
Certified Artificial Intelligence Practitioner
Intensive instructor-led course with study materials, tutor support and certification exam included.
Opportunities Workshop
Summary
Flexible payment options available. Spread tuition fees payments over 3 to 12 months.
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- CertNexus x Certified Artificial Intelligence Practitioner - Free
- Exam(s) / assessment(s) is included in price
- Tutor is available to students
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Overview
A Certified Artificial Intelligence Practitionerâ„¢ (CAIP) programme is designed to equip data professionals with the skills to implement the power of AI and machine learning to solve business challenges using various modelling techniques.
CAIPs can utilise AI to automate processes, reduce costs, drive down completion times, and perform operational tasks that allow humans to perform higher level work. They have advanced knowledge of the engineering features of a dataset to prepare it for use in a machine learning model, the ability to select algorithms and perform model training and model handoff, and an understanding of the ethics and oversight required to create ethical outcomes with AI.
Certified AI Practitioners enable organizations to enhance customer experiences and propel innovation to achieve their AI goals.
Certificates
CertNexus x Certified Artificial Intelligence Practitioner
Digital certificate - Included
Resources
- CAIP - Programme Overview -
Description
Programme Overview
This programme is delivered full-time over five days with live instructor-led classes to prepare students fully for successfully sitting the CertNexus Certified Artificial Intelligence Practitioner exam.
Part One: Understanding the Artificial Intelligence Problem
- Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems
- Analyse the use cases of ML algorithms to rank them by their success probability
- Research Learning Systems [Identify business case for image recognition; NLP; Speech recognition; Predictive & recommendation systems; Discovery & diagnostic systems; Robotics and autonomous systems]
- Analyse machine learning system use cases
- Communicate with stakeholders
- Identify potential ethical concerns
Part Two : Engineering Features for Machine Learning
- Recognise relative impact of data quality and size to algorithms
- Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations)
- Work with textual, numerical, audio, or video data formats
- Transform numerical and categorical data
- Address business risks, ethical concerns, and related concepts in data exploration / feature engineering
Part Three : Training and Tuning ML Systems and Models
- Design machine and deep learning models [Differentiate types of ML algorithms; differentiate types of DL algorithms; design for pattern recognition in predictive models]
- Optimise the algorithm (e.g., structure, run time, tuning hyperparameters)
- Train, validate, and test data subsets
- Evaluate the model
- Address business risks, ethical concerns, and related concepts in training and tuning
Part Four : Operationalising Machine Learning Models
- Deploy a model
- Secure a pipeline (includes maintenance)
- Maintain the model postproduction
- Address business risks, ethical concerns, and related concepts in operationalising the model
About CertNexus
CertNexus is a vendor-neutral certification body, providing emerging technology certifications and micro-credentials for business, data, developer, IT, and security professionals. CertNexus’ mission is to assist closing the emerging tech global skills gap while providing individuals with a path towards rewarding careers in Cybersecurity, Data Science, Data Ethics, Internet of Things, and Artificial Intelligence (AI)/ Machine Learning (ML).
We rely on our Subject Matters Experts (SMEs) to provide their industry expertise and help us develop these credentials by participating in a Job Task Analysis, Exam Item Development, and determining the Cut Score. We also depend upon practitioners in the field to participate in a survey of the Job Task Analysis and beta testing to ensure that our certifications validate knowledge and skills relevant to the industry.
Who is this course for?
This programme is designed for data science professionals seeking to acquire globally recognised certification in the use of Artificial Intelligence tools and techniques in their role.
Target candidates will typically posses 1 to 3 years experience in a professional environment and/or foundation certifications such as AIBiz or DSBiz.
Requirements
This programme is designed for data science professionals with background knowledge in statistical modelling procedures (linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc.) and programming abilities for ML and statistics (e.g., Python® and R)
Career path
This programme supports progression into a wide variety of Artificial Intelligence and Machine Learning job roles, including Machine Learning Scientist, Data Scientist, AI Developer, Machine Learning Engineer, UI/UX Designer, Robotics Process Analyst, Business Intelligence Data Analyst, Data engineer, Robotics Scientist, AI Research
Statistician, AI Researcher.
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This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.