Getting Started with Machine Learning with R
Getting Started with Machine Learning with R
Analyze and understand your data with the power and simplicity of Python
About This Machine Learning with Open CV and Python Video course
- Prepare your data for analysis and use it to implement regression, clustering, and more
- Put to use the machine learning modules offered by OpenCV
- Build superior machine learning models with the help of OpenCV using this easy-to-follow course
Machine Learning with Open CV and Python In Detail
OpenCV is a library of programming functions mainly aimed at real-time computer vision. This video 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 will you learn in this course?
Course plan
Chapter 1
Introduction to Machine Learning
Introduction to Machine Learning
Chapter 2
Extracting Features and Preparing the Data
Extracting Features and Preparing the Data
Chapter 3
Classifier and Regression
Classifier and Regression
Chapter 4
Decision Trees and Support Vector Machines
Decision Trees and Support Vector Machines
Chapter 5
Neural Networks
Neural Networks
Chapter 6
Unsupervised Learning and Introduction to Deep Learning
Unsupervised Learning and Introduction to Deep Learning
Detailed course plan
Chapter 1 : Introduction to Machine Learning
09m10
Lesson 1The Course Overview
Lesson 2The Basics of Machine Learning
Chapter 2 : Extracting Features and Preparing the Data
10m25
Lesson 1Creating Training Data and Extracting Information
Lesson 2Extracting Features
Chapter 3 : Classifier and Regression
21m07
Lesson 1K-Nearest Neighbors
Lesson 2Logistic Regression
Lesson 3Normal Bayes Classifier
Chapter 4 : Decision Trees and Support Vector Machines
22m15
Lesson 1Decision Trees
Lesson 2Support Vector Machines
Chapter 5 : Neural Networks
12m30
Lesson 1Artificial Neural Networks
Chapter 6 : Unsupervised Learning and Introduction to Deep Learning
20m07
Lesson 1Unsupervised Learning
Lesson 2Deep Learning
