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Machine Learning with scikit-learn and Tensorflow

Video Description

Learn everything you need to know about Machine learning with Tensorflow and Scikit-Learn

About This Video

  • 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

In Detail

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-

Table of Contents

  1. Chapter 1 : Linear Regression and Its Many Applications
    1. The Course Overview 00:03:59
    2. Understanding Linear Regression 00:10:09
    3. Estimating the Price of Housing 00:10:31
    4. Ad Ranking Using Clickthrough Rates and User Demographics 00:08:34
    5. Building a Full Ad Ranking System 00:08:16
  2. Chapter 2 : Classification Problems with SVMs, Decision Trees, and Random Forest Methods
    1. Understanding Support Vector Machines 00:07:53
    2. Classification of Movie Genres with SVMs 00:09:36
    3. Working with Decision Trees 00:05:18
    4. Wine Classification with Decision Trees 00:06:50
    5. Exploring Random Forest Methods 00:05:23
    6. Credit Card Fraud Detection with Random Forests 00:11:26
  3. Chapter 3 : Applications in Unsupervised Learning
    1. Introduction to Unsupervised Learning 00:03:33
    2. K-Means Clustering Explained 00:06:12
    3. Unsupervised Clustering of Patients with K-Means Clustering 00:09:54
    4. Dimensionality Reduction with Principal Component Analysis 00:08:33
    5. Using PCA to Compress Images 00:08:15
  4. Chapter 4 : Applications in Natural Language Processing
    1. Essential Feature Extraction – Bag of Words and N-Grams 00:05:23
    2. Tweet Classification with Bag of Words Features 00:09:14
    3. Building a Tweet-Bot with N-Gram Features 00:11:17
    4. Working with Latent Dirichlet Allocation (LDA) 00:06:47
    5. LDA for Natural Language Topic Discovery 00:08:42
  5. Chapter 5 : Convolutional Neural Networks (CNNs) and Computer Vision
    1. Deep Neural Networks and Convolutional Neural Networks 00:05:02
    2. Building a Flower Species Classifier with CNN’s with TensorFlow + Keras 00:05:39
    3. Semantic Image Segmentation Explained 00:05:20
    4. Image Segmentation with CNNs and TensorFlow 00:08:57
  6. Chapter 6 : Sequence Modelling with Recurrent Neural Networks
    1. Understanding Recurrent Neural Networks 00:05:14
    2. Working with Long-Short Term Memory Networks (LSTMs) 00:04:46
    3. Better Tweet Sentiment Classification with RNNs 00:08:49
    4. Build a Cryptocurrency Prediction Bot with RNNs 00:06:04
  7. Chapter 7 : Applications with Transfer Learning and Deep Embeddings
    1. Understanding Word2Vec, Representation Learning, and Embeddings 00:05:25
    2. Applying Word2Vec for Analogy Completion 00:06:47
    3. Pretrained ImageNet Embeddings and Image Search Engines 00:04:00
    4. Build an Image Retrieval System Using Embeddings 00:06:08