Practical Guide to AI & ML: Mastering Future Tech Skills

Practical Guide to AI & ML: Mastering Future Tech Skills, available at $54.99, has an average rating of 4.53, with 46 lectures, based on 129 reviews, and has 950 subscribers.

You will learn about Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning. Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent. Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning. Explain the concept of machine learning and its relation to AI. Define artificial intelligence (AI) and differentiate it from human intelligence. Describe what Artificial Intelligence is, and what it is not. Explain what types of sophisticated software systems are not AI systems. Describe how Machine Learning is different to the classical software development approach. Compare and contrast supervised, unsupervised, and reinforcement learning. Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features. Explain Function Approximators and the role of Neural Networks as Universal Function Approximators. Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data. Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals. Identify examples of AI in everyday life and discuss their impact. Evaluate the effectiveness of different AI applications in real-world scenarios. Apply basic principles of neural networks to a hypothetical problem. Discuss the role of data in training AI models Construct a neural network model for a specified task Assess the impact of AI on job markets and skill requirements See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net. Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation. Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization. Understanding the impact of evaluation metrics on model performance, and how to check for overfitting. Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use. Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration. Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance. Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process. Learn how to use Model Builder to train models without having to code. This course is ideal for individuals who are Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations. or Busy professionals who need a short, easy but solid understanding of AI fundamentals. or Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role. or Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration. or Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning. or Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products. or Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists. or Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood. or This course is not for you if you have an aversion or intense dislike for Mathematics. or Also, if you are looking for coding tips, technical detail about the different machine learning algorithms, back-propagation in Neural Networks, loss functions, gradient descent, policy gradient methods, etc., then these series of lessons are definitely not for you. or This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice. or Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice. or Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation. or Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required. It is particularly useful for Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations. or Busy professionals who need a short, easy but solid understanding of AI fundamentals. or Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role. or Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration. or Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning. or Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products. or Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists. or Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood. or This course is not for you if you have an aversion or intense dislike for Mathematics. or Also, if you are looking for coding tips, technical detail about the different machine learning algorithms, back-propagation in Neural Networks, loss functions, gradient descent, policy gradient methods, etc., then these series of lessons are definitely not for you. or This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice. or Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice. or Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation. or Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.

Enroll now: Practical Guide to AI & ML: Mastering Future Tech Skills

Summary

Title: Practical Guide to AI & ML: Mastering Future Tech Skills

Price: $54.99

Average Rating: 4.53

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

• Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning.
• Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent.
• Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
• Explain the concept of machine learning and its relation to AI.
• Define artificial intelligence (AI) and differentiate it from human intelligence.
• Describe what Artificial Intelligence is, and what it is not.
• Explain what types of sophisticated software systems are not AI systems.
• Describe how Machine Learning is different to the classical software development approach.
• Compare and contrast supervised, unsupervised, and reinforcement learning.
• Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
• Explain Function Approximators and the role of Neural Networks as Universal Function Approximators.
• Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data.
• Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
• Identify examples of AI in everyday life and discuss their impact.
• Evaluate the effectiveness of different AI applications in real-world scenarios.
• Apply basic principles of neural networks to a hypothetical problem.
• Discuss the role of data in training AI models
• Construct a neural network model for a specified task
• Assess the impact of AI on job markets and skill requirements
• See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
• Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
• Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
• Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
• Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
• Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
• Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
• Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
• Learn how to use Model Builder to train models without having to code.

Who Should Attend

• Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations.
• Busy professionals who need a short, easy but solid understanding of AI fundamentals.
• Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role.
• Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration.
• Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning.
• Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products.
• Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists.
• Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood.
• This course is not for you if you have an aversion or intense dislike for Mathematics.
• Also, if you are looking for coding tips, technical detail about the different machine learning algorithms, back-propagation in Neural Networks, loss functions, gradient descent, policy gradient methods, etc., then these series of lessons are definitely not for you.
• This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
• Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
• Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
• Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.

Target Audiences

• Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations.
• Busy professionals who need a short, easy but solid understanding of AI fundamentals.
• Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role.
• Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration.
• Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning.
• Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products.
• Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialiists.
• Ai and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood.
• This course is not for you if you have an aversion or intense dislike for Mathematics.
• Also, if you are looking for coding tips, technical detail about the different machine learning algorithms, back-propagation in Neural Networks, loss functions, gradient descent, policy gradient methods, etc., then these series of lessons are definitely not for you.
• This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
• Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
• Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
• Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.