Enhance your machine learning expertise with the "Build Decision Trees, SVMs, and Artificial Neural Networks" course. Delve into the nuances of decision trees, support-vector machines (SVMs), and artificial neural networks (ANNs) to strengthen your problem-solving capabilities. This comprehensive program, offered by CertNexus, equips you with the knowledge to deploy regression, classification, computer vision, and natural language processing solutions.
Throughout the course, explore the intricacies of decision trees and random forests, understand the principles of SVMs for linear and non-linear classification, and harness the power of multi-layer perceptrons (MLPs) and convolutional and recurrent neural networks (CNNs/RNNs) for advanced applications. Learn to train, evaluate, and deploy these algorithms effectively, providing you with the expertise to select the most suitable model for diverse business challenges.
Equip yourself with the valuable skills needed to become a Certified Artificial Intelligence Practitioner (CAIP) and elevate your career in the rapidly evolving field of artificial intelligence and machine learning.
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This course offers comprehensive training in decision trees, support-vector machines, artificial neural networks, and advanced neural network architectures such as CNNs and RNNs, encompassing regression, classification, computer vision, and natural language processing techniques.
Discover the fundamental concepts of decision trees, including the principles of classification and regression trees (CART), Gini Index, and ensemble learning. Gain insights into building and refining decision trees and random forests, and learn to compare these algorithms with others for effective model selection.
Immerse yourself in the intricacies of support-vector machines (SVMs) for linear and non-linear classification, as well as regression tasks. Explore the nuances of hard-margin and soft-margin classification, kernel methods, and effectively build SVM models for diverse applications.
Delve into the world of artificial neural networks (ANNs), from the basic perceptron to multi-layer perceptrons (MLPs). Understand the principles of backpropagation and activation functions, and learn to build and train MLPs for regression and classification tasks.
Embark on an exploration of advanced neural network architectures with a focus on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Master the principles of CNN filters, padding, stride, as well as the applications of RNNs in natural language processing. Learn to build and train CNNs and RNNs for diverse tasks.
This module involves applying the knowledge gained from the course to real-world scenarios, integrating the skills acquired in building decision trees, support-vector machines, and artificial neural networks into practical business problem-solving models.
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