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Artificial Intelligence for Everyone – Easy to Understand

By kpmkhaja2 Uncategorized
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About Course

This course provides a comprehensive introduction to the fundamentals of Artificial Intelligence (AI), exploring key concepts, techniques, and practical applications. Participants will delve into the realm of AI, understanding its impact on various industries and gaining hands-on experience with prominent AI technologies, including OpenAI, Bing Image Creator, and Google Bard.

This course is designed for individuals with a basic understanding of programming and a curiosity about the field of Artificial Intelligence. It is suitable for professionals across various domains who want to explore the foundational concepts of AI and gain practical experience with leading AI technologies.

Basic programming knowledge (preferably in Python) is recommended. No prior experience in AI is required.

By the end of this course, participants will have a solid understanding of AI fundamentals and practical experience with key technologies, enabling them to embark on further exploration or integration of AI concepts into their respective fields.Whether participants are looking to kickstart a career in AI, enhance their technical expertise, or simply stay informed about the cutting-edge developments in the field, this course offers a well-rounded exploration of AI basics with a focus on hands-on experience and ethical considerations.

The course aims not only to empower participants with technical skills but also to equip them with the critical thinking necessary for navigating the ethical complexities surrounding AI.

What Will You Learn?

  • You will learn about fundamentals of AI
  • The explanations are designed to make anyone understand what this is all about
  • AI tools which will help you in your job, college, school or career
  • Technical or development knowledge to get you going in development of AI

Course Content

Introductory topics

  • AI and Non-AI Systems – Differences
    09:00
  • Reduce AI Fear
    04:13
  • AI – Is it Software or Hardware
    06:31
  • AI Tools you need to know
    05:02
  • What are language models in AI
    13:03
  • Language Model Demo with a simple Chat
    09:15
  • What is a Linear regression model in AI
    08:00
  • What is NLP or Natural Language Processing
    06:24
  • What is Labelling in AI
    04:32
  • What is Generative AI
    04:16
  • What is Deep Learning
    11:23
  • What are AI Data Centers
  • What does training a model mean
    13:34
  • What are neural networks
    06:53
  • How Data is very important for AI
    13:41

AI in many industries
In this section i will explain how AI is being used or will be used in many industries. Note that AI is tech which will help other industries just like information technology.

What is Generative AI and AI terminologies
Generative AI refers to a subset of artificial intelligence techniques where algorithms are designed not just to recognize patterns or classify data but to generate new content, whether it's images, text, music, or even entire sequences of events. This field encompasses a range of technologies and methodologies, including but not limited to: Generative Adversarial Networks (GANs): A type of neural network architecture where two networks (generator and discriminator) compete against each other to generate increasingly realistic data. GANs are widely used in generating images, videos, and other forms of media. Recurrent Neural Networks (RNNs): A class of neural networks designed to handle sequential data, such as text or speech. They are often used in natural language processing (NLP) tasks like language generation and text prediction. Transformer Networks: A type of neural network architecture based on self-attention mechanisms, originally designed for NLP tasks like translation and language modeling. Transformers have been highly successful in generating coherent and contextually relevant text. Variational Autoencoders (VAEs): A type of generative model that learns a low-dimensional representation of data (latent space) and can generate new data points that resemble the training data. Autoregressive Models: Models that generate output one element at a time, conditioning each prediction on previous elements. Examples include autoregressive language models like GPT (Generative Pretrained Transformer) series. Natural Language Generation (NLG): A subfield of AI focused on generating natural language output from structured data or other inputs. NLG is essential for applications like chatbots, summarization, and storytelling. AI Terminologies: Machine Learning: A branch of AI where algorithms learn patterns and make predictions from data without explicit programming. Deep Learning: A subset of machine learning using deep neural networks with many layers, capable of learning from large amounts of data. Supervised Learning: Learning where the model is trained on labeled data, i.e., data with input-output pairs provided. Unsupervised Learning: Learning where the model is trained on unlabeled data and must find patterns or groupings within the data. Reinforcement Learning: Learning where an agent learns to make decisions by interacting with an environment and receiving feedback in terms of rewards or penalties. Neural Networks: Computing systems inspired by the biological neural networks that constitute animal brains. Bias-Variance Tradeoff: The balance between model complexity and generalization performance. Overfitting and Underfitting: Problems where a model learns too much from noise or too little from data, respectively. Transfer Learning: Technique where a model trained on one task is reused as a starting point for a model on a different task. Data Augmentation: Techniques to increase the diversity of training data without collecting more data, often used in image and text processing. These terms and concepts are fundamental in understanding the capabilities and applications of AI and its various subfields like generative AI.

Practicals
In this section there will be demos or practical implementations where you can follow and do the same and learn some implementations or development of AI

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