12x Your Business with AI: Proven Techniques for Revenue and Productivity Boost Course

Unlock the Potential of AI to Grow Your Business: Proven Strategies for Increasing Revenue and Productivity

  • Category Artificial Intelligence
12x Your Business with AI: Proven Techniques for Revenue and Productivity Boost Course

What you'll learn

  • Fundamentals of AI and Machine Learning
  • Natural Language Processing (NLP)
  • Prompt Engineering
  • Hands-On Experience with AI Tools:
  • AI Ethics and Responsible AI
  • Advanced AI Topics

Course Syllabus

Introduction to AI
  • General AI
  • Superintelligent AI)";i:4;s:40:"Applications of AI in Various Industries";}
  • a:5:{i:0;s:35:"Overview of Artificial Intelligence";i:1;s:27:"History and Evolution of AI";i:2;s:28:"Key Concepts and Terminology";i:3;s:56:"Types of AI (Narrow AI
Fundamentals of Machine Learning
  • Clustering
  • Decision Trees
  • etc.)";i:3;s:31:"Model Evaluation and Validation";i:4;s:29:"Introduction to Deep Learning";}
  • a:5:{i:0;s:32:"Introduction to Machine Learning";i:1;s:32:"Introduction to Machine Learning";i:2;s:68:"Key Algorithms (Linear Regression
Natural Language Processing (NLP)
  • GPT
  • Lemmatization
  • Named Entity Recognition)";i:3;s:49:"Introduction to Language Models (BERT
  • etc.)";i:4;s:19:"Applications of NLP";}
  • a:5:{i:0;s:13:"Basics of NLP";i:1;s:29:"Text Preprocessing Techniques";i:2;s:72:"Common NLP Tasks (Tokenization
Applications of NLP
  • a:4:{i:0;s:27:"What is Prompt Engineering?";i:1;s:27:"Importance and Applications";i:2;s:43:"Key Principles of Writing Effective Prompts";i:3;s:31:"Understanding AI Model Behavior";}
Prompt Engineering Techniques•
  • Translation
  • etc.)";i:4;s:37:"Best Practices for Prompt Engineering";}
  • a:5:{i:0;s:26:"Basic Prompting Techniques";i:1;s:29:"Advanced Prompting Techniques";i:2;s:28:"Iterative Prompt Development";i:3;s:71:"Using Prompts for Different AI Tasks (Summarization
Hands-On with AI Tools
  • DALL-E
  • GPT-4
  • etc.)";i:1;s:32:"Practical Exercises and Examples";i:2;s:24:"Building Custom Chatbots";i:3;s:24:"Automating Tasks with AI";}
  • a:4:{i:0;s:52:"Working with AI Tools (ChatGPT
AI Ethics and Responsible AI
  • a:4:{i:0;s:28:"Ethical Considerations in AI";i:1;s:30:"Bias and Fairness in AI Models";i:2;s:29:"Privacy and Security Concerns";i:3;s:41:"Guidelines for Responsible AI Development";}
AI Integration and Deployment
  • a:4:{i:0;s:32:"Integrating AI into Applications";i:1;s:21:"Deployment Strategies";i:2;s:37:"Monitoring and Maintaining AI Systems";i:3;s:41:"Case Studies of Successful AI Deployments";}
Advanced Topics in AI
  • a:4:{i:0;s:22:"Reinforcement Learning";i:1;s:38:"Generative Adversarial Networks (GANs)";i:2;s:21:"AI in Computer Vision";i:3;s:27:"AI for Predictive Analytics";}
Case Studies and Projects
  • a:3:{i:0;s:20:"Real-World Use Cases";i:1;s:29:"Guided Projects and Exercises";i:2;s:32:"Q&A and Troubleshooting Sessions";}

Course Syllabus

  • Overview of Artificial Intelligence
  • History and Evolution of AI
  • Key Concepts and Terminology
  • Types of AI (Narrow AI, General AI, Superintelligent AI)
  • Applications of AI in Various Industries

  • Introduction to Machine Learning
  • Introduction to Machine Learning
  • Key Algorithms (Linear Regression, Decision Trees, Clustering, etc.)
  • Model Evaluation and Validation
  • Introduction to Deep Learning

