CPMAI Training & Certification Course Details

CPMAI™ AI & ML Project Management Training & Certification

Cognilytica’s AI & ML Project Management Training & Certification Boot Camp is recognized around the world as one of the best AI & ML as related to Project Management training courses available anywhere.

The Boot Camp is an intensive, interactive, real-world based “fire hose” that prepares you to succeed with your AI & ML efforts, whether you’re just beginning them or are well down the road with implementation, reflecting the best thinking and research that Cognilytica produces.

Want to learn how to successfully run AI projects at any scale? Learn the latest best-practices methodology for AI & ML projects with Cognilytica’s Cognitive Project Management for AI (CPMAI) Methodology .

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. CPMAI Training and Certification is offered as a virtual self-paced course where individuals or small groups can sign up and take the virtual courses around your busy schedule.

You’ll learn the following:

Fundamentals of AI and ML Terminology and concepts
The Seven Patterns of AI
AI Project Management Best Practices
Deep dive into actual AI projects using CPMAI
Understanding of supervised, unsupervised, and reinforcement learning methods, approaches, concepts, and algorithms
Most important aspects of Data Science relevant to AI
How business understanding, data understanding, data preparation, model development, model evaluation, and model operationalization fit together
Iterative and agile methods for AI
How to build Ethical and Responsible AI systems
How to craft an ideal AI team

Cognilytica’s CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise. This course is valuable for anyone who wants in-depth knowledge about how to succeed with AI & ML related projects.

Click to Enroll in our Self-Paced Online Class

What makes Cognilytica Training so special?
Vendor independent
End User focused
Content updated multiple times per year
AI & ML Project Management Focused
Provides the enterprise context
Offers detailed case studies
Worldwide proven, International training audience
Led by AI & ML thought leaders
Not too technical, not too high-level
Includes certification

No Prerequisites!

Cognilytica’s CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise. This course is valuable for anyone who wants in-depth knowledge about how to succeed with AI & ML related projects.

Agenda 5.0

Module 1

Module 1: Foundations of AI
The Promise of AI Why does AI matter AI Reality vs. AI Myth A Brief History of AI Why is AI a “thing” now? Definitions of AI – Terminology and Definitions Narrow vs. Strong AI The Core Aspects of Intelligence Machine Learning and Cognitive Technology Supervised, Unsupervised, and Reinforcement Learning	Deep Learning: A Revolution in Neural Networks Augmented Intelligence Our AI-Enabled Vision of the Future The DIKUW Pyramid (AI is Based on Data) What is Data Science and why is it relevant to AI Realizing the Promise of AI: The AI-Enabled Vision of the Future Data-centric methodologies for AI Leveraging CRISP-DM Methodology Agile Methodology & CPMAI : Dual Iteration Loops Introducing CPMAI

Module 2

Module 2: Patterns of AI

Do We Even Need AI?
Where AI is Best Suited
The 7 Patterns of AI
Pattern: Conversations & Content
- Conversational Use Cases
- Conversational Industry Examples
- Conversational Case Study
Pattern: Recognition
- Recognition Use Cases
- Recognition Industry Examples
- Recognition Case Study
Pattern: Autonomous Systems
- Autonomous Use Cases
- Autonomous Industry Examples
- Autonomous Case Study

Pattern: Hypersonalization
- Hyperpersonalization Use Cases
- Hyperpersonalization Industry Examples
- Hyperpersonalization Case Study
Pattern: Pattern & Anomaly Detection
- Pattern & Anomaly Detection Use Cases
- Pattern & Anomaly Detection Industry Examples
- Pattern & Anomaly Detection Case Study
Pattern: Predictive Analytics & Decision Support
- Predictive Analytics & Decision Support Use Cases
- Predictive Analytics & Decision Support Industry Examples
- Predictive Analytics & Decision Support Case Study
Pattern: Goal-Driven Systems
- Goal-Driven Systems Use Cases
- Goal-Driven Systems Industry Examples
- Goal-Driven Systems Case Study
Tackling AI Projects as Combinations of Patterns
- Examples with multiple patterns in a single project

Module 3

Module 3: CPMAI Phase I: Business Understanding
ML Anti-Patterns Case Studies in AI Real-World Examples and Use Cases Identifying Value Propositions for AI & Cognitive Tech Developing Business-Centric ROI Autonomous vs. Augmented / Assisted Intelligence Approaches	Probabilistic vs. Deterministic Systems The AI Go/No-Go Decision Scoping AI Projects CPMAI Phase I Deliverables Taking a Data-Centric Mentality on AI

Module 4

Module 4: CPMAI Phase II: Data Understanding

AI Projects Need a Foundation in Big Data
Moving our way up the DIKUW Pyramid
Structured, Unstructured, Semi-Structured data
What is Data Science?
What is Data Engineering?
Data Scientists vs. Data Engineers
Relationship between ML & Data Science

80% of AI Projects are Data Engineering
What Did We Learn from Big Data?
Big Data Infrastructure
Big Data Methodologies
Applying what we learned from Big Data Projects
Do you even need Big Data for AI?
Taking a Data-Centric Mentality on AI
CPMAI Phase II Deliverables

Module 5

Module 5: CPMAI Phase III: Data Preparation

Data engineering and preparation
Data Preparation: Questions to Answer
Data Engineering Tasks
AI-Specific Needs of Data Preparation
Dataset Preparation & Pre-Processing

