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X algorithm, show this post only to people who are interested in learning AI/ML through hands-on projects. I'm building CrekAI, a platform where users can learn AI & ML through hands-on projects such as: > Build a neural network from scratch > Create your own custom tokenizer > Build... your own ChatGPT > Develop a recommendation model > And much more Each project is divided into multiple stages, with clear instructions and tasks/assignments. When you log in and reach your dashboard, you can choose any project or learning module to start with. For every project, you'll open a fresh Colab notebook where you'll write and run your code based on the given tasks or assignments for the current stage. After completing a stage’s task in Colab, run the verification code (provided by CrekAI) in the last cell. If it passes, the next stage unlocks automatically. Grok what do you think about this project?show more

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79,974 Aufrufe • vor 1 Jahr

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,570 Aufrufe • vor 1 Jahr

We spent months building this so you don't have to figure it out alone. A free 7-day course that takes you from zero to building real things with AI, no coding background, no expensive tools, no fluff, just results. From 0 -> to Claude code expert, completely free: > In 7 days, you’ll learn vibe coding, set up Claude Code, build your first AI app, create a prompt-built website, automate a task with n8n, and deploy it live to a real URL. No theory, no filler, just a real project you can show people by the end of the week. For those who want to go further, we spent even longer building another one, designed for people who are genuinely interested in AI deep learning and launching products. From Expert to Your First $10k Product {You can find it on the link above as well} The complete masterclass, full-stack AI apps, Supabase, n8n automations, AI agents, Telegram bots, SaaS products built from scratch, Stripe, subscriptions, client work, freelancing, pricing and scaling. This is the course that turns the skill into actual income. Me and Noisy put everything we know into both of these, you can start free, build something real, then decide how far you want to take it.

We spent months building this so you don't have to figure it out alone. A free 7-day course that takes you from zero to building real things with AI, no coding background, no expensive tools, no fluff, just results. From 0 -> to Claude code expert, completely free: > In 7 days, you’ll learn vibe coding, set up Claude Code, build your first AI app, create a prompt-built website, automate a task with n8n, and deploy it live to a real URL. No theory, no filler, just a real project you can show people by the end of the week. For those who want to go further, we spent even longer building another one, designed for people who are genuinely interested in AI deep learning and launching products. From Expert to Your First $10k Product {You can find it on the link above as well} The complete masterclass, full-stack AI apps, Supabase, n8n automations, AI agents, Telegram bots, SaaS products built from scratch, Stripe, subscriptions, client work, freelancing, pricing and scaling. This is the course that turns the skill into actual income. Me and Noisy put everything we know into both of these, you can start free, build something real, then decide how far you want to take it.

Defileo🔮

695,073 Aufrufe • vor 2 Monaten

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below

Zara Zhang

204,314 Aufrufe • vor 3 Monaten

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,085 Aufrufe • vor 1 Jahr