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Learn everything you need to know about Machine learning with Tensorflow and Scikit-Learn
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
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.
What You Will Learn
- 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
Table of Contents
Linear Regression and Its Many Applications
1 The Course Overview
2 Understanding Linear Regression
3 Estimating the Price of Housing
4 Ad Ranking Using Clickthrough Rates and User Demographics
5 Building a Full Ad Ranking System
Classification Problems with SVMs, Decision Trees, and Random Forest Methods
6 Understanding Support Vector Machines
7 Classification of Movie Genres with SVMs
8 Working with Decision Trees
9 Wine Classification with Decision Trees
10 Exploring Random Forest Methods
11 Credit Card Fraud Detection with Random Forests
Applications in Unsupervised Learning
12 Introduction to Unsupervised Learning
13 K-Means Clustering Explained
14 Unsupervised Clustering of Patients with K-Means Clustering
15 Dimensionality Reduction with Principal Component Analysis
16 Using PCA to Compress Images
Applications in Natural Language Processing
17 Essential Feature Extraction – Bag of Words and N-Grams
18 Tweet Classification with Bag of Words Features
19 Building a Tweet-Bot with N-Gram Features
20 Working with Latent Dirichlet Allocation (LDA)
21 LDA for Natural Language Topic Discovery
Convolutional Neural Networks (CNNs) and Computer Vision
22 Deep Neural Networks and Convolutional Neural Networks
23 Building a Flower Species Classifier with CNN’s with TensorFlow + Keras
24 Semantic Image Segmentation Explained
25 Image Segmentation with CNNs and TensorFlow
Sequence Modelling with Recurrent Neural Networks
26 Understanding Recurrent Neural Networks
27 Working with Long-Short Term Memory Networks (LSTMs)
28 Better Tweet Sentiment Classification with RNNs
29 Build a Cryptocurrency Prediction Bot with RNNs
Applications with Transfer Learning and Deep Embeddings
30 Understanding Word2Vec, Representation Learning, and Embeddings
31 Applying Word2Vec for Analogy Completion
32 Pretrained ImageNet Embeddings and Image Search Engines
33 Build an Image Retrieval System Using Embeddings
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