English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 35m | 1.09 GB
Analyze and understand your data with the power and simplicity of Python
OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine learning concepts.
The video will teach you how to work with the various OpenCV modules for statistical modelling and machine learning. You will start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to implement them with the help of real-world examples. The course will also show you how you can implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.
What You Will Learn
- Master Logistic Regression and regularization techniques
- Understand supervised and unsupervised machine learning algorithms
- Implement OpenCV’s functionalities in machine learning algorithms
- Solve image segmentation problem using K-Means Clustering
- Get to know the key elements of a neural network and deep learning and its ability to learn
- Load models trained with popular deep learning libraries such as Caffe
Table of Contents
Introduction to Machine Learning
The Basics of Machine Learning
The Course Overview
Extracting Features and Preparing the Data
Creating Training Data and Extracting Information
Extracting Features
Classifier and Regression
K-Nearest Neighbors
Logistic Regression
Normal Bayes Classifier
Decision Trees and Support Vector Machines
Decision Trees
Support Vector Machines
Neural Networks
Artificial Neural Networks
Unsupervised Learning and Introduction to Deep Learning
Deep Learning
Unsupervised Learning
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