There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job. This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow. Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module Introduction
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Destrezas que aprenderás
Deep Learning, Artificial Neural Network, Decision Tree, Support Vector Machine (SVM), Machine Learning (ML) Algorithms
De la lección
Build Convolutional and Recurrent Neural Networks (CNN/RNN)
Impartido por:
Stacey McBrine
CDSP, CAIP, CIoTP, CIoTSP, CFR, CISSP, SSCP, CASP, CFR, CEI, CEH, ECSA, CHFI, CCNA, CCSI, CTT+, LINUX+, PENTEST+, SECURITY+, A+, SCNP, ITIL Foundations, ITIL SO, ITIL OSA, MCSA, MCITP
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