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Machine Learning with scikit-learn and Tensorflow [Video]

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  • Work through detailed tutorials of projects such as ad ranking, sentiment classification, image retrieval, and threat detection.
  • Use the most powerful and ubiquitous Machine Learning techniques
  • Implement the cutting-edge methods of Machine Learning including recent advancements in Deep Learning
  • Dissect any machine learning research paper into actionable insights
  • Develop a playbook for determining the best approach to any machine learning problem
  • Use TensorFlow to build deep learning models
  • Implement Convolutional Neural Networks for Computer Vision
  • Build Recurrent Neural Networks for applications involving sequenced data such as natural language and stock prediction
  • Segment images using computer vision
  • Build a stock price prediction with recurrent neural networks
  • Apply autoencoders for image denoising
  • Work with Generative Adversarial Networks to enhance blurry photos

Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks.

We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification. The course will then take you through the methods for unsupervised learning and what to do when you have limited or no labels for your data. We use the techniques we have learned, along with some new ones, to build a sentiment classifier, an autocomplete keyboard and a topic discoverer.

The course will also cover applications for Natural Language Processing, explaining the types of language processing. We will cover TensorFlow, the most popular deep learning framework, and use it to build convolutional neural networks for object recognition and segmentation. We will then discuss recurrent neural networks and build applications for sentiment classification and stock prediction. We will then show you how to process sequences of data with recurrent neural networks with applications in sentiment classification and stock price prediction. Finally, you will learn applications with deep unsupervised learning and generative models. By the end of the course, you will have mastered Machine Learning in your everyday tasks

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Machine-learning-with-Sci-kit-Learn-and-Tensorflow-V-

Style and Approach

A practical course packed with step-by-step instructions, working examples, and helpful advice. This course will teach you everything about Tensorflow and Scikit-Learn. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

  • A comprehensive but fast and friendly guide to using Machine Learning with Scikit-Learn and Tensorflow.
  • Get insights into essential concepts, from machine learning algorithms to deep neural networks
  • Real-world professional projects that are a perfect blend of Machine Learning theory and implementation details
Course Length 3 hours 58 minutes
Date Of Publication 28 Mar 2018
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Nick Locascio

Nick Locascio is a deep learning consultant, writer, researcher, and entrepreneur. Nick attended MIT, where he obtained his B.S. and MEng conducting research in NLP and Computer Vision. He has worked on projects ranging from training neural networks that generate code, to collaborating with the MGH Radiology department to apply deep learning to assist in clinical screening mammography. Nick's work has been featured on MIT News and CNBC. Nick does private deep learning consulting for Fortune-500 enterprise companies. He also co-founded the landmark MIT course 6.S191, “Intro to Deep Learning,” which he taught to an audience of 300 students, post-docs, and professors. Nick’s writing experience includes credits as a contributing author to Fundamentals of Deep Learning.