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.
제공자:


이 강좌에 대하여
ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
배울 내용
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.
귀하가 습득할 기술
- 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.
제공자:

CertNexus
CertNexus is a vendor-neutral certification body, providing emerging technology certifications and micro-credentials for Business, Data, Development, IT, and Security professionals. CertNexus’ exams meet the most rigorous development standards possible which outlines a global framework for developing personnel certification programs to narrow the widening skills gap.
강의 계획표 - 이 강좌에서 배울 내용
Build Decision Trees and Random Forests
You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.
Build Support-Vector Machines (SVM)
Another alternative approach to regression and classification comes in the form of support-vector machines (SVMs). In this module, you'll build SVMs that can do a good job of handling outliers and tackling high-dimensional data in an efficient manner.
Build Multi-Layer Perceptrons (MLP)
All of the algorithms discussed thus far fall under the general umbrella of machine learning. While they are powerful and complex in their own right, the algorithms that make up the subdomain of deep learning—called artificial neural networks (ANNs)—are even more so. In this module, you'll build a fundamental version of an ANN called a multi-layer perceptron (MLP) that can tackle the same basic types of tasks (regression, classification, etc.), while being better suited to solving more complicated and data-rich problems.
Build Convolutional and Recurrent Neural Networks (CNN/RNN)
Now that you've built MLP neural networks, you can incorporate them into two wider architectures: convolutional neural networks (CNNs), which excel at solving computer vision problems; and recurrent neural networks (RNNs), which are most often used to process natural languages.
CertNexus 인증 인공 지능 전문가 전문 자격증 정보
The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP.

자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
이 수료 과정을 구독하면 무엇을 이용할 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.