Book Image

Python Machine Learning Solutions [Video]

By : Prateek Joshi
Book Image

Python Machine Learning Solutions [Video]

By: Prateek Joshi

Overview of this book

<p>Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.</p> <p>With this course, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.</p> <p>You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modelling, data visualization techniques, recommendation engines, and more with the help of real-world examples.</p> <h1>Style and Approach</h1> <p>These independent videos teach you how to perform various machine learning tasks in different environments. Each of the video in the section will cover a real-life scenario.</p>

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Python Machine Learning Solutions [Video]
Prateek Joshi
Oct, 2016 | 4 hours 30 minutes
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Table of Contents (12 chapters)
Free Chapter
1
The Realm of Supervised Learning
The Course Overview
Preprocessing Data Using Different Techniques
Label Encoding
Building a Linear Regressor
Regression Accuracy and Model Persistence
Building a Ridge Regressor
Building a Polynomial Regressor
Estimating housing prices
Computing relative importance of features
Estimating bicycle demand distribution
2
Constructing a Classifier
Building a Simple Classifier
Building a Logistic Regression Classifier
Building a Naive Bayes’ Classifier
Splitting the Dataset for Training and Testing
Evaluating the Accuracy Using Cross-Validation
Visualizing the Confusion Matrix and Extracting the Performance Report
Evaluating Cars based on Their Characteristics
Extracting Validation Curves
Extracting Learning Curves
Extracting the Income Bracket
3
Predictive Modeling
Building a Linear Classifier Using Support Vector Machine
Building Nonlinear Classifier Using SVMs
Tackling Class Imbalance
Extracting Confidence Measurements
Finding Optimal Hyper-Parameters
Building an Event Predictor
Estimating Traffic
4
Clustering with Unsupervised Learning
Clustering Data Using the k-means Algorithm
Compressing an Image Using Vector Quantization
Building a Mean Shift Clustering
Grouping Data Using Agglomerative Clustering
Evaluating the Performance of Clustering Algorithms
Automatically Estimating the Number of Clusters Using DBSCAN
Finding Patterns in Stock Market Data
Building a Customer Segmentation Model
5
Building Recommendation Engines
Building Function Composition for Data Processing
Building Machine Learning Pipelines
Finding the Nearest Neighbors
Constructing a k-nearest Neighbors Classifier
Constructing a k-nearest Neighbors Regressor
Computing the Euclidean Distance Score
Computing the Pearson Correlation Score
Finding Similar Users in a Dataset
Generating Movie Recommendations
6
Analyzing Text Data
Preprocessing Data Using Tokenization
Stemming Text Data
Converting Text to Its Base Form Using Lemmatization
Dividing Text Using Chunking
Building a Bag-of-Words Model
Building a Text Classifier
Identifying the Gender
Analyzing the Sentiment of a Sentence
Identifying Patterns in Text Using Topic Modelling
7
Speech Recognition
Reading and Plotting Audio Data
Transforming Audio Signals into the Frequency Domain
Generating Audio Signals with Custom Parameters
Synthesizing Music
Extracting Frequency Domain Features
Building Hidden Markov Models
Building a Speech Recognizer
8
Dissecting Time Series and Sequential Data
Transforming Data into the Time Series Format
Slicing Time Series Data
Operating on Time Series Data
Extracting Statistics from Time Series
Building Hidden Markov Models for Sequential Data
Building Conditional Random Fields for Sequential Text Data
Analyzing Stock Market Data with Hidden Markov Models
9
Image Content Analysis
Operating on Images Using OpenCV-Python
Detecting Edges
Histogram Equalization
Detecting Corners and SIFT Feature Points
Building a Star Feature Detector
Creating Features Using Visual Codebook and Vector Quantization
Training an Image Classifier Using Extremely Random Forests
Building an object recognizer
10
Biometric Face Recognition
Capturing and Processing Video from a Webcam
Building a Face Detector using Haar Cascades
Building Eye and Nose Detectors
Performing Principal Component Analysis
Performing Kernel Principal Component Analysis
Performing Blind Source Separation
Building a Face Recognizer Using a Local Binary Patterns Histogram
11
Deep Neural Networks
Building a Perceptron
Building a Single-Layer Neural Network
Building a deep neural network
Creating a Vector Quantizer
Building a Recurrent Neural Network for Sequential Data Analysis
Visualizing the Characters in an Optical Character Recognition Database
Building an Optical Character Recognizer Using Neural Networks
12
Visualizing Data
Plotting 3D Scatter plots
Plotting Bubble Plots
Animating Bubble Plots
Drawing Pie Charts
Plotting Date-Formatted Time Series Data
Plotting Histograms
Visualizing Heat Maps
Animating Dynamic Signals
Chapter 6
Analyzing Text Data
Content Locked
Section 5
Building a Bag-of-Words Model
When we deal with text documents that contain millions of words, we need to convert them into some kind of numeric representation so as to make them usable for machine learning algorithms. A bag-of- words model is what helps us achieve this task quite easily. - Divide the text data into chunks - Extract a document term matrix - Extract vocabulary from the vectorizer object
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