extremely lightweight at a little over 200 pages and is too high level to have any practicality The content isust an odd assortment of stuff with bizarre sidebars on transfer learning and code snippets with no cohesiveness The chapter on deployment is exactly ten pages long and The chapter on deployment is exactly ten pages long and a big nothing burger I don t even recommend this book for a beginner because it will CONFUSE THEM THIS BOOK IS NOT them This book is NOT overly technical book The way I read it it s a book that s centered around the lessons the author Emmanuel learned during his time as a data scientistML engineer He formats these lessons in such a way that makes the book extremely easy to read and grasp As a newly hired data scientist who has been charged with created the company s anomaly detection application this book will serve me well In the ungle of publications about ML this book provides a uniue hands on and principled set of tools to really get you through a project from start to finish A must read to any working data scientist or data engineer "out there Can t recommend it enough. To improve the model until it fulfills your original "there Can t recommend it enough. To improve the model until it fulfills your original Part IV covers deployment and monitoring strategiesThis book will help youDefine your product goal and set up a machine learning problemBuild your first end to end pipeline uickly and acuire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environme. Pply XGBoost versus using Scikit Learn But then on the next page it tries to explain the K Nearest Neighbors algorithm Like you are expecting the reader to next page it tries to explain the K Nearest Neighbors algorithm Like you are expecting the reader to how different Machine Learning libraries affect computational needs but then assume they don t know the most basic clustering algorithm WhatTo me it feels like a hastily written half white paperhalf wiki article about DSML algorithms Computer Science and how Machine Learning is actually bad for humanityAlso he interviews people who have DSML experience which is a good idea and cool in theory but some of the interviews ust Feel Like Sales Pitches like sales pitches their products Like I haven t used StictchFix and it might be a great product but I will go to their website to learn about it I don t want to pay to read a sales pitchI wish I could return this book but have already highlighted it from from to back Please don t buy this book unless you fall into whatever very niche group this author targeted the book towards Instead buy Hands on Machine "Learning if you want to learn about DSML If you want to know "if you want to learn about DSML If you want to know to deploy your models maybe try Applied Data Science 20 but due to version updates and dependencies I couldn t get it to deploy but the reference on how to build the pipeline is. Ilding a real world ML application step by stepAuthor Emmanuel Ameisen an experienced data scientist who led an AI education program demonstrates practical ML concepts using code snippets illustrations screenshots and interviews with industry leaders Part I teaches you how to plan an ML application and measure success Part II explains how to build a working ML model Part III demonstrates ways. I will start off by saying on a scale of 1 to 10 in data science machine learning knowledge 1 being I barely know what a linear model is and 10 being I contribute to building Machine Learning Libraries conduct research that I am around a 4 I initially bought this book because I have a decent understanding of Data Science created a few models at work and personally and was interested in ways to serve the model via webserver like flaskdjangoThe best analogy I can give about this book is its like going to a give about this book is its like going to a seeing beef stew on the menu and ordering it When it arrives you realize it is ust beef broth and when you complain to the waiter they tell you beef was and when you complain to the waiter they tell you beef was in it but you have to pay extra for the actual beef Hence the title of my reviewChapter after chapter I kept waiting for him to dive into the python scripts and explaining how they build the model In this 250 page book maybe 30 of the pages are dedicated to explaining the model and pipeline with the rest dedicated to superficially explaining DSML conceptsIt doesn t go deep enough for anyone who has an intermediate level of knowledge DSML On the other hand it doesn t explain enough for people who might be beginners For example it ust assumes you understand when to Learn the skills necessary to design build and deploy applications powered by machine learning ML Through the course of this hands on book youll build an example ML driven application from initial idea to deployed product Data scientists software engineers and product managersincluding experienced practitioners and novices alikewill learn the tools best practices and challenges involved in bu. ,


Building Machine Learning Powered Applications: Going from Idea to Product