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Cognilytica CPMAI Methodology Training

The CPMAI methodology is the industry’s best practice for AI & ML projects. Cognilytica’s CPMAI training and certification prepares you to succeed with your AI & ML efforts, whether you’re just beginning them or are well down the road with implementation.

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Foundations of AI & ML for Executives & Decision-Makers

This course is for executives and decision makers who are looking to get a foundational level overview of AI, ML, and cognitive technologies.

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Foundations of AI & ML for AI Practitioners

This course provides an introduction to AI and ML concepts, key terms, conceptualizing probabilistic reasoning, core elements of AI stack, a history of AI, previous pitfalls in the advancement of AI, and additional concepts.

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Fundamentals of AI

This course will provide you with basic AI literacy with an introduction to AI and ML concepts, key terms, conceptualizing probabilistic reasoning, core elements of AI stack, and additional foundational level concepts

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Foundations of Computer Vision & Recognition Systems

The course explains what computer vision is, discuss neural networks and their role, as well as use cases for computer vision and recognition.

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Foundations of Natural Language Processing & Conversational Systems

The course explains what natural language processing (NLP) is, highlights algorithms including neural networks and their role, as well as use cases for NLP and conversational systems

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Foundations of Predictive Analytics Systems

The course explains the basis of predictive analytics, machine learning approaches to predictive analytics, and how predictive analytics

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Foundations of Pattern & Anomaly Detection Systems

This course provides an overview of pattern & anomaly detection, which algorithms are commonly used for this pattern, and use case examples.

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Foundations of Autonomous Systems

This course provides an advanced AI concept overview of autonomous systems, which algorithms are commonly used for this pattern, and use case examples.

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Foundations of Personalization & Recommendation Systems

This course provides an advanced AI concept overview of hyperpersonalization systems, which algorithms are commonly used for this pattern, and use case examples.

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Foundations of Reinforcement Learning & Goal-Driven Systems

This course provides an advanced AI conceptual overview of goal-driven systems, with a focus on reinforcement learning. We explain which algorithms are commonly used for this pattern, and provide use case examples.

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Foundations of Data Science

This course is for those looking to get a foundational level overview of data science. This course provides a foundation of data science concepts, key terms, and additional knowledge and competencies needed to perform analysis.

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Foundations of Robotic Process Automation (RPA)

In this foundation level course we’ll explore the basics of RPA, what these bots can and can’t do, and how AI is being applied to help handle more complex tasks.

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Thinking and Acting Like a Data Scientist

This course is ideal for anyone, from executives, managers, or implementers, looking to gain skills to help them think more like a data scientist.

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Applications of AI

In this course we’ll go over various use cases for AI from a range of different industries and applications. We’ll cover the basics of interpreting AI output and recognizing potential use cases, as well as get you thinking about how to actively look for ways to identify market demands and meet customer or client needs.

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Emerging Trends in AI

This course will highlight and recognize emerging trends in AI, as well as opportunities for research. It will also go over recognizing what things are important and what things are not important as well as highlight both the power and limitations of the main machine learning and data analytics algorithms and tools.

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Risks, Threats, and Challenges of AI

This course provides a foundational level overview to help you recognize data and network security and privacy risks that come with AI, as well as AI bias, complementary compliance, incident response, as well as unique challenges to the organization.

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Managing Data for AI

In this course we’ll go over the fundamentals of managing data for AI including understanding how to collect, store, and monitor data for use in AI systems.

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Data Engineering for AI

This course provides an overview of what data engineers need to be able to identify, extract, and pull together available and pertinent heterogeneous data, including modern data sources such as social media data, open data, governmental data.

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