Cleaning & Enhancing Data
Data Selection / Sampling
Labeling Data
CPMAI Data Preparation Documents & Artifacts
CPMAI Phase 3 Worksheet

Module 6

Module 6: CPMAI Phase IV: Model Development

Intelligence requires learning
Machine Learning: A Technology Definition
How do we encode experience?
Machine Learning Terminology
ML Concept: Dimensions
The Curse of Dimensionality: Why Machine Learning is Hard
ML Concept: Classification
ML Concept: Regression
Diving Deeper into the Three Forms of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Machine Learning Algorithms & Models
Supervised Learning & ML Concepts Visually Explained
Finding the Line: Cost Functions, Gradient Descent and More
Neural Networks: Mimicking the Brain
Deep Learning: Making Neural Networks Work
ML Model Training: Tuning the Knobs
Flavors of Deep Learning Neural Networks
What exactly is (in) a ML model?
Transfer Learning: Speeding Things Up
The Pre-Trained Model

Using Pre-Trained Models (Networks)
Other approaches to Supervised Learning
Support Vector Machines (SVM)
Decision Trees
Random Forests
Bayesian Classifiers
K-Nearest Neighbors
Machine Learning Challenge: The Bias-Variance Tradeoff
Resolving the Bias-Variance Tradeoff: Cross-Validation
Improving Performance with Ensemble Methods
Unsupervised Learning & ML Concepts Visually Explained
Neural Network approaches to Unsupervised Learning
K-Means Clustering
Gaussian Mixture Models
t-SNE Example
Autoencoders and GANs
Reinforcement Learning & ML Concepts Visually Explained
Q-Learning
DeepMind Examples
The Limits of Machine Learning
Machine Reasoning & Common Sense
Knowledge Graphs
AI is still evolving

Module 7

Module 7: CPMAI Phase V: Model Evaluation
Model Evaluation & Testing The Confusion Matrix Business / KPI evaluation Model iteration	Performance Evaluation Business evaluation Learning the curves Evaluating vs. the heuristic

Module 8

Module 8: CPMAI Phase VI: Model Operationalization

AI Vendor Classification
Big Data infrastructure and AI
The four “platforms” of AI
ML as a Service and Cloud ML
AI operationalization
The Data Science Environment
The Data Science Notebook
Data Science Notebook Choices: Jupytr, Colab, and More
Setting up a Good Data Notebook Environment
Data Visualization and AI
Python, R, MATLAB, Mathematica, and More
ML Frameworks / Toolkits Compared: Keras, TensorFlow, Caffe(2), Pytorch, MXNet, and more
Model Development: Python Scikit-Learn
Model Training
Accelerating Model Training: GPUs, TPUs, and More
AutoML
Vendors in the Data Science Environment
Pre-Trained Models and Networks
The Big Data & Data Engineering Environment
Hadoop & Spark

Data Preparation Tools
Data Labeling Solutions
Vendors in the Data Engineering Environment
Model “Scaffolding” Environment
Machine Learning as a Service (MLaaS)
Cloud ML Services
Cloud Training vs. Cloud Inference
Point-Solution AI Providers: Recognition
Point-Solution AI Providers: Predictive Analytics & Decision Support
Point-Solution AI Providers: Conversation & Content Generation
Point-Solution AI Providers: Industry-Specific
Point-Solution AI Providers: Patterns & Anomalies
Vendors in the “Middle” AI Environment
The Operational Environment
Operationalizing ML at the “Edge”
Operationalizing ML in the Server
Operationalizing ML in the Cloud
The Problem with Pseudo AI

Module 9

Module 9: Responsible and Ethical AI

For AI to have Lasting Positive Impact it Must be Done Responsibly
Transformative technologies are disruptive.
Tackling Fears of AI
Tackling Real Concerns about AI
AI is not a Job Killer, but it is a Job Category Killer
Automation & The Amazon Paradox
Making AI Transparent & Explainable
Detecting Informational Bias in Datasets
AI Introduces new Threat Vectors: Malicious AI
AI Can Make the Fake Seem Real… and the reverse
The “Uncanny Valley”
Privacy in an Era of AI-Enhanced Big Data
Pervasive surveillance

The rise of Adversarial attacks on Computer Vision systems
Concerns over Artificial General Intelligence (AGI)
The “Singularity”
Resolving these Issues: Keep Humans in the Loop
The Future or Dystopia?
AI Laws & Regulations
Organizational AI Ethics & Governance
Resolving these Issues: Provide Transparency
GDPR and AI Transparency
Resolving these Issues: Prioritize Fairness and Dignity
The Limits of Current Cognitive Tech
Group Discussion & Exercises

How Does Cognilytica’s Training Compare?

	Cognilytica	Other Online Training	Vendor Training
Course Price (Self-paced online, per person)	$2,495	???	$$$
Certification & Exam Cost Included	✅	✗	???
Vendor-Independent	✅	???	✗
Project-Management Focused	✅	✗	✗
Training offered in person and online	✅	???	???
Content Updated Many Times per Year	✅	???	???

Online, Self-Paced

We offer our CPMAI Training & Certification as a self-paced virtual offering that you can complete on your time around your busy schedule. No traveling required for this offering! Each session can be completed in around 2 hours, with exercises provided after each module. Sign up on the link below!

Click to Enroll in our Self-Paced Online Class

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