Build Decision Trees, SVMs, and Artificial Neural Networks
- Offered byCoursera
Build Decision Trees, SVMs, and Artificial Neural Networks at Coursera Overview
Duration | 22 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Build Decision Trees, SVMs, and Artificial Neural Networks at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 5 in the CertNexus Certified Artificial Intelligence Practitioner
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
- Approx. 22 hours to complete
- English Subtitles: English
Build Decision Trees, SVMs, and Artificial Neural Networks at Coursera Course details
- 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 Decision Trees, SVMs, and Artificial Neural Networks at Coursera Curriculum
Build Decision Trees and Random Forests
Build Decision Trees, SVMs, and Artificial Neural Networks Course Introduction
CAIP Specialization Introduction
Build Decision Trees and Random Forests Module Introduction
Decision Tree
Classification and Regression Tree (CART)
Gini Index Example
CART Hyperparameters
Pruning
C4.5
Bin Determination
One-Hot Encoding
Decision Trees Compared to Other Algorithms
Ensemble Learning
Random Forest
Random Forest Hyperparameters
Feature Selection Benefits
Overview
Decision Tree Algorithm Comparison
Guidelines for Building a Decision Tree Model
Guidelines for Building a Random Forest Model
Building Decision Trees and Random Forests
Build Support-Vector Machines (SVM)
Build Support-Vector Machines (SVM) Module Introduction
Support-Vector Machines (SVMs)
SVMs for Linear Classification
Hard-Margin and Soft-Margin Classification
SVMs for Non-Linear Classification
Kernel Trick
Kernel Methods
SVMs for Regression
Overview
Guidelines for Building SVM Models for Classification
Guidelines for Building SVM Models for Regression
Building SVMs
Build Multi-Layer Perceptrons (MLP)
Build Multi-Layer Perceptrons (MLP) Module Introduction
Artificial Neural Network (ANN)
Perceptron
Perceptron Training
Multi-Layer Perceptron (MLP)
ANN Layers
Backpropagation
Activation Functions
Overview
Guidelines for Building MLPs
Building MLPs
Build Convolutional and Recurrent Neural Networks (CNN/RNN)
Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module Introduction
Convolutional Neural Network (CNN)
CNN Filters
Padding and Stride
CNN Architecture
Generative Adversarial Network (GAN)
Recurrent Neural Network (RNN)
Memory Cell
RNN Training
Long Short-Term Memory (LSTM) Cell
Embedding
Overview
Guidelines for Building CNNs
Guidelines for Building RNNs
Building CNNs and RNNs
Apply What You've Learned
Build Decision Trees, SVMs, and Artificial Neural Networks at Coursera Admission Process
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