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 course is part of the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
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About this Course
ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
What you will learn
Train and evaluate decision trees and random forests for regression and classification.
Train and evaluate support-vector machines (SVM) for regression and classification.
Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.
Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.
Skills you will gain
- Deep Learning
- Artificial Neural Network
- Decision Tree
- Support Vector Machine (SVM)
- Machine Learning (ML) Algorithms
ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
Offered by
Syllabus - What you will learn from this course
Build Decision Trees and Random Forests
Build Support-Vector Machines (SVM)
Build Multi-Layer Perceptrons (MLP)
Build Convolutional and Recurrent Neural Networks (CNN/RNN)
About the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

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