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Introducing Our Python Primer for Generative AI
Artificial Intelligence   Latest   Machine Learning

Introducing Our Python Primer for Generative AI

Towards AI Editorial Team
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March 3, 2025
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Last Updated on March 3, 2025 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

The First Python Course Designed for AI Development — from scratch!

This week, we are excited to announce our most requested course, Python Primer for Generative AI — designed to help you learn Python specifically for LLMs, how an AI engineer would.

We built this course with three guiding principles:

  • Teach Python skills for LLM development — not generic programming. By the end of this course, you’ll be able to build AI applications that make a real difference in your daily tasks and long-term goals.
  • Focus on building, not just theory — so you gain hands-on experience from day one and build your dream projects right away. You will work with libraries like Hugging Face and integrate open-source models into your projects, all while gradually building up the Python skills you need to take on AI development confidently.
  • Equip you with the ability to teach yourself — so you can keep up with the fast-moving AI landscape without needing endless courses. You’ll learn to code the modern way — iteratively, with AI assistance and leverage tools like GitHub Copilot and ChatGPT to write better Python code faster.

Unlike traditional Python courses, this one is built from the ground up for AI learners. It’s practical, interactive, and designed to help you think like an AI engineer — not just a coder.

What Makes This Course Different?

In Python Primer for Generative AI, you won’t just memorize code structures. Instead, you’ll:

  • Use LLMs as Coding Assistants

Leverage tools like ChatGPT and GitHub Copilot to troubleshoot code, learn new libraries, and improve your workflow — so you can code faster and smarter.

  • Build Mini-Projects from Day One

Theory is kept to a minimum; the best way to learn is by doing. You’ll start building applications right away.

  • Develop a Problem-Solving Mindset

Rather than just learning syntax, you’ll learn how to think like an AI engineer — so you can adapt to new tools and frameworks as the LLM space evolves.

  • Engage with a 70,000+ Learner Community

You won’t be learning alone. Connect with peers, get mentorship, and earn a certificate upon completing your final project.

Who Is This Course For?

This course is designed for anyone who wants to work with AI but doesn’t yet have the Python foundation to start building.

  • Beginners with no coding experience — We break down Python into easy-to-follow, AI-specific lessons.
  • LLM enthusiasts who want to go beyond prompting — Learn how to build and fine-tune models instead of just interacting with them.
  • Professionals looking to future-proof their careers — Develop AI skills that are becoming essential across industries.

We’ve also heard from many learners who wanted to dive straight into our From Beginner to Advanced LLM Developer course, but felt they first needed a solid grounding in Python. If that sounds like you, the Python Primer is the perfect place to start. Already comfortable with Python? Then feel free to jump into our advanced LLM program!

The Complete AI Development Bundle

For those ready to go all-in on AI development, we’re offering an exclusive bundle deal: get the Python Primer, the Advanced LLM Developer course, and our e-book for a big discount — over $125 in savings. Think of it as the ultimate zero-to-hero track for building production-grade AI solutions.

The best time to start was yesterday. The second-best time is today. Get the bundle here!

Will You Suddenly Become a Top Developer? No. But Here’s What You Will Gain.

Let’s be honest — no course will turn you into a top AI developer overnight. Mastery takes time, practice, and experience. But that doesn’t mean you have to spend months learning syntax before doing anything useful. This course is designed to get you building LLMs for the industry**.** Instead of memorizing Python in isolation, you’ll apply everything immediately, so you can start thinking and working like an AI developer from day one.

By the end, you won’t just “know Python” — you’ll be able to use it for AI. You’ll write Python scripts confidently, leverage AI coding assistants like GitHub Copilot, integrate real-world libraries like Hugging Face Transformers, and build AI-powered mini-projects that reinforce your skills. Whether you want to automate tasks, prototype AI applications, or fine-tune models, you’ll have the foundation to do it.

Of course, becoming an advanced AI developer takes more than one course. But this isn’t just an intro — it’s a launchpad. We’ve structured it to give you the problem-solving mindset, hands-on experience, and real-world tools to keep learning efficiently. Once you complete this, you’ll be ready for the next step, whether that’s diving into our Advanced LLM Developer Course or applying your skills to real AI projects. Instead of waiting until you’ve mastered every concept, you’ll learn just enough, in the right order, to start creating immediately. That’s how real AI engineers learn — and that’s how you’ll learn too.

If you’re ready to stop overthinking and start building, join the Python Primer today.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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