Es to xplain the K Nearest Neighbors algorithm Like you are xpecting the reader to understand 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 feels like a hastily written half paperhalf wiki article about DSML algorithms Computer Science and how Machine Learning is actually bad for humanityAlso he interviews people who have DSML xperience which is a good idea and cool in theory but some of the interviews just feel like sales pitches for 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 how 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 usefulTo me this book felt like a lot of bad Medium or Towards Data Science articles stacked on top of The Bookshop on the Shore each other This book is NOT an overly. Ilding a real world ML application step by stepAuthor Emmanuel Ameisen anxperienced data scientist who led an AI ducation program demonstrates practical ML concepts using code snippets illustrations screenshots and interviews with industry leaders led an AI ducation program demonstrates practical ML concepts using code snippets illustrations screenshots and interviews with industry leaders I teaches you how to plan an ML application and measure success Part II xplains 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 a scale of 1 to 10 in data science machine learning knowledge 1 being I barely 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 restaurant seeing beef stew on the menu and ordering it When it arrives you realize it is just beef broth and when you complain to the waiter they tell you beef was stewed in it but you have to pay xtra for the actual beef Hence the title of my reviewChapter after chapter I kept waiting for him to dive into the python scripts and The Day Christ Was Born: The True Account of the First 24 Hours of Jesus's Life explaining how they build the model In this 250 page book maybe 30 of the pages are dedicated toxplaining the model and pipeline with the rest dedicated to superficially xplaining DSML conceptsIt doesn t go deep nough for anyone who has an intermediate of knowledge DSML On the other hand it doesn
t xplain nough for people who might beginners For xample it just assumes you explain nough for people who might be beginners For Integrity Restored: Helping Catholic Families Win the Battle Against Pornography example it just assumes you when to apply XGBoost versus using Scikit Learn But then on the next page it tri. 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 anxample ML driven application from initial idea to deployed product Data scientists software The Taste of Night (Signs of the Zodiac, engineers and product managersincludingxperienced practitioners and novices alikewill learn the tools best practices and challenges involved in bu.
review Building Machine Learning Powered Applications: Going from Idea to ProductTechnical 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 ngineer He formats these lessons in such a way that makes the book xtremely asy 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 I don t think the author has built a machine learning powered application This book is xtremely lightweight at a little over 200 pages and is Too High Level To high level to any practicality The content is just an odd assortment of stuff with bizarre sidebars on transfer learning and code snippets with no cohesiveness The chapter on deployment is The Road From Home: The Story Of An Armenian Girl exactly ten pages long and is a big nothing burger I don tven recommend this book for a beginner because it will confuse them In the jungle 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 datastart to finish A must read to any working data or data ngineer out there Can
recommend it I got book today Surprised to see the uality of the book No color picture and pages look like photocopy with poor uality ink uite disappointed as not getting motivation to start readingBe careful before you order. To improve the model until it fulfills your original vision Part IV covers deployment and monitoring strategiesThis book will help youDefine your product goal and set up a machine learning problemBuild your first nd to nd pipeline uickly and acuire an initial datasetTrain and valuate your ML models and address performance bottlenecksDeploy and monitor your models in a production nvironme. ,t recommend it