Packt Machine Learning for OpenCV Supervised Learning-XQZT

Packt Machine Learning for OpenCV Supervised Learning-XQZT
English | Size: 1.01 GB
Category: Tutorial

Computer vision is one of today’s most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains.

This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks.
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Data Science Supervised Machine Learning in Python

Data Science Supervised Machine Learning in Python
English | Size: 464.44 MB
Category: Programming | E-learning

What Will I Learn?
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
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Packt Publishing – Supervised and Unsupervised Learning with Python

Packt Publishing – Supervised and Unsupervised Learning with Python
English | Size: 407.28 MB
Category: Tutorial

This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers. [Read more…]

O’Reilly – Supervised Classification Algorithms

O’Reilly – Supervised Classification Algorithms
English | Size: 497.49 MB
Category: Tutorial

Classification is the sub-field of machine learning encountered more frequently than any other in data science applications. There are many different classification techniques and this course explains some of the most important ones, including algorithms such as logistic regression, k-nearest neighbors (k-NN), decision trees, ensemble models like random forests, and support vector machines. The course also covers Naive Bayes classifiers and in so doing, covers Bayes’ theorem and basic Bayesian inference, both of which are widely used in training many machine learning algorithms. A basic knowledge of algebra is required. A solid understanding of differential calculus will be necessary for logistic regression, Support Vector Machines and Bayesian Inference. [Read more…]

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