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AI and Machine Learning for Non-Technical Leaders

AI and Machine Learning for Non-Technical Leaders

$198.00
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What does the AI and Machine Learning for Non-Technical Leaders course cover?

AI and Machine Learning for Non-Technical Leaders is covered here in 9 modules: Introduction to AI and Machine Learning: Defining AI and machine learning, Understanding Machine Learning Algorithms: Model evaluation and selection, Deep Learning and Neural Networks: Training and optimizing neural networks and 6 more.

How do you approach AI and Machine Learning for Non-Technical Leaders step by step?

The work is sequenced in 9 stages. It starts with Introduction to AI and Machine Learning: Defining AI and machine learning, moves through Understanding Machine Learning Algorithms: Model evaluation and selection and Deep Learning and Neural Networks: Training and optimizing neural networks, and ends at Future of AI and Machine Learning.

What is in Module 1 of the AI and Machine Learning for Non-Technical Leaders course?

Module 1 is Introduction to AI and Machine Learning: Defining AI and machine learning. It works through defining AI and machine learning, history and evolution of AI and machine learning, types of machine learning: supervised, unsupervised, and reinforcement learning and 2 more. It sets the vocabulary the remaining 8 modules build on.

How is the AI and Machine Learning for Non-Technical Leaders course delivered?

The AI and Machine Learning for Non-Technical Leaders course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the AI and Machine Learning for Non-Technical Leaders course cost?

The AI and Machine Learning for Non-Technical Leaders course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Applied AI & Machine Learning Strategy for Non-Technical, Machine Learning Toolkit, Amazon Machine Learning, Azure Machine Learning.

More answers: what you get with every course, refund policy, all help answers.

AI and Machine Learning for Non-Technical Leaders Course Curriculum



Course Overview

This comprehensive course is designed to equip non-technical leaders with the knowledge and skills necessary to understand and leverage AI and machine learning in their organizations. Participants will receive a certificate upon completion of the course.



Course Features

  • Interactive and engaging learning experience
  • Comprehensive curriculum covering 80+ topics
  • Personalized learning with expert instructors
  • Up-to-date and practical knowledge with real-world applications
  • High-quality content and hands-on projects
  • Certificate upon completion
  • Flexible learning with lifetime access
  • User-friendly and mobile-accessible platform
  • Community-driven with discussion forums
  • Actionable insights and progress tracking
  • Gamification and bite-sized lessons


Course Outline

Module 1. Introduction to AI and Machine Learning: Defining AI and machine learning

  • Defining AI and machine learning
  • History and evolution of AI and machine learning
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Applications of AI and machine learning in business
  • Understanding the role of data in AI and machine learning

Module 2. Understanding Machine Learning Algorithms: Model evaluation and selection

  • Linear regression and logistic regression
  • Decision trees and random forests
  • Support vector machines and neural networks
  • Clustering and dimensionality reduction
  • Model evaluation and selection

Module 3. Deep Learning and Neural Networks: Training and optimizing neural networks

  • Introduction to deep learning and neural networks
  • Types of neural networks: feedforward, convolutional, and recurrent
  • Training and optimizing neural networks
  • Applications of deep learning: computer vision and natural language processing
  • Understanding the role of GPUs in deep learning

Module 4. Natural Language Processing: Language models and sentiment analysis

  • Introduction to natural language processing
  • Text preprocessing and feature extraction
  • Language models and sentiment analysis
  • Named entity recognition and topic modeling
  • Applications of NLP: chatbots and language translation

Module 5. Computer Vision: Object detection and segmentation, Image classification and generation

  • Introduction to computer vision
  • Image processing and feature extraction
  • Object detection and segmentation
  • Image classification and generation
  • Applications of computer vision: self-driving cars and facial recognition

Module 6. Ethics and Bias in AI: Understanding bias in AI and machine learning

  • Understanding bias in AI and machine learning
  • Types of bias: data bias, algorithmic bias, and human bias
  • Consequences of bias: fairness and transparency
  • Mitigating bias: data curation and algorithmic auditing
  • Ensuring accountability and explainability in AI

Module 7. Implementing AI in Business: Developing an AI strategy and roadmap

  • Identifying business problems for AI solutions
  • Developing an AI strategy and roadmap
  • Building an AI team and infrastructure
  • Managing AI projects and stakeholders
  • Measuring ROI and impact of AI initiatives

Module 8. AI and Machine Learning in Industry: AI in healthcare: medical imaging and disease diagnosis

  • AI in healthcare: medical imaging and disease diagnosis
  • AI in finance: risk management and portfolio optimization
  • AI in marketing: customer segmentation and personalization
  • AI in transportation: autonomous vehicles and route optimization
  • AI in education: adaptive learning and student assessment

Module 9: Future of AI and Machine Learning

  • Emerging trends: explainability, transparency, and accountability
  • Advances in AI: multimodal learning and cognitive architectures
  • Impact of AI on work and society: job displacement and skills training
  • Ensuring AI safety and security: adversarial attacks and defenses
  • Future directions: human-AI collaboration and hybrid intelligence


Certificate and Assessment

Participants will receive a certificate upon completion of the course, which includes:

  • Completing all course modules and assignments
  • Passing a final assessment with a minimum score of 80%
  • Participating in discussion forums and engaging with peers


Course Format

The course is delivered online, with:

  • Video lectures and tutorials
  • Interactive quizzes and assessments
  • Hands-on projects and assignments
  • Discussion forums and peer feedback
  • Lifetime access to course materials
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