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I'm currently self studying machine learning using the book. Is anybody else studying this book and interested in being part of a remote study group to complete it?

13th October, 2021
Last date to join: 31st October, 2021

I find that the problem with self studying is that unlike studying through a university or online bootcamp there is nobody to ask for help when you're stuck and thus studying becomes slower because a lot of time is wasted when you're not able to get quick and effective help online which can also result in feeling demotivated at times. I'm currently at the beginning of this book and I'd like to complete it as quick as possible with a few study partners if I can find any. I also got my hands on "Mastering OpenCV 4 with Python" if anyone is doing that. I think it would help to find other people who'd be interested to be part of a remote machine learning study group and maybe at the end build two or so projects (outside of the book) or MVP's together to be able to have competitive portfolios of work when trying to establish our careers in the AI industry. I think that working on projects together at the end of studying this book can also show potential employers that we can work well in remote and collaborative environments or maybe if we're lucky and build something that can get a good number of users we could even get funded for a startup for one of the products we build together. So far the group has two people (including myself) and we're at the beginning of our first ML project in chapter 2. Please feel free to send me a DM if you'd be interested to complete this book (or any other similar book) with me as quickly and efficiently as we can so that we can find our feet in this very exciting industry! If you see this late also feel free to contact me. I may be able to help if I have any success. I'm looking forward to hearing from you. : )

Advanced-beginner English

Description

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use Scikit-Learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition

Start Learning
Books
O'Reilly Media
Exclusively Paid
24h 18m
No Certificate
848 pages

I'm currently self studying machine learning using the book. Is anybody else studying this book and interested in being part of a remote study group to complete it?

13th October, 2021
Last date to join: 31st October, 2021
Start Learning
Affiliate notice

I find that the problem with self studying is that unlike studying through a university or online bootcamp there is nobody to ask for help when you're stuck and thus studying becomes slower because a lot of time is wasted when you're not able to get quick and effective help online which can also result in feeling demotivated at times. I'm currently at the beginning of this book and I'd like to complete it as quick as possible with a few study partners if I can find any. I also got my hands on "Mastering OpenCV 4 with Python" if anyone is doing that. I think it would help to find other people who'd be interested to be part of a remote machine learning study group and maybe at the end build two or so projects (outside of the book) or MVP's together to be able to have competitive portfolios of work when trying to establish our careers in the AI industry. I think that working on projects together at the end of studying this book can also show potential employers that we can work well in remote and collaborative environments or maybe if we're lucky and build something that can get a good number of users we could even get funded for a startup for one of the products we build together. So far the group has two people (including myself) and we're at the beginning of our first ML project in chapter 2. Please feel free to send me a DM if you'd be interested to complete this book (or any other similar book) with me as quickly and efficiently as we can so that we can find our feet in this very exciting industry! If you see this late also feel free to contact me. I may be able to help if I have any success. I'm looking forward to hearing from you. : )

Advanced-beginner English

  • Type
    Books
  • Provider
    O'Reilly Media
  • Pricing
    Exclusively Paid
  • Duration
    24h 18m
  • Certificate
    No Certificate
  • Pages
    848 pages

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use Scikit-Learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets

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31st October, 2021

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