Build Decision Trees, SVMs, and Artificial Neural Networks
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Course Summary
Learn how to build decision trees, support vector machines, and neural networks in this course. Gain the skills needed to analyze complex datasets and make informed decisions.Key Learning Points
- Understand the basics of machine learning algorithms
- Learn how to build decision trees, support vector machines, and neural networks
- Gain practical experience in analyzing complex datasets
Job Positions & Salaries of people who have taken this course might have
- Data Scientist
- USA: $117,345
- India: ₹849,818
- Spain: €41,634
- Machine Learning Engineer
- USA: $112,558
- India: ₹1,358,342
- Spain: €35,126
- Data Analyst
- USA: $62,453
- India: ₹412,420
- Spain: €24,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of machine learning algorithms
- Be able to build decision trees, support vector machines, and neural networks
- Gain practical experience analyzing complex datasets
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge in Python
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Applied Data Science with Python
- Introduction to Machine Learning
Related Education Paths
Related Books
Description
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.
Knowledge
- 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.
Outline
- 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
Summary of User Reviews
This course on building decision trees, SVMs, and neural networks has received positive reviews from many users. The course covers advanced topics in machine learning and is taught by expert instructors. One key aspect that many users appreciated was the hands-on approach to learning, which helped them to apply the concepts they learned. However, some users found the course to be too complex and challenging, and others felt that the material could be more organized and easier to follow.Key Aspect Users Liked About This Course
Hands-on approach to learningPros from User Reviews
- Expert instructors
- Covers advanced topics in machine learning
- Provides practical experience with building decision trees, SVMs, and neural networks
- Offers valuable insights into the latest trends and developments in the field
- Includes helpful resources and tools for further learning and practice
Cons from User Reviews
- Course material can be too complex and challenging for some users
- Some users found the material to be disorganized and difficult to follow
- The course may not be suitable for beginners or those without a strong background in machine learning
- The pace of the course can be fast and demanding
- Some users would have appreciated more guidance and support from the instructors
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