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Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn
Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn: Build effective models in scikit-learn with TensorFlow 2.0

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Profile Icon Samuel Holt
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$9.99 $137.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Video Jun 2020 10hrs 28mins 1st Edition
Video
$9.99 $137.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Samuel Holt
Arrow right icon
$9.99 $137.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Video Jun 2020 10hrs 28mins 1st Edition
Video
$9.99 $137.99
Subscription
Free Trial
Renews at $19.99p/m
Video
$9.99 $137.99
Subscription
Free Trial
Renews at $19.99p/m

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Key benefits

  • Embark on your ML journey using the best machine learning practices and the powerful features of TensorFlow 2.0 and scikit-learn
  • Learn to work with unstructured data, images, and noisy text input, and implement the latest Natural Language Processing models and methods
  • Explore supervised and unsupervised algorithms and put them into practice using mini implementation projects as a basis for real-world applications

Description

Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques? If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data. The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task. By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits). The code bundle for this course is available at https://github.com/PacktPublishing/Practical-Machine-Learning-with-TensorFlow-2.0-and-Scikit-Learn

Who is this book for?

This course is for developers who are familiar with pandas and NumPy concepts and are keen to develop their machine learning methodologies and practices effectively using scikit-learn and TensorFlow 2.0. Requirement:Prior Python programming knowledge is mandatory for this course.

What you will learn

  • Fundamentals of machine learning (and introducing the benefits of scikit-learn)
  • Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning in scikit-learn
  • How to identify a problem, select the right model, and optimize it to get the best desired outcome: insights into data
  • TensorFlow 2.0 for deep learning with neural networks
  • Deep learning and image-classification examples, and time series predictive model examples
  • Reinforcement learning, and how to implement various types with examples
  • Effectively use scikit-learn and TensorFlow in your production system, including framing a task in each task example

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2020
Length: 10hrs 28mins
Edition : 1st
Language : English
ISBN-13 : 9781789959161
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Google
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Languages :

What do you get with a video?

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Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details

Publication date : Jun 30, 2020
Length: 10hrs 28mins
Edition : 1st
Language : English
ISBN-13 : 9781789959161
Vendor :
Google
Category :
Languages :

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Table of Contents

9 Chapters
Installing Scikit-Learn and TensorFlow 2.0 Chevron down icon Chevron up icon
ML Fundamentals: Scikit-Learn Introduction Chevron down icon Chevron up icon
Applied Scikit-Learn: Supervised Learning Models Chevron down icon Chevron up icon
Unsupervised Learning Chevron down icon Chevron up icon
TensorFlow 2.0 Essentials for ML Chevron down icon Chevron up icon
Applied Deep Learning for Computer Vision Tasks Chevron down icon Chevron up icon
Natural Language Processing and Sequential Data Chevron down icon Chevron up icon
Applied Sequence to Sequence and Transformer Models Chevron down icon Chevron up icon
Working with Reinforcement Learning Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 0%
1 star 50%
Qiusheng Xu Feb 04, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book for machine learning.
Feefo Verified review Feefo
Eric Dec 19, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
What a total waste of money. I was hoping an expert was going to explain how to actually use these tools and give some novel examples. If you are just going to have somebody read the scikit-learn help pages, you could at least hire a professional reader.
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