Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
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이 강좌에 대하여
General knowledge of AI is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
배울 내용
Collect and prepare a dataset to use for training and testing a machine learning model.
Analyze a dataset to gain insights.
Set up and train a machine learning model as needed to meet business requirements.
Communicate the findings of a machine learning project back to the organization.
귀하가 습득할 기술
- Artificial Intelligence (AI)
- Machine Learning
- Data Analysis
- Modeling
- Process Management
General knowledge of AI is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
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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
Collect the Dataset
The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
Analyze the Dataset
You've formulated a machine learning problem, and have identified a potential dataset to use. Now you'll analyze the dataset to develop ideas on how to make the best use of the information it contains as you prepare to create your initial machine learning model.
Prepare the Dataset
Before a dataset can be used with a machine learning model, there are typically various tasks you need to perform to ensure that data is an optimal state. In this module, you'll use various methods to prepare the data.
Set Up and Train a Model
To set up a machine learning model in an environment like Python, you must determine the algorithm that will produce the results you're after, and then use it to create a model based on your training data. After the initial setup, it may take multiple tests and refinements to produce a model that meets your requirements.
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.

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