Cognilytica AI & Cognitive Technology Education

Further your knowledge on AI and machine learning best practices, methodologies, concepts, and technologies from industry experts. On your schedule. At your pace.

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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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Applications of Robotic Process Automation (RPA)

In this course we’ll go over various use cases for RPA bots including examples for attended bots and unattended bots from a range of different industries and applications. We’ll cover the basics of RPA including best practices for building RPA bots as well as best practices for deploying RPA bots. We’ll also address the current limits of RPA as well as how you can apply AI to RPA to make it more intelligent.

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Applying Big Data Practices to AI

Data is often stored in silos, hard to access data warehouses, or stuck in old systems that lack integration with the rest of the organization. This course shows you how to apply big data practices to AI to allow for better data management.

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Applying Responsible AI

This course provides guidance on how to implement various controls, safeguards, governance approaches, and technical issues in responsible use of AI such as measuring bias and fairness.

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Best Practices & Methodologies for Successful AI Implementation

In this course we’ll walk through different Methodologies including CRISP-DM and CPMAI to provide you with the foundation needed to project success.

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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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CPMAI Methodology Phase I: Business Understanding

In this course we’ll walk you through Phase I of the CPMAI Methodology: Business Understanding. In this phase, we’ll help you with business understanding including answering what are your core business requirements and objectives, provide you with AI-relevant considerations, and an assessment of the business situation.

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CPMAI Methodology Phase II: Data Understanding

In this course we’ll walk you through Phase II of the CPMAI Methodology. In this phase, we’ll help you with data understanding including answering what are your core data requirements and objectives, identifying sources and types of data for training, and other data focused understanding.

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CPMAI Methodology Phase III: Data Preparation

In this course we’ll walk you through Phase III of the CPMAI Methodology. In this phase, we’ll help you with data preparation including reviewing feature enhancement and pruning, rationale for dataset inclusion / exclusion, data cleansing report, and other associated data preparation steps needed. You’ll gain an understanding of AI project management including product development & prototyping and learn best practice for implementing AI on a large scale as well as AI’s impact on strategy.

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CPMAI Methodology Phase IV: Model Development

In this course we’ll walk you through Phase IV of the CPMAI Methodology. This phased is focused on model development including modeling assumptions, test design, algorithm selection, and other critical things needed for building and developing models.

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CPMAI Methodology Phase V: Model Evaluation

In this course we’ll walk you through Phase V of the CPMAI Methodology. This phase is focused onmodel evaluation, including use of training learning curves, confusion matrix evaluation considerations, real-world model testing and validation, and other necessary steps for evaluating your model.

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CPMAI Methodology Phase VI: Model Operationalization

In this course we’ll walk you through phase VI of the CPMAI Methodology. In this phase, you’re focused on putting your models into operation, also called model operationalization. We discuss a deployment plan, a monitoring and maintenance plan, as well as additional considerations needed for model operationalization.

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Data & Intellectual Property Considerations for AI

In this course we’ll provide a foundational level overview of data & intellectual property (IP) considerations for AI.

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Data Analysis and Visualization with AI

Knowing how to structure & display data, as well as use data to create a story are necessary skills when it comes to making sense of your data. This course will go over data analysis and visualization concepts and tools needed for Data analysis, management & visualization.

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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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Data Preparation for AI

Without clean and accurate data you run the risk of training your AI systems on bad data, resulting in skewed or inaccurate results. In this course we’ll go over best practices for preparing data for use with AI.

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Diving Deeper into AI: Algorithms & Methods

In this course we will provide a deeper level overview of AI Algorithms. We’ll get into concepts of classification, regression, and clustering. We’ll also discuss different algorithms you can select when doing supervised, unsupervised, or reinforcement learning and dig deeper into various algorithms.

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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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Ethical & Responsible AI

This course outlines ethical issues related to AI and how you can adhere to all relevant regulations and emerging ethical frameworks.

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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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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 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 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 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 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 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 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 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 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 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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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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Implementing AI: AI and ML Frameworks & Platforms

Understanding how to implement AI is just crucial to your project’s success. This course will explore the common frameworks to implement AI models, as well as focus on operating in different environments such as the Cloud and on-premise vs. in-cloud ML-as-a-Service (MLaaS) technologies.

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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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Model Evaluation and Testing

This course digs into model evaluation and testing, providing insights into key evaluation concepts like the confusion matrix, Business / KPI evaluation, learning curves, ROC and AUC curves, and evaluating AI models vs. the heuristic.

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Model Operationalization

This course digs deeper into model operationalization, and the specifics for how to put models for inference into various places of deployment and operations. We’ll go over the different ways you operationalize your model including on the edge, in the cloud, on premise, and hybrid options.

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Overview of Machine Learning Algorithms, Methods & Models

In this course we will provide an overview of AI algorithms, models, and methods. We’ll get into concepts of classification, regression, and clustering including what they are and when to use each. We’ll also discuss different algorithms you can select when doing supervised, unsupervised, or reinforcement learning.

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Responsible AI: Incident Response

This course focuses on how to design appropriate incident response for AI and cloud service models as well as when to escalate concerns about ethical and safety issues.

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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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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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Hear from our Successful Trainees

Cognilytica’s AI & ML project management training provided me with a deep dive into the fundamentals of AI & machine learning with a brief exposure to project management. This course is a must attend for someone who has little/no prior experience on AI and is interested to learn an overview of AI, how to do AI right, bring his/her knowledge to the next level, and gain in-depth knowledge into some of the major algorithms of supervised, unsupervised, & reinforced learning approaches to AI & ML.
Mayur Shah
AI / Cognitive Technologies Project Manager at Motorola Mobility LLC - A Lenovo Company
I really did enjoy Cognilytica's AI & ML training; the verbal and visual aspect of the training were very informative and definitely kept my attention. The exercises helped to re-emphasize what we learned in the class, rendering them very useful.
Mitchell Winans
Senior Advisor, Enterprise Digitalization at Internal Revenue Service (IRS)
Cognilytica’s AI/ML training helped teach me the fundamentals of AI including various algorithms, why deep learning has become so popular, and why data is at the heart of AI. I highly recommend this training for anyone looking to learn how to do AI right, and who wants to learn the data and AI centric CPMAI methodology.
Casey Royer
Executive Director, Personalized Experience Management at USAA
Our management team, consisting of clinical science, data science, data engineering, software and IT ops team leads, completed Cognilytica’s AI & ML training. The course gave an excellent overview of the fundamentals of AI and also provided us with a common framework for our teams to communicate and collaborate in the management of AI projects. The training has put us in a good position to launch several projects in the next month. I would highly recommend the course for those seeking to rapidly accelerate their knowledge and readiness to successfully manage AI projects.
Dr. Salim Samanani
CEO and Medical Director, OKAKI Health Intelligence