  • Basics of NLP
  • Text Preprocessing Techniques
  • Common NLP Tasks (Tokenization, Lemmatization, Named Entity Recognition)
  • Introduction to Language Models (BERT, GPT, etc.)
  • Applications of NLP

  • What is Prompt Engineering?
  • Importance and Applications
  • Key Principles of Writing Effective Prompts
  • Understanding AI Model Behavior

  • Basic Prompting Techniques
  • Advanced Prompting Techniques
  • Iterative Prompt Development
  • Using Prompts for Different AI Tasks (Summarization, Translation, etc.)
  • Best Practices for Prompt Engineering

  • Working with AI Tools (ChatGPT, GPT-4, DALL-E, etc.)
  • Practical Exercises and Examples
  • Building Custom Chatbots
  • Automating Tasks with AI

  • Ethical Considerations in AI
  • Bias and Fairness in AI Models
  • Privacy and Security Concerns
  • Guidelines for Responsible AI Development

  • Integrating AI into Applications
  • Deployment Strategies
  • Monitoring and Maintaining AI Systems
  • Case Studies of Successful AI Deployments

  • Reinforcement Learning
  • Generative Adversarial Networks (GANs)
  • AI in Computer Vision
  • AI for Predictive Analytics

  • Real-World Use Cases
  • Guided Projects and Exercises
  • Q&A and Troubleshooting Sessions

Requirements

  • Good wifi
  • Laptop

Description

12x Your Business with AI: Proven Techniques for Revenue and Productivity Boost Course

This comprehensive course provides an in-depth exploration of artificial intelligence and prompt engineering.

  • This comprehensive course offers a detailed exploration of artificial intelligence and prompt engineering, providing a thorough understanding of key concepts and practical applications.
  • Gain insight into the history of AI, various types, and machine learning fundamentals, including supervised and unsupervised learning, key algorithms, and model evaluation.
  • Learn essential text preprocessing techniques and common NLP tasks in natural language processing (NLP), as well as the importance of effective prompt engineering and understanding AI model behavior.
  • Explore advanced prompting techniques and best practices, with hands-on sessions using AI tools like ChatGPT, GPT-4, and DALL-E to build custom chatbots and automate tasks.
  • Dive into AI ethics, focusing on bias, fairness, privacy, and security considerations, while also exploring advanced AI topics such as reinforcement learning, GANs, and predictive analytics with real-world case studies and guided projects.

Who this course is for:

  • Aspiring AI and Machine Learning Professionals
  • Data Scientists and Analysts
  • Researchers and Academics
  • Business Leaders and Managers

Meet your instructors

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Shiva Krishnan

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Why enroll in this course?

Lead digital transformation in your organization by mastering the core concepts of generative artificial intelligence and its potential impact.

Master and integrate prompt engineering to optimize day-to-day tasks and automate workflows.

Foster an “AI friendly” culture in your organization by understanding the ethical aspects and the risks associated with the implementation of this technology.

Explore tools such as ChatGPT, as well as other emerging technologies, to improve productivity.

Interact with MIT experts, instructors, and peers in live synchronous sessions for a more comprehensive learning experience.

Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

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Course Syllabus

  • Overview of Artificial Intelligence
  • History and Evolution of AI
  • Key Concepts and Terminology
  • Types of AI (Narrow AI, General AI, Superintelligent AI)
  • Applications of AI in Various Industries

  • Introduction to Machine Learning
  • Introduction to Machine Learning
  • Key Algorithms (Linear Regression, Decision Trees, Clustering, etc.)
  • Model Evaluation and Validation
  • Introduction to Deep Learning

  • Basics of NLP
  • Text Preprocessing Techniques
  • Common NLP Tasks (Tokenization, Lemmatization, Named Entity Recognition)
  • Introduction to Language Models (BERT, GPT, etc.)
  • Applications of NLP

  • What is Prompt Engineering?
  • Importance and Applications
  • Key Principles of Writing Effective Prompts
  • Understanding AI Model Behavior

