This course will teach you how to build AI-powered apps with the ChatGPT, Dall-E and GPT-4 APIs. Go here to try the interactive browser-version: https://scrimba.com/learn/buildaiapps

✏️ This course was created by Tom Chant, a teacher at Scrimba. If you have any feedback to Tom, please reach out to him on Twitter here:
https://twitter.com/tpchant

Also, follow Scrimba on YouTube here: https://www.youtube.com/c/Scrimba

We recommend that you learn basic HTML, CSS, and JavaScript before taking this course. Here are two free courses that will get you up to speed:
🔗 HTML & CSS: https://scrimba.com/learn/htmlandcss
🔗 JavaScript: https://scrimba.com/learn/learnjavascript

⭐️ Code ⭐️
🔗 Download via Scrimba: https://scrimba.com/learn/buildaiapps

💫 Links mentioned in course:
🔗 GPT-4 waiting list: https://scrimba.com/links/gpt4-waitlist-openai
🔗 OpenAI Home: https://scrimba.com/links/openai
🔗 OpenAI Docs: https://scrimba.com/links/openai-docs-intro
🔗 OpenAI Completions endpoint docs: https://scrimba.com/links/openai-completions-docs
🔗 GPTTools.com model comparison: https://scrimba.com/links/open-ai-comparison-tool
🔗 OpenAI Playground: https://scrimba.com/links/open-ai-playground
🔗 Dall-E: https://scrimba.com/links/dall-e-openai
🔗 OpenAI endpoint compatibility table: https://scrimba.com/links/chat....gpt-endpoint-compati
🔗 GPT-4 Chatbot conversation object format: https://scrimba.com/links/chatgpt-object-format
🔗 Data used to fine-tune We-Wingit Chatbot: https://scrimba.com/links/we-wingit-data-csv
🔗 Firebase home: https://scrimba.com/links/firebase-home
🔗 Firebase .val() method: https://scrimba.com/links/firebase-val-method
🔗 Object.values MDN: https://scrimba.com/links/object-dot-values
🔗 Netlify: https://scrimba.com/links/netlify-home

⭐️ Contents ⭐️
0:00:00 Introduction
0:01:19 Course Intro
0:04:56 MoviePitch intro
0:07:53 The Boilerplate
0:11:26 Getting an OpenAI API Key
0:13:32 Getting info for fetch request
0:15:14 Building an OpenAI fetch request
0:20:23 The first AI fetch request
0:26:41 Models
0:30:18 Tools
0:34:03 Refactor to use dependency l - env variable
0:38:11 Refactor to use dependency ll - The dependency
0:41:07 Refactor to use dependency lll - update fetchReply
0:44:40 Take out of Scrimba
0:46:45 Personalising the message
0:53:04 Tokens
0:57:09 fetchSynopsis
1:03:44 Aside - few shot approach
1:10:45 Aside - few shot approach ll
1:13:42 Refactor fetchSynopsis
1:21:00 Architecture
1:23:23 Title and Temperature
1:31:52 Reaching for the stars
1:37:52 Aside - createImage
1:46:56 fetchImagePrompt
1:54:21 Displaying the image and finishing off the UX
2:03:16 OutroKnowItAll: GPT-4 Chatbox2:06:47 KnowItAll Intro
2:09:40 Starter Code
2:13:10 Aside: How ChatGPT models work for chatbots
2:18:24 Conversation and instructions
2:20:21 Add user input to conversation array
2:23:06 The createChatCompletion endpoint
2:24:38 The model and object
2:28:46 Render the output, update the array
2:33:37 Aside: Theory: Frequency and presence penalties
2:37:07 presence_penalty practice
2:38:36 frequency_penalty practice
2:44:54 The chatbot’s personality
2:47:06 Firebase Intro
2:48:27 Firebase Account and database set up
2:50:43 Firebase dependency and database set up
2:55:53 Push method and instructions object
2:58:33 Update fetch Reply
3:02:24 Update fetchReply 2
3:04:49 Update the database
3:07:19 Render the conversation from the DB
3:12:02 The “start over” button
3:15:20 OutroWe-Wingit: Fine-tuned chatbot3:17:28 Intro to fine-tuning
3:20:04 Convert the Chatbot to We-Wingit
3:22:15 An Overview of the AI
3:23:52 Data for fine-tuning
3:26:34 The data we’re using
3:30:05 CLI 1 - Setting up the environment
3:33:03 CLI 2 - Data Preparation Tool
3:37:03 CLI 3 - Tuning the model
3:38:55 Updating the JS 1
3:41:33 Updating the JS 2
3:44:15 Updating the JS 3
3:47:01 The Separator
3:52:32 Aside - Stop Sequence
3:55:50 Adding the stop sequence
4:00:36 n_epochs
4:07:24 Intro to deployment
4:09:46 Download and GitHub
4:12:07 Netlify sign-up
4:13:56 Add Netlify env var
4:15:54 Netlify CLI
4:17:30 Netlify serverless function 1
4:19:52 Update fetchReply
4:24:28 Serverless function 2
4:27:30 Serverless function 3
4:29:21 Serverless function 4
4:32:32 Outro

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
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👾 Agustín Kussrow
👾 Nattira Maneerat
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👾 Justin Hual
👾 Otis Morgan

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Code : https://github.com/virendrapat....el62/Chat-gpt-ai-too
Courses : https://courses.feelfreetocode.com

we'll walk through the process of using the OpenAI Chat API to build a chatbot or conversational AI application. We'll cover everything you need to know, including how to sign up for an API key, how to send and receive messages, and how to customize your chatbot's behavior. By the end of this video, you'll have a solid understanding of how to use the OpenAI Chat API to build your own chatbot projects.

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ChatGPT can now see, hear, and speak! In this new update, ChatGPT gains the ability to interact with the world in a more multimodal way. This means that ChatGPT can now process and understand images and videos, and can also generate speech.

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

🔵 About Edureka ChatGPT Certification Training Course

Edureka’s ChatGPT Certification Training Course will teach you about ChatGPT architecture, GPT models, methodology, and real-world applications. The ChatGPT certification training program is an excellent choice for individuals and organizations seeking to improve their language processing and AI skills and knowledge.

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15 Jaw-Dropping Consequences of the Singularity (AGI IS CLOSER THAN YOU THINK!)

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

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This video on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful.The video covers the following topics:
00:00:00 Introduction
00:00:59 Sci-Fi Movies That Might Become Reality
00:01:53 Artificial Intelligence as a Threat
00:03:10 What Is Artificial Intelligence?
00:04:40 Machine Learning
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Python has recently emerged as one of the fastest-growing programming languages. Not only is it extensively used, but it is also an excellent language to learn if you want to go into programming. On the other hand, Machine Learning trains computers by analysing data and statistics. It represents a step toward artificial intelligence (AI). A machine learning programme analyses data and learns to anticipate outcomes.

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➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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This Edureka video on 'Selecting The Correct Predictive Modeling Technique' covers the various deciding factors to choose the correct predictive modeling technique. Following are the topics discussed:
00:00 - Introduction
00:51 - What is Predictive Analysis?
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How it Works?

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in the python programming language to enhance your learning experience.
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Why Learn Machine Learning using Python?

Data Science is a set of techniques that enables computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in the future, living the present
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Who should go for this Machine Learning Certification Training using Python?

Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
Developers aspiring to be a ‘Machine Learning Engineer'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'Python' professionals who want to design automatic predictive models
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