Build Decision Trees, SVMs, and Artificial Neural Networks
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 22 hours
- Course by CertNexus
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Offered by
About
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.Modules
Overview
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3
Videos
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2
Readings
3
Videos
- Build Decision Trees, SVMs, and Artificial Neural Networks Course Introduction
- CAIP Specialization Introduction
- Build Decision Trees and Random Forests Module Introduction
2
Readings
- Overview
- Get help and meet other learners. Join your Community!
Build Decision Tree Models
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9
Videos
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2
Readings
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1
Lab
1
Labs
- Building a Decision Tree Model
9
Videos
- 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
2
Readings
- Decision Tree Algorithm Comparison
- Guidelines for Building a Decision Tree Model
Build Random Forest Models
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4
Videos
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1
Readings
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1
Lab
1
Labs
- Building a Random Forest Model
4
Videos
- Ensemble Learning
- Random Forest
- Random Forest Hyperparameters
- Feature Selection Benefits
1
Readings
- Guidelines for Building a Random Forest Model
Evaluate What You've Learned
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1
Discussion
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1
Assignment
1
Assignment
- Building Decision Trees and Random Forests
1
Discussions
- Reflect on What You've Learned
Overview
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1
Videos
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1
Readings
1
Videos
- Build Support-Vector Machines (SVM) Module Introduction
1
Readings
- Overview
Build SVM Models for Classification
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6
Videos
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1
Readings
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1
Lab
1
Labs
- Building an SVM Model for Classification
6
Videos
- Support-Vector Machines (SVMs)
- SVMs for Linear Classification
- Hard-Margin and Soft-Margin Classification
- SVMs for Non-Linear Classification
- Kernel Trick
- Kernel Methods
1
Readings
- Guidelines for Building SVM Models for Classification
Build SVM Models for Regression
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1
Videos
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1
Readings
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1
Lab
1
Labs
- Building an SVM Model for Regression
1
Videos
- SVMs for Regression
1
Readings
- Guidelines for Building SVM Models for Regression
Evaluate What You've Learned
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1
Discussion
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1
Assignment
1
Assignment
- Building SVMs
1
Discussions
- Reflect on What You've Learned
Overview
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1
Videos
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1
Readings
1
Videos
- Build Multi-Layer Perceptrons (MLP) Module Introduction
1
Readings
- Overview
ANNs and MLPs
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7
Videos
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1
Readings
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1
Lab
1
Labs
- Building an MLP
7
Videos
- Artificial Neural Network (ANN)
- Perceptron
- Perceptron Training
- Multi-Layer Perceptron (MLP)
- ANN Layers
- Backpropagation
- Activation Functions
1
Readings
- Guidelines for Building MLPs
Evaluate What You've Learned
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1
Discussion
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1
Assignment
1
Assignment
- Building MLPs
1
Discussions
- Reflect on What You've Learned
Overview
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1
Videos
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1
Readings
1
Videos
- Build Convolutional and Recurrent Neural Networks (CNN/RNN) Module Introduction
1
Readings
- Overview
Build CNNs
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5
Videos
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1
Readings
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1
Lab
1
Labs
- Building a CNN
5
Videos
- Convolutional Neural Network (CNN)
- CNN Filters
- Padding and Stride
- CNN Architecture
- Generative Adversarial Network (GAN)
1
Readings
- Guidelines for Building CNNs
Build RNNs
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5
Videos
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1
Readings
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1
Lab
1
Labs
- Building an RNN
5
Videos
- Recurrent Neural Network (RNN)
- Memory Cell
- RNN Training
- Long Short-Term Memory (LSTM) Cell
- Embedding
1
Readings
- Guidelines for Building RNNs
Evaluate What You've Learned
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1
Discussion
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1
Assignment
1
Assignment
- Building CNNs and RNNs
1
Discussions
- Reflect on What You've Learned
Project
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1
PeerReview
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1
Lab
1
Peer Review
- Building a CNN to Classify Handwritten Characters
1
Labs
- Course 4 Project
Auto Summary
Enhance your data science and AI skills with "Build Decision Trees, SVMs, and Artificial Neural Networks" on Coursera. This professional-level course delves into advanced machine learning and deep learning algorithms, focusing on decision trees, SVMs, and ANNs. Ideal for learners aiming to master diverse modeling techniques, the course spans 1320 minutes and is part of the Certified Artificial Intelligence Practitioner (CAIP) certification. Subscription options include Starter and Professional tiers. Perfect for professionals eager to solve complex business problems with cutting-edge AI tools.
Stacey McBrine