  • Basic Prompting Techniques
  • Advanced Prompting Techniques
  • Iterative Prompt Development
  • Using Prompts for Different AI Tasks (Summarization, Translation, etc.)
  • Best Practices for Prompt Engineering

  • Working with AI Tools (ChatGPT, GPT-4, DALL-E, etc.)
  • Practical Exercises and Examples
  • Building Custom Chatbots
  • Automating Tasks with AI

  • Ethical Considerations in AI
  • Bias and Fairness in AI Models
  • Privacy and Security Concerns
  • Guidelines for Responsible AI Development

  • Integrating AI into Applications
  • Deployment Strategies
  • Monitoring and Maintaining AI Systems
  • Case Studies of Successful AI Deployments

  • Reinforcement Learning
  • Generative Adversarial Networks (GANs)
  • AI in Computer Vision
  • AI for Predictive Analytics

  • Real-World Use Cases
  • Guided Projects and Exercises
  • Q&A and Troubleshooting Sessions

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Course Syllabus

Introduction to AI
  • General AI
  • Superintelligent AI)";i:4;s:40:"Applications of AI in Various Industries";}
  • a:5:{i:0;s:35:"Overview of Artificial Intelligence";i:1;s:27:"History and Evolution of AI";i:2;s:28:"Key Concepts and Terminology";i:3;s:56:"Types of AI (Narrow AI
Fundamentals of Machine Learning
  • Clustering
  • Decision Trees
  • etc.)";i:3;s:31:"Model Evaluation and Validation";i:4;s:29:"Introduction to Deep Learning";}
  • a:5:{i:0;s:32:"Introduction to Machine Learning";i:1;s:32:"Introduction to Machine Learning";i:2;s:68:"Key Algorithms (Linear Regression
Natural Language Processing (NLP)
  • GPT
  • Lemmatization
  • Named Entity Recognition)";i:3;s:49:"Introduction to Language Models (BERT
  • etc.)";i:4;s:19:"Applications of NLP";}
  • a:5:{i:0;s:13:"Basics of NLP";i:1;s:29:"Text Preprocessing Techniques";i:2;s:72:"Common NLP Tasks (Tokenization
Applications of NLP
  • a:4:{i:0;s:27:"What is Prompt Engineering?";i:1;s:27:"Importance and Applications";i:2;s:43:"Key Principles of Writing Effective Prompts";i:3;s:31:"Understanding AI Model Behavior";}
Prompt Engineering Techniques•
  • Translation
  • etc.)";i:4;s:37:"Best Practices for Prompt Engineering";}
  • a:5:{i:0;s:26:"Basic Prompting Techniques";i:1;s:29:"Advanced Prompting Techniques";i:2;s:28:"Iterative Prompt Development";i:3;s:71:"Using Prompts for Different AI Tasks (Summarization
Hands-On with AI Tools
  • DALL-E
  • GPT-4
  • etc.)";i:1;s:32:"Practical Exercises and Examples";i:2;s:24:"Building Custom Chatbots";i:3;s:24:"Automating Tasks with AI";}
  • a:4:{i:0;s:52:"Working with AI Tools (ChatGPT
AI Ethics and Responsible AI
  • a:4:{i:0;s:28:"Ethical Considerations in AI";i:1;s:30:"Bias and Fairness in AI Models";i:2;s:29:"Privacy and Security Concerns";i:3;s:41:"Guidelines for Responsible AI Development";}
AI Integration and Deployment
  • a:4:{i:0;s:32:"Integrating AI into Applications";i:1;s:21:"Deployment Strategies";i:2;s:37:"Monitoring and Maintaining AI Systems";i:3;s:41:"Case Studies of Successful AI Deployments";}
Advanced Topics in AI
  • a:4:{i:0;s:22:"Reinforcement Learning";i:1;s:38:"Generative Adversarial Networks (GANs)";i:2;s:21:"AI in Computer Vision";i:3;s:27:"AI for Predictive Analytics";}
Case Studies and Projects
  • a:3:{i:0;s:20:"Real-World Use Cases";i:1;s:29:"Guided Projects and Exercises";i:2;s:32:"Q&A and Troubleshooting Sessions";}

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