![Python Machine Learning Solutions [Video]](https://static.packt-cdn.com/products/9781787127692/cover/smaller)
Python Machine Learning Solutions [Video]
4.8
(4 reviews total)
- FREE Subscribe Access now
- $99.99 Video Buy
- Instant online access to over 7,500+ books and videos
- Constantly updated with 100+ new titles each month
- Breadth and depth in over 1,000+ technologies
-
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
-
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
-
Predictive Modeling
-
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
-
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
-
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
-
Speech Recognition
-
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
-
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
-
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
-
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
-
Visualizing Data
About this video
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.
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.
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.
Style and Approach
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.
- Publication date:
- October 2016
- Publisher
- Packt
- Duration
- 4 hours 30 minutes
- ISBN
- 9781787127692
Download code from GitHub
Latest Reviews
(4 reviews total)The presentation is clear and concise. Focusses on the programming aspects. Author occasionally uses a functional programming style, which I like.
4
Good quality, at a reasonable price.
5
Excellent
5